ࡱ>  ^&'() m G V1bjbjَ $]<<<PPPPPd|PĽx,"""(+h3idi4ŷǷǷǷǷǷǷ$<0j<io\ +hiio"(3,oooiX"<(ŷPPiŷoor2 w 8< (LIɃPPmk MAR 5620 Managerial Statistics Fall 2004 (Module 1) Dr. Larry Winner University of Florida Introduction (Section 1.1) This course applies and extends methods from STA 2023 to business applications. We begin with a series of definitions and descriptions: Descriptive Statistics: Methods used to describe a set of measurements, typically either numerically and/or graphically. Inferential Statistics: Methods to use a sample of measurements to make statements regarding a larger set of measurements (or a state of nature). Population: Set of all items (often referred to as units) of interest to a researcher. This can be a large, fixed population (e.g. all undergraduate students registered at UF in Fall 2003). It can also be a conceptual population (e.g. All potential consumers of a product during the products shelf life). Parameter: A numerical descriptive measure, describing a population of measurements (e.g. The mean number of credit hours for all UF undergraduates in Fall 2003). Sample: Set of items (units) drawn from a population. Statistic: A numerical descriptive measure, describing a sample. Statistical Inference: Process of making a decision, estimate, and/or a prediction regarding a population from sample data. Confidence Levels refer to how often estimation procedures give correct statements when applied to different samples from the population. Significance levels refer to how often a decision rule will make incorrect conclusions when applied to different samples from the population. Data Collection (Section 1.8 and Supplement) Data Collection Methods Observational Studies Researchers obtain data by directly observing individual units. These can be classified as prospective, where units are sampled first, and observed over a period of time, or retrospective studies where individuals are sampled after the event of interest and asked about prior conditions. Experimental Studies Researchers obtain data by randomly assigning subjects to experimental conditions and observing some response measured on each subject. Experimental studies are by definition prospective. Surveys Researchers obtain data by directly soliciting information, often including demographic characteristics, attitudes, and opinions. Three common types are: personal interview, telephone interview, and self-administered questionnaire (usually completed by mail). Examples Example Studies of Negative Effects of Smoking A study was conducted at the Mayo Clinic in the 1910s, comparing patients diagnosed with lip cancer (cases) with patients in the hospital with other conditions (controls). Researchers obtained information on many demographic and behavioral variables retrospectively. They found that among the lip cancer cases, 339 out of 537 subjects had been pipe smokers (63%), while among the controls not suffering from lip cancer, 149 out of 500 subjects had been pipe smokers. Source: A.C. Broders (1920). SquamousCell Epithelioma of the Lip, JAMA, 74:656-664. Pipe Smoker?CasesControlsTotalYes339149488No198351549Total5375001037 A huge cohort study was conducted where almost 200,000 adult males between the ages of 50 and 70 were followed from early 1952 through October 31, 1953. The men were identified as smokers and nonsmokers at the beginning of the trial, and the outcome observed was whether the man died during the study period. This study is observational since the men were not assigned to groups (smokers/nonsmokers), but is prospective since the outcome was observed after the groups were identified. Of 107822 smokers, 3002 died during the study period (2.78%). Of 79944 nonsmokers, 1852 died during the study period (2.32%). While this may not appear to be a large difference, the nonsmokers tended to be older than smokers (many smokers had died before the study was conducted). When controlling for age, the difference is much larger. Source: E.C. Hammond and D. Horn (1954). The Relationship Between Human Smoking Habits and Death Rates, JAMA,155:1316-1328. GroupDeathNot DeathTotalSmokers3002104280107822Nonsmokers18527809279944Total4854182912187766 Example Clinical Trials of Viagra A clinical trial was conducted where men suffering from erectile dysfunction were randomly assigned to one of 4 treatments: placebo, 25 mg, 50 mg, or 100 mg of oral sildenafil (Viagra). One primary outcome measured was the answer to the question: During sexual intecourse, how often were you able to penetrate your partner? (Q.3). The dependent variable, which is technically ordinal, had levels ranging from 1(almost never or never) to 5 (almost always or always). Also measured was whether the subject had improved erections after 24 weeks of treatment. This is an example of a controlled experiment. Source: I. Goldstein, et al (1998). Oral Sildenafil in the Treatment of Erectile Dysfunction, New England Journal of Medicine, 338:1397-1404. Treatment# of subjectsMean Std Dev# improving erectionsPlacebo1992.22.85025 mg963.22.05450 mg1053.52.081100 mg1014.02.085 Plot of mean response versus dose:  Example Accounting/Finance Salary Survey Careerbank.com conducts annual salary surveys of professionals in many business areas. They report the following salary and demographic information based on data from 2575 accounting, finance, and banking professionals who replied to an e-mail survey. Source:  HYPERLINK http://www.careerbank.com www.careerbank.com Male: 52% Female: 48% Mean Salary (% of Gender) Highest Level of Education Men Women None $61,868 (5%) $35,533 (16%) Associates $46,978 (7%) $37,148 (14%) Bachelors $60,091 (59%) $46,989 (53%) Masters $78,977 (28%) $57,527 (17%) Doctorate $90,700 (2%) $116,750 (<1%) What can be said of the distributions of education levels? What can be said for salaries, controlling for education levels? What is another factor that isnt considered here? Sampling (Section 1.10) Goal: Make a statement or prediction regarding a larger population, based on elements of a smaller (observed and measured) sample. Estimate: A numerical descriptive measure based on a sample, used to make a prediction regarding a population parameter. Political polls are often reported in election cycles, where a sample of registered voters are obtained to estimate the proportion of all registered voters who favor a candidate or referendum. A sample of subjects are given a particular diet supplement, and their average weight change during treatment is used to predict the mean weight change that would be obtained had it been given to a larger population of subjects. Target Population: The population which a researcher wishes to make inferences concerning. Cholesterol reducing drugs were originally targeted at older males with high cholesterol. Later studies showed effects measured in other patient populations as well. This is an example of expanding a market. Many videogames are targeted at teenagers. Awareness levels of a product should be measured among this demographic, not the general population. Sampled Population: The population from which the sample was taken. Surveys taken in health clubs, upscale restaurants, and night clubs are limited in terms of their representation of general populations such as college students or young professionals. However, they may represent a target population for marketers. Surveys in the past have been based on magazine subscribers and telephone lists when these were higher status items (see Literary Digest story on Page 143). In the early days of the internet, internet based surveys were also potentially biased. Not as large of a concern now. Self-Selected Samples: Samples where individuals respond to a survey question via mailin reply, internet click, or toll phone call. Doomed to bias since only highly interested parties reply. Worse: Respondents may reply multiple times. Sampling Plans (Section 1.10) Simple Random Sample: Sample where all possible samples of size n from a population of N items has an equal opportunity of selected. There must exist a frame (listing of all elements in the population). Random numbers are assigned to each element, elements are sorted by the random number (smallest to largest), and the first n (of the sorted list of) items are sampled. This is the gold standard of sampling plans and should be used whenever possible. Stratified Random Sample: Sample where a population has been divided into group of mutually exclusive and exhaustive sets of elements (strata), and simple random samples are selected from each strata. This is useful when the strata are of different sizes of magnitude, and the researcher wishes the sampled population to resemble the target population with respect to strata sizes. Cluster Sample: Sample where a population has been broken down into clusters of individuals (typically, but not necessarily, geographically based). A random sample of clusters are selected, and each element within each cluster is observed. This is useful when it is very time consuming and cost prohibitive to travel around an area for personal surveys. Systematic Sample: Sample is taken by randomly selecting an element from the beginning of a listing of elements (frame). Then every kth element is selected. This is useful when a directory exists of elements (such as a campus phone directory), but no computer file of elements can be obtained. It is also useful when the elements are ordered (ranked) by the outcome of interest. Sampling and Nonsampling Errors (Section 1.12) Sampling Error: Refers to the fact that sample means and proportions vary from one sample to another. Our estimators will be unbiased, in the sense that the sampling errors tend to average out to 0 across samples. Our estimates will also be efficient in that the spread of the distribution of the errors is as small as possible for a given sample size. Nonsampling Errors: Refer to errors that are not due to sampling. Recording/acquisition error: Data that are entered incorrectly at the site of observation or at the point of data entry. Response error or bias: Tendency for certain subjects to be more or less likely to complete a survey or to answer truthfully. Selection Bias: Situation where some members of target population cannot be included in sample. (e.g. Literary Digest example or studies conducted in locations that some subjects do not enter). Types of Variables (Section 1.11 and Supplement) Measurement Types: We will classify variables as three types: nominal, ordinal, and interval. Nominal Variables are categorical with levels that have no inherent ordering. Assuming you have a car, its brand (make) would be nominal (e.g. Ford, Toyota, BMW). Also, we will treat binary variables as nominal (e.g. whether a subject given Olestra based potato chips displayed gastro-intestinal side effect). Ordinal Variables are categorical with levels that do have a distinct ordering, however, relative distances between adjacent levels may not be the same (e.g. Film reviewers may rate movies on a 5-star scale, College athletic teams and company sales forces may be ranked by some criteria). Interval Variables are numeric variables that preserve distances between levels (e.g. Company quarterly profits (or losses, stated as negative profits), time for an accountant to complete a tax form). Relationship Variable Types: Most often, statistical inference is focused on studying the relationship between (among) two (or more) variables. We will distinguish between dependent and independent variables. Dependent variables are outcomes (also referred to as responses or endpoints) that are hypothesized to be related to the level(s) of other input variable(s). Dependent variables are typically labeled as Y. Independent variables are inputs (also referred to as predictors or explanatory variables) that are hypothesized to cause or be associated with levels of the dependent variable. Independent variables are typically labeled as X when there is a single dependent variable. Graphical Descriptive Methods (Chapter 2) Single Variable (Univariate) Graphs: Interval Scale Outcomes: Histograms separate individual outcomes into bins of equal width (where extreme bins may represent all individuals below or above a certain level). The bins are typically labeled by their midpoints. The heights oh the bars over each bin may be either the frequency (number of individuals falling in that range) or the percent (fraction of all individuals falling in that range, multiplied by 100%). Histograms are typically vertical. Stem-and-Leaf Diagrams are simple depictions of a distribution of measurements, where the stems represent the first digit(s), and leaves represent last digits (or possibly decimals). The shape will look very much like a histogram turned on its side. Stem-and-leaf diagrams are typically horizontal. Nominal/Ordinal/Interval Scale Outcomes: Pie Charts count individual outcomes by level of the variable being measured (or range of levels for interval scale variables), and represent the distribution of the variable such that the area of the pie for each level (or range) are proportional to the fraction of all measurements. Bar Charts are similar to histograms, except that the bars do not need to physically touch. They are typically used to represent frequencies or percentages of nominal and ordinal outcomes Two Variable (Bivariate) Graphs: Scatter Diagrams are graphs where pairs of outcomes (X,Y) are plotted against one another. These are typically interval scale variables. These graphs are useful in determining whether the variables are associated (possibly in a positive or negative manner). The vertical axis is typically the dependent variable and the horizontal axis is the independent variable (one major exception are demand curves in economics). Sub-Type Barcharts represent frequencies of nominal/ordinal dependent variables, broken down by levels of a nominal/ordinal independent variable. Three-Dimensional Barcharts represent frequencies of outcomes where the two variables are placed on perpendicular axes, and the heights represent the counts of number of individual observations falling in each combination of categories. These are typically reserved for nominal/ordinal variables. Time Series Plots are graphs of a single (or more) variable versus time. The vertical axis represents the response, while the horizontal axis represents time (day, week, month, quarter, year, decade,). These plots are also called line charts. Data Maps are maps, where geographical units (mutually exclusive and exhaustive regions such as states, counties, provinces) are shaded to represent levels of a variable. Examples Example Time Lost to Congested Traffic The following EXCEL spreadsheet contains the mean time lost annually in congested traffic (hours, per person) for n=39 U.S. cities. Source: Texas Transportation Institute (5/7/2001).  A histogram of the times, using default numbers of bins and upper endpoints from EXCEL 97:  A stem-and-leaf diagram of the times: Stem & Leaf DisplayStemsLeaves1->482->012446993->01122444577784->1222222455665->0336 Example AAA Quality Ratings of Hotels & Motels in FL The following EXCEL 97 worksheet gives the AAA ratings (1-5 stars) and the frequency counts for Florida hotels. Source: AAA Tour Book, 1999 Edition.  A bar chart, representing the distribution of ratings:  A pie chart, representing the distribution of ratings:  Note that the large majority of hotels get ratings of 2 or 3. Example Production Costs of a Hosiery Mill The following EXCEL 97 worksheet gives (approximately) the quantity produced (Column 2) and total costs (Column 3) for n=48 months of production for a hosiery mill. Source: Joel Dean (1941), Statistical Cost Functions of a Hosiery Mill, Studies in Business Administration. Vol 14, #3. 146.7592.64242.1888.81341.8686.44443.2988.8542.1286.38641.7889.87741.4788.53842.2191.11941.0381.221039.8483.721139.1584.541239.285.661339.5285.871438.0585.231539.1687.751638.5992.621736.5491.561837.0384.121936.681.222037.5883.352136.4882.292238.2580.922337.2676.922438.5978.352540.8974.572637.6671.62738.7965.642838.7862.092936.761.663035.177.143133.7575.473234.2970.373332.2666.713430.9764.373528.256.093624.5850.253720.2543.653817.0938.013914.3531.44013.1129.45419.529.02429.7419.05439.3420.36447.5117.68458.3519.23466.2514.92475.4511.44483.7912.69A scatterplot of total costs (Y) versus quantity produced (X):  Note the positive association between total cost and quantity produced. Example Tobacco Use Among U.S. College Students The following EXCEL 97 worksheet gives frequencies of college students by race (White(not hispanic), Hispanic, Asian, and Black) and current tobacco use (Yes, No). Source: Rigotti, Lee, Wechsler (2000). U.S. College Students Use of Tobacco Products, JAMA 284:699-705. A cross-tabulation (AKA contingency table) classifying students by race and smoking status. The numbers in the table are the number of students falling in each category: SmokeRace YesNoWhite38076738Hispanic261757Asian257860Black125663 A sub-type bar chart depicting counts of smokers/nonsmokers by race:  There is some evidence that a higher fraction of white students than black students currently smoked at the time of the study (the relative height of the Yes bar to No bar is higher for Whites than Blacks. A 3-dimensional bar chart of smoking status by race:  Example NASDAQ Stock Index Over Time This data set is too large to include as an EXCEL worksheet. The following is a graph of the NASDAQ market index versus day of trading from the beginning of the NASDAQ stock exchange (02/05/71) until (03/08/02). Source:  This is appears to be an example of a financial bubble, where prices were driven up dramatically, only to fall drastically. Example U.S. Airline Yield 1950-1999 The following EXCEL 97 worksheet gives annual airline performance measure (Yield in cents per revenue mile in 1982 dollars) for U.S. airlines. Source: Air Transport Association. YearYield82195027.62195128.29195225.17195324.11195422.98195523.86195622.5195721.51195819.52195922.78196021.4196120.13196220.15196319.2196418.53196517.95196616.86196715.89196815.15196914.95197014.39197114.44197214.04197313.78197414.27197513.61197613.51197713.42197812.27197911.58198012.89198113.08198211.78198311.25198411.27198510.4619869.6219879.4419889.6919899.6819909.4219919.0319928.619938.7219948.219958.151996819977.8919987.7619997.48A time series plot (line chart) of airline yields versus year in constant (1982) dollars:  Example 1994 Per Capita Income for Florida Counties The following graph is a map of per capita income for Florida Counties in 1994: Not in textbook.  It can be seen that the counties with the highest per capita incomes tend to be in the southern portion of the state and counties with the lowest per capita incomes tend to be on the panhandle (northwest). Numerical Descriptive Measures (Chapter 3) Measures of Central Location (Sections 3.1,3.3) Arithmetic Mean: The sum of all measurements, divided by the number of measurements. Only appropriate for interval scale data. Population Mean (N items in population, with values X1,,XN):  Sample Mean (n items in sample with values x1,,xn):  Note that measures such as per capita income are means. To obtain it, the total income for a region is obtained and divided by the number of people in the region. The mean represents what each individual would receive if the total for that variable were evenly split by all individuals. Median: Middle observation among a set of data. Appropriate for interval of ordinal data. Computed in same manner for populations and samples. Sort data from smallest to largest. The median is the middle observation (n odd) or mean of middle two (n even). Measures of Variability (Sections 3.2,3.3) Variance: Measure of the average squared distance to the mean across a set of measurements. Population Variance (N items in population, with values x1,,xN):  Sample Variance (n items in sample, with values x1,,xn):  Standard Deviation: Positive square root of the variance. Is measured in the same units as the data. Population: s. Sample: s. Coefficient of Variation: Ratio of standard deviation to the mean, often reported as a percentage. Population:  EMBED Equation.3  Sample:  EMBED Equation.3  Measures of Linear Relationship (Sections 3.5, 12.1-2) Covariance: Measure of the extent that two variables vary together. Covariance can be positive or negative, depending on the direction of the relationship. There are no limits on range of covariance. Population Covariance (N pairs of items in population, with values (Xi,Yi))  Sample Covariance (n pairs of items in sample, with values (Xi,Yi))  Coefficient of Correlation: Measure of the extent that two variables vary together. Correlations can be positive or negative, depending on the direction of the relationship. Correlations are covariances divided by the product of standard deviations of the two variables, and can only take on values between 1 and 1. Higher correlations (in absolute value) are consistent with stronger linear relationships. Population Coefficient of Correlation: r = COV(X,Y) / (sx sY) -1 ( r ( 1 Sample Coefficient of Correlation: r = cov(x,y) / (sx sy) -1 ( r ( 1 Least Squares Estimation of a Linear Relation Between 2 Interval Variables Dependent Variable: Y is the random outcome being observed Independent Variable: X is a variable that is believed to be related to (causing) Y. Procedure: Plot the Y values on the vertical (up and down) axis versus their corresponding X values on the horizontal (left to right) axis. (This step isnt necessary, but is very useful in understanding the relationship). Fit the best line: v = b0 + b1x that minimizes the sum of squared deviations between the actual values and their predicted values based on their corresponding x levels: Slope: How much Y tends to change as X increases by 1 unit. Y-intercept: Where the line crosses the Y-axis (when X=0).  Examples Example Diluted Earnings Per Share The following table gives diluted earnings per share (EPS) for a sample of n=10 publicly traded firms for calendar year 2002. Sources: Corporate Annual Reports. Firm EPS (X) Rank (X -- X-bar) (X X-bar)2 Merck 3.14 9 3.14-2.115 = 1.025 (1.025)2 = 1.050625 MBNA 1.34 2 1.34-2.115 = -0.775 (-0.775)2 = 0.600625 Gentex 1.12 1 1.12-2.115 = -0.995 (-0.995)2 = 0.990025 General Dynamics 4.52 10 4.52-2.115 = 2.405 (2.405)2 = 5.784025 Wachovia 2.60 8 2.60-2.115 = 0.485 (0.485)2 = 0.235225 Pepsico 1.85 6 1.85-2.115 = -0.265 (-0.265)2 = 0.070225 Pfizer 1.46 4 1.46-2.115 = -0.655 (-0.655)2 = 0.429025 Aflac 1.55 5 1.55-2.115 = -0.565 (-0.565)2 = 0.319225 Johnson & Johnson 2.16 7 2.16-2.115 = 0.045 (0.045)2 = 0.002025 General Electric 1.41 3 1.41-2.115 = -0.705 (-0.705)2 = 0.497025 Sum 21.15 -- 0.000 9.978050 To obtain the simple (unweighted) mean for these firms EPS values, we obtain the total of the EPS values and divide by the number of firms:  To obtain the median for these firms EPS values, we first order them from smallest to largest, then take the average of the middle two values (fifth and sixth). See ranks in table: Median = (1.55+1.85)/2 = 3.40/2 = 1.70 To obtain the sample variance, we first obtain each firms deviation from mean, square it, sum these across firms and divide by n-1. To get sample standard deviation, we take positive square root of sample variance. See calculations in table:  Example Times Wasted in Traffic (Continuation of Example) The following EXCEL spreadsheet contains descriptive statistics of time lost annually in congested traffic (hours, per person) for n=39 U.S. cities. Source: Texas Transportation Institute (5/7/2001). Column1Mean35.89744Standard Error1.632495Median37Mode42Standard Deviation10.19493Sample Variance103.9366Kurtosis-0.50803Skewness-0.13792Range42Minimum14Maximum56Sum1400Count39 Note that the coefficient of variation is the standard deviation divided by the mean: CV = 10.19/35.90 = 0.28, which is 28% when stated as a percentage. Example Defense Expenditures and GNP The following table gives defense expenditures (Y, in billions of dollars) and gross national product (X, in billions of dollars) for n=6 Latin American nations in 1997. This is treated as a sample for computational purposes. Calculations are given in tabular form. Nation Y X (Y-Y-bar) (X-X-bar) Brazil 14.15 788.2 14.15-5.05 = 9.10 788.2-291.5 = 496.7 Mexico 4.29 389.8 4.29-5.05 = -0.76 389.8-291.5 = 98.3 Argentina 3.70 318.8 3.70-5.05 = -1.35 318.8-291.5 = 27.3 Colombia 3.46 92.5 3.46-5.05 = -1.59 92.5-291.5 = -199.0 Chile 2.86 74.1 2.86-5.05 = -2.19 74.1-291.5 = -217.4 Venezuela 1.86 85.5 1.86-5.05 = -3.19 85.5-291.5 = -206.0 Mean 5.05 291.5 --- ---- Sums of squares and cross-products:  Variances, standard deviations, Covariance, Correlation, and Regression Equation: Sy2 = 102.72/(6-1) = 20.54 Sy = 4.53 Sx2 = 386418.9/(6-1) = 77283.8 Sx = 278.0 cov(X,Y) = 5858.0/(6-1) = 1171.6 r = 1171.6/(278.0*4.53) = 0.93 b1 = 1171.6 / 77283.8 = 0.01516 b0 = 5.05  0.01516(291.5) = 0.63 v = 0.63 + 0.01516X Plot of data and least squares fitted equation.  Variances and Covariance. GNPDefenseGNP77283.77Defense1171.61320.54207 Correlation. GNPDefenseGNP1Defense0.9298611 Example Estimation of Cost Function For the hosiery mill data in a previous Example, we estimate the cost function by least squares. The y-intercept (b0) can be interpreted as fixed cost, and the slope (b1) represents the unit variable costs. Y is in $1000s and X is in 1000s of dozens of pairs of socks. CoefficientsIntercept3.128201X2.005476 v = 3.13 + 2.01X, thus fixed costs are estimated to be $3130 since units are $1000s, and unit variable costs are approximately $2.00 per dozen pairs (this data was from the 1940s).  Example - Computation of Corporate  Betas A widely used measure of a companys performance is their beta. This is a measure of the firms stock price volatility relative to the overall markets volatility. One common use of beta is in the capital asset pricing model (CAPM) in finance, but you will hear them quoted on many business news shows as well. It is computed as (Value Line): The beta factor is derived from a least squares regression analysis between weekly percent changes in the price of a stock and weekly percent changes in the price of all stocks in the survey over a period of five years. In the case of shorter price histories, a smaller period is used, but never less than two years. In this example, we will compute the stock beta over a 28-week period for Coca-Cola and Anheuser-Busch, using the S&P500 as the market for comparison. Note that this period is only about 10% of the period used by Value Line. Note: While there are 28 weeks of data, there are only n=27 weekly changes. The included Excel worksheet provides the dates, weekly closing prices, and weekly percent changes of: the S&P500, Coca-Cola, and Anheuser-Busch. The following summary calculations are also provided, with X representing the S&P500, YC representing Coca-Cola, and YA representing Anheuser-Busch. All calculations should be based on 4 decimal places.  Compute the stock betas (slopes of the least squares regression lines) for Coca-Cola (bc) and Anhueser-Busch (ba). a) bc = -0.1888 ba = 0.1467 b) bc = 1.2984 ba = 0.6800 c) bc = 1.4075 ba = 0.7204 d) bc = 0.3529 ba = 0.4269 Explain why you would be able to determine which plot represents Coca-Cola, and which represents Anhueser-Busch, even if I could figure out how to remove the axis labels.     Basic Probability (Chapter 4) Approaches to assigning probabilities to outcomes Classical Approach: Based on mathematical means of determining all outcomes of an experiment, and assigning probabilities based on counting rules. We will not pursue this approach any further here. Relative Frequency Appoach: Based on on long-run relative frequencies of what happens when an experiment is conducted repeatedly. Subjective Approach: Based on assessing degrees of belief on certain events occurring. In most financial settings, probabilities bust be based subjectively, since an experiment cannot be conducted repeatedly. General Concepts: Events: Distinct outcomes of an experiment, possibly made up of groups of simpler events. Events are often labelled by capital letters, such as A and B, often with subscripts. Probabilities: Numerical measures of the likelihood (frequency) of the various events. When a listing of all simple events is known, their probabilities are all non-negative and sum to 1. Intersection: The intersection of two events, A and B, is the event that both events occur. Contingency Tables: Crosstabulations of the number of units corresponding to outcomes of events A and B Marginal Probability: Probabilities corresponding to individual event outcomes. Joint Probability: Probabilities corresponding to combinations of event outcomes. Example Phase III Clinical Trial for Pravachol Among a population (for now) of adult males with high cholesterol, approximately half of the males were assigned to receive Pravachol (Bristol--Myers Squibb), and approximately half received a placebo. The outcome observed was whether or not the patient suffered from a cardiac event within five years of beginning treatment. The counts of patients falling into each combination of treatment and outcome are given below. Source: J. Shepherd, et al, (1995), Prevention of Coronary Heart Disease with Pravastatin in Men with Hypocholsterolemia, NEJM, 333:1301-1307. Cardiac EventTreatmentYes (B1)No (B2)TotalPravachol (A1)17431283302Placebo (A2)24830453293Total42261736595 The probability a patient received pravachol and suffered a cardiac event: P(A1 and B1) = 174 / 6595 = 0.0264 The probability a patient received pravachol and did not suffer a cardiac event: P(A1 and B2) = 3128 / 6595 = 0.4743 The probability a patient received placebo and suffered a cardiac event: P(A2 and B1) = 248 / 6595 = 0.0376 The probability a patient received pravachol and did not suffer a cardiac event: P(A1 and B2) = 3045 / 6595 = 0.4617 These represent joint probabilities of treatment and cardiac event status. Joint, Marginal, and Conditional Probability Marginal Probability: Probabilities obtained for events, by summing across joint probabilities given in the table of probabilities. For the Pravachol data: Probability a subject received Pravachol (A1): P(A1) = P(A1 and B1) + P(A1 and B2) = .0264+ .4743 = .5007 Probability a subject received Placebo (A2): P(A2) = P(A2 and B1) + P(A2 and B2) = .0376+ .4617 = .4993 Probability a subject suffered a cardiac event (B1): P(B1) = P(A1 and B1) + P(A2 and B1) = .0264+ .0376 = .0640 Probability a subject did not suffer a cardiac event (B2): P(B2) = P(A1 and B2) + P(A2 and B2) = .4743+ .4617 = .9360 Below is a table, representing the joint and marginal probabilities. Note that this is simply obtained by dividing each count in the previous table by 6595. Cardiac EventTreatmentYes (B1)No (B2)TotalPravachol (A1)0.02640.47430.5007Placebo (A2)0.03760.46170.4993Total0.06400.93601.0000 About half of the subjects received pravachol, the other half received placebo. Approximately 6.4% (0.0640) of the subjects suffered a cardiac event (1 in 16). Conditional Probability: The probability that one event occured, given another event has occurred. The probability that event A has occurred given that B has occurred is written as P(A | B) and is computed as the first of the following equations:  Among patients receiving Pravachol (A1), what is the probability that a patient suffered a cardiac event (B1)?   Note that this could also be obtained from the original table of cell counts by taking 174/3302. Among patients receiving Placebo (A2), what is the probability that a patient suffered a cardiac event (B1)?  Among subjects receiving Pravachol, 5.27% suffered a cardiac event, a reduction compared to the 7.53% among subjects receiving placebo. We will later treat this as a sample and make an inference concerning the effect of Pravachol. Independence: Two events A and B are independent if P(A|B) = P(A) or P(B|A) = P(B). Since P(B1|A1) = .0527 ( .0640 = P(B1), treatment and cardiac event outcome are not independent in this population of subjects. Bayes Theorem (Section 4.3) Sometimes we can easily obtain probabilities of the form P(A|B) and P(B) and wish to obtain P(B|A). This is very important in decision theory with respect to updating information. We start with a prior probability, P(B), we then observe an event A, and obtain P(A|B). Then, we update our probability of B in light of knowledge that A has occurred. First note: P(A|B) = P(A and B) / P(B) ==> P(A and B) = P(A|B) * P(B) Second note: If factor B can be broken down into k mutually exclusive and exhaustive events B1, ..., Bk, then: P(A) = P(A and B1) + ... + P(A and Bk) = P(A|B1)*P(B1) + ... + P(A|Bk)*P(Bk) Third note: Given we know A has occurred, then the probability Bi occured is:  Example Cholera and Londons Water Companies Epidemiologist John Snow conducted a massive survey during a cholera epidemic in London during 1853-1854. He found that water was being provided through the pipes of two companies: Southwark & Vauxhall (W1) and Lambeth (W2). Apparently, the Lambeth company was obtaining their water upstream in the Thames River from the London sewer outflow, while the S&V company got theirs near the sewer outflow. The following table gives the numbers (or counts) of people who died of cholera and who did not, seperately for the two firms. Source: W.H. Frost (1936). Snow on Cholera, London, Oxford University Press. Cholera Death Water CompanyYes (C)NoTotalS&V (W1)3702261211264913Lambeth (W2)407170956171363Total4109432167436276 What is the probability a randomly selected person received water from the Lambeth company? From the S&V company? What is the probability a randomly selected person died of cholera? Did not die of cholera? What proportion of the Lambeth consumers died of cholera? Among the S&V consumers? Is the incidence of cholera death independent of firm? What is the probability a person received water from S&V, given (s)he died of cholera? Example - Moral Hazard A manager cannot observe whether her salesperson works hard. She believes based on prior experience that the probability her salesperson works hard (H) is 0.30. She believes that if the salesperson works hard, the probability a sale (S) is made is 0.75. If the salesperson does not work hard, the probability the sale is made is 0.15. She wishes to obtain the probability the salesperson worked hard based on his/her sales performance. Step 1: What do we want to compute? What is the probability that the salesperson worked hard if the sale was made? Prob(Work Hard | Sale) = Prob(Work Hard & Sale) / Prob (Sale) If not made? Prob(Work Hard | No Sale) = Prob(Work Hard & No Sale) / Pr(No Sale) Step 2: What is given/implied? Prob(Works Hard)=P(H)=0.30 Prob(Sale | Works Hard) = P(S|H)=0.75 Prob(No Sale | Works Hard) = P(Not S | H) = 1-0.75 = 0.25 Prob(Not Work Hard)= P(Not H) = 1-P(H) = 1-0.30=0.70 Prob(Sale | Not Work Hard)=P(S|Not H)=0.15 Prob(No Sale | Not Work Hard) = P(Not S | Not H) = 1-0.15 = 0.85 Step 3: Compute probabilities in step 1 from information given in step 2: Prob(Works Hard & Sale) = P(H)*P(S|H) = 0.30(0.75) = 0.225 Prob(Not Work Hard & Sale) = P(Not H)*P(S|Not H) = 0.70(0.15) = 0.105 Prob(Sale) = Prob(Works Hard & Sale) + Prob(Not Work Hard & Sale) = 0.225+0.105=0.330 Prob(Work Hard | Sale) = Prob(Work Hard & Sale) / Prob (Sale) = 0.225/0.330 = 0.682 Prob(Works Hard &No Sale) = P(H)*P(Not S|H) = 0.30(0.25) = 0.075 Prob(Not Work Hard & No Sale) = P(Not H)*P(Not S|Not H) = 0.70(0.85) = 0.595 Prob(No Sale) = Prob(Works Hard & No Sale) + Prob(Not Work Hard & No Sale) = 0.075+0.595=0.670 Prob(Work Hard | No Sale) = Prob(Work Hard & No Sale) / Prob (No Sale) = 0.075/0.670 = 0.112 % Note the amount of updating of the probability the salesperson worked hard, depending on whether the sale was made. This is a simplified example of a theoretical area in information economics (See e.g. D.M. Kreps, A Course in Microeconomic Theory, Chapter 16). Example -- Adverse Selection (Job Market Signaling) Consider a simple model where there are two types of workers -- low quality and high quality. Employers are unable to determine the worker's quality type. The workers choose education levels to signal to employers their quality types. Workers can either obtain a college degree (high education level) or not obtain a college degree (low education level). The effort of obtaining a college degree is lower for high quality workers than for low quality workers. Employers pay higher wages to workers with higher education levels, since this is a (imperfect) signal for their quality types. Suppose you know that in the population of workers, half are low quality and half are high quality. Thus, prior to observing a potential employee's education level, the employer thinks the probability the worker will be high quality is 0.5. Among high quality workers, 80% will pursue a college degree (20% do not pursue a degree), and among low quality workers, 15% pursue a college degree (85% do not). You want to determine the probability that a potential employee is high quality given they have obtained a college degree. Given they have not obtained a college degree. Step 1: What do we want to compute? Prob(High Quality|College) = Prob(High Quality & College) / Prob(College) = ? Prob(High Quality|No College) = Prob(High Quality & No College) / Prob(No College) = ? Step 2: What is given? Prob(High Quality) = 0.50 Prob(College|High Quality) = 0.80 Prob(No College|High Quality) = 1-0.80 = 0.20 Prob(Low Quality) = 0.50 Prob(College | Low Quality) = 0.15 Prob(No College|Low Quality)=1-0.15=0.85 Step 3: Computing probabilities in step 1 based on information in step 2: Prob(High Quality and College) = 0.50(0.80) = 0.400 Prob(Low Quality and College) = 0.50(0.15) = 0.075 Prob(College) = 0.400 + 0.075 = 0.475 Prob(High Quality | College) = 0.400/0.475 = 0.842 Prob(High Quality and No College) = 0.50(0.20) = 0.100 Prob(Low Quality and No College) = 0.50(0.85) = 0.425 Prob(No College) = 0.100 + 0.425 = 0.525 Prob(High Quality | No College) = 0.100/0.525 = 0.190 This is a simplified example of a theoretical area in information economics (See e.g. D.M. Kreps, A Course in Microeconomic Theory, Chapter 17). Random Variables and Discrete Probability Distributions Chapter 5 Random Variable: Function or rule that assigns a number to each possible outcome of an experiment. Discrete Random Variable: Random variable that can take on only a finite or countably infinte set of outcomes. Continuous Random Variable: Random variable that can take on any value across a continuous range. These have an uncountable set of possible values. Probability Distribution: Table, graph, or formula, describing the set of values a random variable can take on as well as probability masses (for discrete random variables) or densities (for continuous random variables). Requirements for a Probability Distribution for a discrete random variable: 0 ( p(x) ( 1 for every possible outcome x sum of p(x) values across all possible outcomes is 1 Example AAA Florida Hotel Ratings In a previous Example, we observed the distribution of quality ratings among Florida hotels. By treating this as a population (it includes all hotels rated by AAA), we can set this up as a probability distribution. Source: AAA Tour Book, 1999 Ed. The following table gives the frequency and proportion of hotels by quality rating. The probability distribution is obtained by dividing the frequency counts by the total number of hotels rated (1423). The random variable X is the quality rating of a randomly selected hotel. Rating (x) # of hotels P(x) 1 108 108/1423 = .07590 2 519 519/1423 = .36472 3 744 744/1423 = .52284 4 47 47/1423 = .03303 5 5 5/1423 = .00351 Sum 1423 1.00000 The shape of the probability distribution is identical to the histogram in Example 2, with the vertical axis rescaled (all frequencies turned into probabilities by dividing by 1423). What is the probability a randomly selected hotel gets a quality rating of 4 or higher? What is the median rating? Describing the Population/Probability Distribution Population Mean (Expected Value):  Population Variance:  Population Standard Deviation:  AAA Rating Example (Note this variable is technically ordinal, so this is for demonstration purposes only): Rating (x) p(x) xp(x) x2p(x) 1 .07590 1(.07590)=0.07590 1(.07590)=0.07590 2 .36472 2(.36472)=0.72944 4(.36472)=1.45888 3 .52284 3(.52284)=1.56852 9(.52284)=4.70556 4 .03303 4(.03303)=0.13212 16(.03303)=0.52848 5 .00351 5(.00351)=0.01755 25(.00351)=0.08775 Sum 1.00000 2.52353 6.85657    Example - Adverse Selection (Akerlof's Market for Lemons) George Akerlof shared the Nobel Prize for Economics in 2002 for an extended version of this model. There are two used car types: peaches and lemons. Sellers know the car type, having been driving it for a period of time. Buyers are unaware of a car's quality. Buyers value peaches at $3000 and lemons at $2000. Sellers value peaches at $2500 and lemons at $1000. Note that if sellers had higher valuations, no cars would be sold. Suppose that 2/5 (40%) of the cars are peaches and the remaining 3/5 (60%) are lemons. What is the expected value to a buyer, if (s)he purchases a car at random? We will let X represent the value to the buyer, which takes on the values 3000 (for peaches) and 2000 (for lemons). Thus, buyers will not pay over $2400 for a used car, and since the value of peaches is $2500 to sellers, only lemons will be sold, and buyers will learn that, and pay only $2000. At what fraction of the cars being peaches, will both types of cars be sold? For a theoretical treatment of this problem, see e.g. D.M. Kreps, A Course in Microeconomic Theory, Chapter 17. Bivariate Distributions (Section 5.2) Often we are interested in the outcomes of 2 (or more) random variables. In the case of two random variables, we will label them X and Y. Suppose you have the opporunity to purchase shares of two firms. Your (subjective) joint probability distribution (p(x,y)) for the return on the two stocks is given below, where: p(x,y) = Prob(X=x and Y=y) (this is like an intersection of events in Chapter 6):  Stock B Return (Y)Stock A Return (X)0%10%-5%0.150.3515%0.350.15 For instance, the probability they both perform poorly (X=-5 and Y=0) is small (0.15). Also, the probaility that they both perform strongly (X=15 and Y=10) is small (0.15). Its more likely that one will perform strongly, while the other will perform weakly (X=15 and Y=0) or (X=-5 and Y=10), each outcome with probability 0.35. We can think of these firms as substitutes. Marginal Distributions Marginally, what is the probability distribution for stock A (this is called the marginal distribution)? For stock B? These are given in the following table, and are computed by summing the joint probabilities across the level of the other variable. Stock A Stock B x p(x)=p(x,0)+p(x,10) y p(y)=p(-5,y)+p(15,y) -5 .15+.35 = .50 0 .15+.35 = .50 15 .35+.15 = .50 10 .35+.15 = .50 Hence, we can compute the mean and variance for X and Y:  So, both stocks have the same expected return, but stock A is riskier, in the sense that its variance is much larger. Note that the standard deviations are the square roots of the variances: sX = 10.0 and sY = 5.0 How do X and Y "co-vary" together? Covariance  For these two firms, we find that the covariance is negative, since high values of X tend to be seen with low values of Y and vice versa. We compute the Covariance of their returns in the following table. (mX = mY = 5) x y p(x,y) xy x-mX y-mY xyp(x,y) (x-mX)( y-mY)p(x,y) -5 0 .15 0 -10 -5 0(.15)=0 (-10)(-5)(.15)=7.5 -5 10 .35 -50 -10 5 -50(.35)=-17.5 (-10)(5)(.35)=-17.5 15 0 .35 0 10 -5 0(.35)=0 (10)(-5)(.35)=-17.5 15 10 .15 150 10 5 150(.15)=22.5 (10)(5)(.15)=7.5 Sum 5.0 -20.0 COV(X,Y) = -20.0 = 5.0-(5.0)(5.0) The negative comes from the fact that when X tends to be large, Y tends to be small and vice versa, based on the joint probability distribution. Coefficient of Correlation For the stock data: COV(X,Y) = -20.0, sX = 10.0, sY = 5.0, r = -20/(10*5)=-20/50 = -0.40 Functions of Random Variables Probability Distribution of the Sum of Two Variables Suppose you purchase 1 unit of each stock. What is your expected return (in percent). You want the probability distribuion for the random variable X+Y. Consider the joint probability distribution of X and Y, and compute X+Y for each outcome. x y p(x,y) x+y (x+y)p(x,y) (x+y)2p(x,y) -5 0 .15 -5+0=-5 (-5)(.15)=-0.75 (25)(.15)=3.75 -5 10 .35 -5+10=5 (5)(.35)=1.75 (25)(.35)=8.75 15 0 .35 15+0=15 (15)(.35)=5.25 (225)(.35)=78.75 15 10 .15 15+10=25 (25)(.15)=3.75 (625)(.15)=93.75 Sum 1.00 --- 10.00 185.00  Thus, the mean, variance, and standard deviation of X+Y (the sum of the returns) are: 10.00, 85.00, and 9.22, respectively. Rules for the Mean and Variance of X+Y  For the stock return example, we have: E(X) = 5 E(Y) = 5 V(X) = 100 V(Y) = 25 COV(X,Y) = -20 which gives us: E(X+Y) = 5+5 = 10 V(X+Y) = 100+25+2(-20)=85 which is in agreement with what we computed by generating the probability distribution for X+Y by brute force above. Probability Distribution of a Linear Function of Two Variables Consider a portfolio of two stocks, with Returns (R1,R2), and fixed weights (w1,w2) Return of portfolio: Rp = w1R1 + w2R2 where w1+w2 = 1, w1(0, w2(0 Expected Return on portfolio: E(Rp) = w1E(R1) + w2E(R2) Variance of Return on Portfolio: V(Rp) = (w1)2V(R1) + (w2)2V(R2) + 2w1w2COV(R1,R2) Note that the rules for expected value and variance of linear functions does not depend on the weights summing to 1. For stock portfolio from two stocks given above, set R1 = X and R2 = Y: Rp = w1R1 + w2R2 = w1R1 + (1-w1)R2 Expected Return: E(Rp) = w1(5) + (1-w1)(5) = 5 Variance of Return: V(Rp) = (w1)2(100) + (1-w1)2(25) + 2w1(1-w1)(-20) Compute the variance if: i) w1=0.25 and w2=0.75 ii) w1=0.00 and w2=1.00 iii) w1=1.00 and w2=0.00 To minimize the variance of returns, we expand the equation above in terms of w1, take its derivative with respect to w1, set it equal to 0, and solve for w1: V(Rp) = 165(w1)2 90w1 + 25 ii) dV(Rp)/dw1 = 2(165)w1 90 = 0 iii) 330w1 = 90 ==> w1* = 90/330 = 0.2727 No matter what portfolio we choose, expected returns are 5.0, however we can minimize the variance of the return (risk) by buying 0.27 parts of Stock A and (1-0.27)=0.73 parts of stock B. A classic paper on this topic (more mathematically rigorous than this example, where each stock has only two possible outcomes) is given in: Harry M. Markowitz, ``Portfolio Selection,'' Journal of Finance, 7 (March 1952), pp 77-91. Decision Making (Sections 16.1-2) Often times managers must make long-term decisions without knowing what future events will occur that will effect the firm's financial outcome from their decisions. Decision analysis is a means for managers to consider their choices and help them select an optimal strategy. For instance: Financial officers must decide among certain investment strategies without knowing the state of the economy over the investment horizon. A buyer must choose a model type for the firm's fleet of cars, without knowing what gas prices will be in the future. A drug company must decide whether to aggressively develop a new drug without knowing whether the drug will be effective the patient population. The decision analysis in its simplest form include the following components: Decision Alternatives (acts) - These are the actions that the decision maker has to choose from. States of Nature - These are occurrences that are out of the control of the decision maker, and that occur after the decision has been made. Payoffs - Benefits (or losses) that occur when a particular decision alternative has been selected and a given state of nature has observed. Payoff Table - A tabular listing of payoffs for all combinations of decision alternatives and states of nature. Case 1: Decision Making Under Certainty In the extremely unlikely case that the manager knows which state of nature will occur, the manager will simply choose the decision alternative with the highest payoff conditional on that state of nature. Of course, this is a very unlikely situation unless you have a very accurate psychic on the company payroll. Case 2: Decision Making Under Uncertainty When the decision maker does not know which state will occur, or even what probabilities to assign to the states of nature, several options occur. The two simplest criteria are: Maximax - Look at the maximum payoff for each decision alternative. Choose the alternative with the highest maximum payoff. This is an optimistic strategy. Maximin - Look at the minimum payoff for each decision alternative. Choose the alternative with the highest minimum payoff. This is a pessimistic strategy. Case 3: Decision Making Under Risk In this case, the decision maker does not know which state will occur, but does have probabilities to assign to the states. Payoff tables can be written in the form of decision trees. Note that in diagarams below, squares refer to decision alternatives and circles refer to states of nature. Expected Monetary Value (EMV): This is the expected payoff for a given decision alternative. We take each payoff times the probability of that state occuring, and sum it across states. There will be one EMV per decision alternative. One criteria commonly used is to select the alternative with the highest EMV. Return-to-Risk Ratio: EMV of an act, divided by its standard deviation Expected Value of Perfect Information (EVPI): This is a measure of how valuable it would be to know what state will occur. First we obtain the expected payoff with perfect information by multiplying the probability of each state of nature and its highest payoff, then summing over states of nature. Then we subtract off the highest EMV to obtain EVPI. Example Fashion Designers Business Decision A fashion designer has to decide which of three fashion lines to introduce (lines A, B, C) for the upcoming season. The designer believes there are three possibilities on how the economy will be perform (Positive, Neutral, Negative). Her beliefs about profits (payoffs) under each scenario are given in the following table. Her firm only has enough resources and staff to produce a single line. Economy Performance Positive Neutral Negative A 600 100 -400 Line B 100 300 100 C 500 400 -100 Give the decision alternatives (acts) Her firm can produce either Line A, B, or C. These are her choices. Give the states of nature Nature can produce either a positive, neutral, or negative economy. She has no control over this. If the designer is certain that the economy performance will be neutral, which line should he introduce for the season? Why? Under a neutral economy, Line A makes 100, Line B makes 300, and Line C makes 400. Clearly, she would choose Line C. When the designer has no idea what the economy performance will be, she wants to maximize the minimum profits he will make. That is, she is pessimistic regarding nature. Which strategy will he choose? Why? If she is pessimistic, she considers the worst case scenario (minimum) under each state of nature, and chooses the alternative with the highest minimum value (maximin). Minimums: Line A: -400 Line B: 100 Line C: -100 Choose B The designer consults her financial guru, and he tells her that the probability that the economy performance will be positive is 0.6, probability of neutral is 0.3, and probability of negative is 0.1. Give the expected monetary value (EMV) of each strategy: Line A: EMV(A) = 0.6(600) + 0.3(100) + 0.1(-400) = 360+30-40=350 Line B: EMV(B) = 0.6(100) + 0.3(300) + 0.1(100) = 60+90+10=160 Line C: EMV(C) = 0.6(500) + 0.3(400) + 0.1(-100) = 300+120-10=410 Return-to Risk Ratios: The standard deviations and R-T-R ratios are:  EMBED Equation.3  Based on the probabilities in the previous problem, how much would you be willing to pay for Perfect information regarding the economys state (that is, give EVPI). Under Positive economy, you select A, making 600 with probability 0.6 Under Neutral economy, you select C, making 400 with probability 0.3 Under Negative economy, you select B, making 100 with probability 0.1 E(Payoff Given perfect information) = 600(0.6)+400(0.3)+100(0.1)=360+120+10=490 EVPI = 490 410 = 80. You would be willing to pay up to 80 for this information Example - Merck's Decision to Build New Factory Around 1993, Merck had to decide whether to build a new plant to manufacture the AIDS drug Crixivan. The drug had not been tested at the time in clinical trials. The plant would be very specialized as the process to synthesize the drug was quite different from the process to produce other drugs. Consider the following facts that were known at the time (I obtained most numbers through newspaper reports, and company balance sheets, all numbers are approximate): Projected revenues - $500M/Year Merck profit margin - 25% Prior Probability that drug will prove effective and obtain FDA approval - 0.10 Cost of building new plants - $300M Sunk costs - $400M (Money spent in development prior to this decision) Length of time until new generation of drugs - 8 years Ignoring tremendous social pressure, does Merck build the factory now, or wait two years and observe the results of clinical trials (thus, forfeiting market share to Hoffman Laroche and Abbott, who are in fierce competition with Merck). Assume for this problem that if Merck builds now, and the drug gets approved, they will make $125M/Year (present value) for eight years (Note 125=500(0.25)). If they wait, and the drug gets approved, they will generate $62.5M/Year (present value) for six years. This is a by product of losing market share to competitors and 2 years of production. Due to the specificity of the production process, the cost of the plant will be a total loss if the drug does not obtain FDA approval. a) What are Merck's decision alternatives? b) What are the states of nature? c) Give the payoff table and decision tree. d) Give the Expected Monetary Value (EMV) for each decision. Ignoring social pressure, should Merck go ahead and build the plant? e) At what probability of the drug being successful, is Merck indifferent to building early or waiting. That is, for what value are the EMV's equal for the decision alternatives? Note: Merck did build the plant early, and the drug did receive FDA approval. Binomial Distribution (Section 5.3, EH5.2) Binomial Experiment: Experiment Consists of n trials Each trial has 2 possible outcomes (Success,Failure) Probability of a Success is p for each trial Outcomes of trials are independent of one another Random variable, X, is the number of successes in the n trials and can take on values: 0,1,,n Binomial distribution is indexed by 2 parameters: n and p Notation: X ~ Bin(n,p)  EMBED Equation.3  These probabilities are tabulated in Table E.6 of text Mean, Variance, and standard deviation of X:  EMBED Equation.3  Example: An inexpensive diagnostic test is given to a random sample of n=10 rural children who are known from an invasive procedure to have a disease. The test is known to have a sensitivity (the percentage patients with the disease who test positive on the diagnostic test) of 90% (p=0.90). What is the probability that 8 test positive?  EMBED Equation.3  Note that this is the same as P(X=2|X~Bin(10,.10)) in Table E.6. What is the probability at least 8 test positive?  EMBED Equation.3  What is the mean and standard deviation of X?  EMBED Equation.3  Probaility Distribution: xP(X=x)01E-1019E-0923.64E-0738.75E-0640.00013850.00148860.0111670.05739680.1937190.38742100.348678  Poisson Distribution (Section 5.5, EH5.4) Poisson process: Events occur at random across an area (time, length, surface area) Probability of event occuring in a given area is the same for all areas of equal size The number of events occuring in one area is independent of numbers of occurences in other areas As the size of the area gets smaller, the probability of 2 or more occurences in that area gets smaller Mean number of occurrence in area of unit length is parameter l The random number of occurences in a unit length is random variable X Notation: X~Poi(l)  EMBED Equation.3  These probabilities are tabulated in Table E.7 of text Mean, Variance, and standard deviation of X:  EMBED Equation.3  Example: Calls arrive at a computer help center according to a Poisson distribution with mean of 5 per hour. What is the probability that there will be at least one call in any given hour?  EMBED Equation.3  Probability Distribution xP(X=x)00.00673810.0336920.08422430.14037440.17546750.17546760.14622370.10444580.06527890.036266100.018133110.008242120.003434130.001321140.000472>140.000226  Continuous Probability Distributions Chapter 6, Supplement Normal Distributions (Section 6.1, Table E.2) The normal distribution is a family of symmetric distributions that are indexed by two parameters, the mean (m) and the variance (s2 ) (or by the standard deviation, s). The mean represents the center of the distribution, while the variance (and standard deviation) measure the dispersion or the spread of the distribution. While there are infinitely many normal distributions, they all share the following properties. Let X be a random variable that is normally distributed: P(X ( () = P(X ( () = 0.5 P((-k( ( X ( (+k() is the same for all distributions for any positive constant k P(X ( (+k() is given in the standard normal (Z) table on page E.2 for k in the range of 3.99 to 3.99. The distribution is symmetric, and has total area under the curve of 1.0 Approximately 68% of measurements lie within 1 standard deviation of the mean Approximately 95% of measurements lie within 2 standard deviations of the mean To obtain probabilities: Convert the endpoint(s) of the region of interest (say X0) into a z-score by subtracting off the mean and dividing by the standard deviation. This measures the number of standard deviations X0 falls above (if positive) or below (if negative) the mean: Z0 = (X0-m)/s Find Z0 on the outside border of Table E.2. The value in the body of the table is equivalently: P(Z ( Z0) = 1-P(Z ( Z0) = P(X ( (+Z0() = 1-P(X ( (+Z0s) 3) P(ZLo ( Z ( ZHi) = P(Z ( ZHi) - P(Z ( ZLo) Example  GRE Scores 1992-1995 Scores on the Verbal Ability section of the Graduate Record Examination (GRE) between 10/01/92 and 9/30/95 had a mean of 479 and a standard deviation of 116, based on a population of N=1188386 examinations. Scores can range between 200 and 800. Scores on standardized tests tend to be approximately normally distributed. Let X be a score randomly selected from this population. Useful shorthand notation is to write:, X ~ N(m=479,s=116). What is the probability that a randomly selected student scores at least 700? P(X (700) = P(Z ( (700-479)/116 = 1.91) = 1-P(Z ( 1.91) = 1-.9719 = .0281 What is the probability the student scores between 400 and 600? P(400 ( X ( 600) =? ZLo = (400-479)/116 = -0.68 ZHi = (600-479)/116 = 1.04 P(400 ( X ( 600) = P(Z ( 1.04) - P(Z ( -0.68) = .8508 - .2482 = .6026 Above what score do the top 5% of all students score above? Step 1: Find the z-value that leaves a probability of 0.05 in the upper tail (and a probability of 0.9500 below it). P(Z ( 1.645)=0.9500. That is, only the top 5% of students score more than 1.645 standard deviations above the mean. Step 2: Convert back to original units: 1.645 standard deviations is 1.645( = 1.645(116) = 191, and add back to the mean: (+1.645( = 479+191 = 670. The top 5% of students scored above 670 (assuming scores are approximately normally distributed). Source: Interpreting Your GRE General Test and Subject Test Scores -- 1996-97, Educational Testing Service. Normal probability density function: (Notation: X~N(m,s))  EMBED Equation.3   Example - Normal Distribution -- Galton s Measurements The renowned anthropologist Sir Francis Galton studied measurements of many variables occurring in nature. Among the measurements he obtained in the Anthropologic Laboratory in the International Exhibition of 1884 among adults are (where mM and sM represent the mean and standard deviation for males and mF and sF represent the mean and standard deviation for females: Standing height (inches) --- mM=67.9 sM=2.8 mF=63.3 sF=2.6 Sitting height (inches) --- mM=36.0 sM=1.4 mF=33.9 sF=1.3 Arm span (inches) --- mM=69.9 sM=3.0 mF=63.0 sF=2.9 Weight (pounds) --- mM=143 sM=15.5 mF=123 sF=14.3 Breathing Capacity (in3) --- mM=219 sM=39.2 mF=138 sF=28.6 Pull Strength (pounds) --- mM=74 sM=12.2 mF=40 sF=7.3 These were based on enormous samples and Galton found that their relative frequency distributions were well approximated by the normal distribution (that is, they were symmetric and mound-shaped). Even though these are sample means and standard deviations, they are based on almost 1000 cases, so we will treat them as population parameters. What proportion of males stood over 6 feet (72 inches) in Galtons time? What proportion of females stood under 5 feet (60 inches)? Sketch the approximate distributions of sitting heights among males and females on the same plot. Above what weight do the heaviest 10% of males fall? Below what weight do the lightest 5% of females fall? Between what bounds do the middle 95% of male breathing capacities lie? What fraction of women have pull strengths that exceed the pull strength that 99% of all men exceed? Where would you fall in the distributions of these men/women from a century ago? Source: Galton, F. (1889), Natural Inheritance, London: MacMillan and Co. Other Continuous Distributions Used for Statistical Inference t-distribution: A symmetric, mound shaped distribution, indexed by a parameter called degrees of freedom, closely related to the standard normal distribution. Critical values for certain upper tail areas (.25, .10, .05, .025, .010, .005) are given for a wide range of degrees of freedom in Table E.3. Distribution is symmetric around 0, and unbounded. Lower tail critical values are obtained by symmetry of the distribution. Chi-Square distribution (c2): A skewed right distribution, indexed by a parameter called degrees of freedom, related to squares of standard normal random variables. Critical values for certain upper tail areas (.995, .990, .975, .950, .900, .750, .250,.100, .050, .025, .010, .005) are given for a wide range of degrees of freedom in Table E.4. Distribution is skewed right and only defined for postive values. F-disribution: A skewed right distribution, indexed by 2 parameters, called numerator and denominator degrees of freedom, related to ratios of chi-square random variables. Critical values for upper tail areas are given for upper tail areas (.050, .025, .010, .005) are given for wide ranges of degrees of freedom in Table E.5. Distribution is skewed right and only defined for positive values. To get lower tail critical values are obtained by reversing numerator and denominator and taking reciprocal of table value. Examples will be given when we get to inference problems using these distributions. Sampling Distributions (Sections 6.5-6.7) Sampling Distributions of estimators: Probability distributions for estimators that are based on random samples. Sample means and sample proportions vary from one sample to another. Their sampling distributions refer to how these quantities vary from one sample to another. Interval Scale Outcomes: If a population of interval scale outcomes has mean m and variance s2, then the sampling distribution of sample mean  EMBED Equation.3 , obtained from random samples of size n has the following mean, variance, and standard deviation (note that standard deviations of estimators are referred to as standard errors): Section 6.6.  If the distribution of individual measurements is normal, the sampling distribution is normal, regardless of sample size. If the distribution of indivinual measurements is nonnormal, the sampling distribution is approximately normal for large sample sizes. This is a result of the so-called Central Limit Theorem. When independent random samples of sizes n1 and n2 are sampled from two populations with means m1 and m2, and variances s12 and s22, respectively, the samplling distribution for the difference between the two sample means,  EMBED Equation.3 , has the following mean, variance, and standard deviation and the same rules regarding normality (Section 9.1):  Standard deviations of sampling distributions for estimators are referred to as STANDARD ERRORS. When population variances (s2, s12, s22) are unknown, we replace them with their sample variances (s2, s12, s22), and refer to the resulting standard errors as ESTIMATED STANDARD ERRORS. Nominal outcomes: If among a population of elements, the proportion that has some characteristic is p, then if elements are taken at random in samples of size n, the sample proportion of elements having the characteristic, ps, has a sampling distribution with the following mean, variance, and standard deviation (standard error): Section 6.7.  For large samples, the sampling distribution is approximately normal. To obtain the ESTIMATED STANDARD ERROR, replace p with ps. When independent random samples of sizes n1 and n2 are sampled from two populations with proportions of elements having a characteristic of p1 and p2, respectively, the samplling distribution for the difference between the two sample sample proportions, pS1-pS2, has the following mean, variance, and standard deviation and the same rules regarding normality and estimated standard errors. Section 9.3:  Example: Sampling Distributions -- Galtons Measurements The renowned anthropologist Sir Francis Galton studied measurements of many variables occurring in nature. Among the measurements he obtained in the Anthropologic Laboratory in the International Exhibition of 1884 among adults are (where mM and sM represent the mean and standard deviation for males and mF and sF represent the mean and standard deviation for females: Standing height (inches) --- mM=67.9 sM=2.8 mF=63.3 sF=2.6 Sitting height (inches) --- mM=36.0 sM=1.4 mF=33.9 sF=1.3 Arm span (inches) --- mM=69.9 sM=3.0 mF=63.0 sF=2.9 Weight (pounds) --- mM=143 sM=15.5 mF=123 sF=14.3 Breathing Capacity (in3) --- mM=219 sM=39.2 mF=138 sF=28.6 Pull Strength (pounds) --- mM=74 sM=12.2 mF=40 sF=7.3 These were based on enormous samples and Galton found that their relative frequency distributions were well approximated by the normal distribution (that is, they were symmetric and mound-shaped). Even though these are sample means and standard deviations, they are based on almost 1000 cases, so we will treat them as population parameters. Source: Galton, F. (1889), Natural Inheritance, London: MacMillan and Co. Give the approximate sampling distribution for the sample mean  EMBED Equation.3 , for samples in each of the following cases: Standing heights of 25 randomly selected males Sitting heights of 35 randomly selected females Arm spans of 9 randomly selected males Weights of 50 randomly selected females The differences in heights between 10 females  EMBED Equation.3  and 10 males  EMBED Equation.3  The differences in heights between 3 females  EMBED Equation.3  and 3 males  EMBED Equation.3  Obtain the following probabilities: A sample of 25 males has a mean standing height exceeding 70 inches A sample of 35 females has a mean sitting height below 32 inches A sample of 9 males has an arm span between 69 and 71 inches A sample of 50 females has a mean weight above 125 pounds. A sample of 10 females has a higher mean height than a sample of 10 males. A sample of 3 females has a higher mean height than a sample of 3 males Example Imported Footwear in the United States The following table gives the U.S. consumption for footwear and the number of imports (both in units of 1000s of pairs) for the years 1993-2000, as well as the proportion of pairs consumed that were imports. Source: Shoe Stats 2001. American Apparel and Footwear Association (AAFA). Year Consumption Imports Proportion Imports (p) 1993 1,567,405 1,347,901 1,347,901/1,567,405 = .8600 1994 1,637,449 1,425,834 1,425,834/1,637,449 = .8708 1995 1,594,204 1,409,232 1,409,232/1,594,204 = .8840 1996 1,538,008 1,376,080 1,376,080/1,538,008 = .8947 1997 1,640,993 1,488,118 1,488,118/1,640,993 = .9068 1998 1,619,407 1,499,465 1,499,465/1,619,407 = .9259 1999 1,693,646 1,615,821 1,615,821/1,693,646 = .9540 2000 1,793,661 1,745,540 1,745,540/1,793,661 = .9732 What is the approximate sampling distribution for the proportion of imported pairs among random samples of n=500 pairs purchased in 1993?  The sampling distribution would be approximately normal with mean .8600 and standard error (deviation) of .0155. If we took a random sample of 500 pairs there would be a very good chance (95%) that the proportion of imports would be in the range: .8600 ( 2(.0155) = .8600 ( .0310 = (.8290 , .8910). What is the approximate sampling distribution for the proportion of imported pairs among random samples of n=500 pairs purchased in 2000? Would you expect that a sample proportion of imports of 500 pairs purchased in 1993 is higher than a sample proportion of imports of 500 pairs purchased in 2000? First, get the sampling distribution for the difference, then give a range that contains the difference in sample means with probability 0.95 (95%). Mean: .8600 - .9732 = -.1132 Standard Error:  Shape: Approximately Normal: Would expect difference to lie in the range: -.1132 (2(.0170) = (-.1472 , -.0792) ... < 0 Repeat parts 1-3 for samples of 1000 pairs. Confidence Interval Estimation of Mean (Sections 7.1-2) Case 1 (s Known): Section 7.1 Parameter: m (Unknown, fixed value) Estimator:  EMBED Equation.3  (observed from a random sample of population) Sampling Distribution:  EMBED Equation.3  From Z-table, we know:  EMBED Equation.3   (1-a)100% Confidence Interval for m:  EMBED Equation.3  90% Confidence: a/2=0.050 Z0.050 = 1.645 95% Confidence: a/2=0.025 Z0.025 = 1.960 99% Confidence: a/2=0.005 Z0.005 = 2.326  Case 2 (s Unknown): Section 7.2 Parameter: m (Unknown, fixed value) Estimator:  EMBED Equation.3  (observed from a random sample of population) Sampling Distribution:  EMBED Equation.3  (Student s t distribution with n-1 degrees of freedom, the number of independent observations in the sample estimate S of the parameter s) From t-table, we know:  EMBED Equation.3   (1-a)100% Confidence Interval for m:  EMBED Equation.3  90% Confidence: a/2=0.050 95% Confidence: a/2=0.025 99% Confidence: a/2=0.005  Confidence Interval Estimation of Proportion (Sect. 7.3) Parameter: p (Unknown, fixed value) Estimator: ps (observed from a random sample of population) Sampling Distribution:  EMBED Equation.3   (1-a)100% Confidence Interval for p:  EMBED Equation.3  90% Confidence: a/2=0.050 Z0.050 = 1.645 95% Confidence: a/2=0.025 Z0.025 = 1.960 99% Confidence: a/2=0.005 Z0.005 = 2.326  Determining Sample Size (Section 7.4) Sampling Error - the difference between a sample quantity (mean or proportion) and the true parameter. Bound on error of estimation (with (1-a)100% confidence): Mean:  EMBED Equation.3  Proportion:  EMBED Equation.3  e is also referred to as the margin of error. Goal: Choose n so that you will have a specified margin of error (note that if we use the standard 95% confidence level, Z.025=1.96( 2 Mean:  EMBED Equation.3  Proportion:  EMBED Equation.3  Applications in Auditing (Section 7.5) Estimating a Population Total Amount: N=Total number of sales vouchers/Records m=Mean value of all sales vouchers/Records n=Number of sales vouchers/Records in random sample  EMBED Equation.3 = Mean value of sales vouchers/records in sample (1-a)100% CI for Population Total (Nm):  EMBED Equation.3  Estimating total (not mean) Finite population correction factor Estimating a Population Mean Auditing Error For a random sample of n items being audited, compute: Di = audited value  original value for record i  EMBED Equation.3  (1-a)100% CI for Total Difference (Error):  EMBED Equation.3  Upper Bound Estimate for Noncompliance Rate ps = sample proportion of items shipped without authorization (1-a)100% Upper Confidence Limit for a Proportion:  EMBED Equation.3  Hypothesis Testing (Chapter 8) Methods to make inference regarding unknown parameters. Often referred to as decision making under uncertainty. Null Hypothesis (H0) Represents a current state of nature or that two groups of subjects are the same (e.g. The proportion of defective items produced has not changed over time, or that the mean clinical response is the same for patients receiving an active drug and those receiving a placebo). Null hypotheses always contain an equal sign. Alternative Hypothesis (H1) Represents a contradiction to the null hypothesis. Typically is a research claim the experimenter wishes to demonstrate. Can be 1-sided (e.g. proportion defective has decreased since last year) or 2-sided (mean responses differ for those receiving active drug and those receiving placebo). Test Statistic Quantity based on sample data and the parameter value under the null hypothesis, that is used to test the research claim. Rejection Region Range of values of the test statistic for which we reject the null hypothesis in favor of the alternative hypothesis. P-value Measure of the strength of the evidence against the null hypothesis in favor of the alternative hypothesis. Technically, the probability that we would have obtained sample data as strong or stronger than our current sample in favor of the alternative hypothesis, had the null been true. Type I Error Rejecting the null hypothesis in favor of the alternative when the null is true. a=P(Type I Error) Type II Error  Failing to reject the null hypothesis when the alternative hypothesis is true. b=P(Type II error) Power  Probability that we reject the null hypothesis. When H0 is false, power=1-b. 2-Tailed Z-test for Mean (s Known) (Section 8.2) H0: m = m0 (m0 is a specified constant) H1: m `" m0 Test Statistic:  EMBED Equation.3  Decision Rule/Rejection Region: (a=P(Type I Error)) Conclude m > m0 if Zobs e" Za/2 Do not reject m = m0 if -Za/2 < Zobs < Za/2 Conclude m < m0 if Zobs d" -Za/2 P-value: 2P(Z e" |Zobs|) Note: If the P-value d" a, we reject the null hypothesis, otherwise we fail to reject the null hypothesis. 1-Tailed Z-test for Mean (s Known) (Section 8.3) To show that m is larger than m0: H0: m = m0 (m0 is a specified constant) H1: m > m0 Test Statistic:  EMBED Equation.3  Decision Rule/Rejection Region: (a=P(Type I Error)) Conclude m > m0 if Zobs e" Za Do not reject m = m0 if Zobs < Za P-value: P(Z e" Zobs) To show that m is smaller than m0: H0: m = m0 (m0 is a specified constant) H1: m < m0 Test Statistic:  EMBED Equation.3  Decision Rule/Rejection Region: (a=P(Type I Error)) Conclude m < m0 if Zobs d" -Za Do not reject m = m0 if Zobs > -Za P-value: P(Z d" Zobs) 2-Tailed t-test for Mean (s Unknown) (Section 8.4) H0: m = m0 (m0 is a specified constant) H1: m `" m0 Test Statistic:  EMBED Equation.3  Decision Rule/Rejection Region: (a=P(Type I Error)) Conclude m > m0 if tobs e" ta/2,n-1 Do not reject m = m0 if -ta/2,n-1 < tobs < ta/2,n-1 Conclude m < m0 if tobs d" -ta/2,n-1 P-value: 2P(t e" |tobs|) This test is based on the assumption that the population of measurements is approximately normal when the sample size is small (say less than 30). No such assumtion needed for larger samples. 2-Tailed Z-test for Proportion (Section 8.5) H0: p = p0 (po is a specified constant) H1: p `" p0 Test Statistic:  EMBED Equation.3  Decision Rule/Rejection Region: (a=P(Type I Error)) Conclude m > m0 if Zobs e" Za/2 Do not reject m = m0 if -Za/2 < Zobs < Za/2 Conclude m < m0 if Zobs d" -Za/2 P-value: 2P(Z e" |Zobs|) Comparing 2 Populations  Chapter 9 We often wish to compare two groups with respect to either interval scale or nominal outcomes. Typical research questions may include: Does a new drug decrease blood pressure more than a currently prescribed medication? Are men or women more likely to like a certain product after exposure to a particular advertisement? Does a new fat substitute cause higher rates of an undesirable side effect than traditional additives? Do firms stock performances differ between two industries? We are generally interested in questions of the forms: Are the population mean scores the same for two groups, or are they different (or does one group have a higher mean than the other)? Are the population proportions with a certain characteristic the same for two groups, or are they different (or does one group have a higher proportion than the other)? In each case, we wish to make statements concerning 2 POPULATIONS, based on 2 SAMPLES. Comparing Two Population Means (Section 9.1) Hypothesis Testing Concerning m1-m2 Null Hypothesis (H0): Two populations have same mean responses (m1-m2 = 0) 2a. Alternative Hypothesis (HA): Means are not Equal (m1-m2 ( 0) 2b. Alternative Hypothesis (HA): Mean for group 1 is higher (m1-m2 > 0) Test Statistic:  EMBED Equation.3  Decision Rule (based on a=0.05 probability of a Type I error): Alternative 2a: Conclude that means differ if absolute value of the test statistic exceeds t.025,n (the critical value leaving 0.025 in the upper tail of the t-distribution with ndegrees of freedom). Alternative 2b: Conclude that the mean for group 1 is higher if the test statistic exceeds t.05,n (the critical value leaving 0.05 in the upper tail of the t-distribution with ndegrees of freedom). P-value: Measure of the extent that the data contradicts the null hypothesis. P-values below a (0.05) are contradictory to the null hypothesis. That is, if the there were no difference in the population means, we would find it unlikely that the sample means differ to this extent. We will rely on computer software to compute P-values, but will need to interpret them throughout the course. 95% Confidence Interval for m1-m2 Construct interval: Based on interval: If interval is entirely above 0, conclude m1>m2 (risking type I error) If interval is entirely below 0, conclude m170% rigidity). We define mH as the true mean duration on the high dose, and mL as the true mean duration on the low dose, and  EMBED Equation.3  and  EMBED Equation.3  as the sample means. The manufacturer wishes to demonstrate that its drug is effective, in the sense that at higher doses there will be higher effect. What are the appropriate null and alternative hypotheses?  The researchers reported the following information from a clinical trial (the data are duration of erection, in minutes): Dose Low High  A test of equal variances does find that they are unequal. The adjustment of degrees of freedom is from 57+58-2=113 to 84 (Page 395). Since df is large, well use z-distribution to approximate t-distribution critical values, but will use standard error based on unequal variances (although with approximately equal sample sizes, they are mathematically equivalent). 2) Compute the appropriate test statistic for testing the test described in 1).  The appropriate rejection region for this test (a = .05) is: RR: tobs > 1.96 RR: |tobs|> 1.96 RR: tobs > 1.645 *** RR: tobs < -1.645 Is your P-value larger/smaller than 0.05? Is it likely that you have made a Type II error in this problem? Yes/No In many situations, statistical tests of this form are criticized for detecting statistical, but not practical or clinical differences? You should have concluded that the drug is effective. By how much do the sample means differ? Does this difference seem real? Recall that duration is measured in minutes. Source: Linet, O.I. and F.G. Ogric (1996). Efficacy and Safety of Intracevernosal Alprostadil in Men With Erectile Dysfunction, New England Journal of Medicine, 334:873-877. Example: Salary Progression Gap Between Dual Earner and Traditional Male Managers A study compared the salary progressions from 1984 to 1989 among married male managers of Fortune 500 companies with children at home. For each manager, the salary progression was computed as: X=(1989 salary 1984 salary)/1984 salary The researchers were interested in determining if there are differences in mean salary progression between dual earner (group 1) and traditional (group 2) managers. The authors report the following sample statistics: If the authors wish to test for differences in mean salary progressions between dual earner and traditional male managers, what are the appropriate null and alternative hypotheses?  Compute the test statistic to be used for this hypothesis test (theres no need to pool the variances for this large of pair of samples).   What is the appropriate rejection region (based on a=0.05)?  What is your conclusion based on this test? Reject H0, do not conclude differences exist between the 2 groups Reject H0, conclude differences exist between the 2 groups Don t Reject H0, do not conclude differences exist between the 2 groups Dont Reject H0, do conclude differences exist between the 2 groups Based on this conclusion, we are at risk of (but arent necessarily) making a: Type I error Type II error Both a) and b) Neither a) or b) Source: Stroh, L.K. and J.M. Brett (1996), The Dual-Earner Dad Penalty in Salary Progression, Human Resource Management;35:181-201. Comparing Two Population Proportions (Section 9.3) Hypothesis Testing Concerning p1-p2 (Large Sample) Null Hypothesis (H0): Two populations have same proportions with a characterisic (p1-p2 = 0) 2a. Alternative Hypothesis (HA): Proportions are not Equal (p1-p2 ( 0) 2b. Alternative Hypothesis (HA): Mean for group 1 is higher (p1-p2 > 0) Test Statistic:  EMBED Equation.3  Decision Rule (based on a=0.05 probability of a Type I error): Alternative 2a: Conclude that means differ if absolute value of the test statistic exceeds z.025 = 1.96 (the critical value leaving 0.025 in the upper tail of the z-distribution). Alternative 2b: Conclude that the mean for group 1 is higher if the test statistic exceeds z.05 = 1.645 (the critical value leaving 0.05 in the upper tail of the z-distribution). P-value: Measure of the extent that the data contradicts the null hypothesis. P-values below a (0.05) are contradictory to the null hypothesis. That is, if the there were no difference in the population means, we would find it unlikely that the sample means differ to this extent. We will rely on computer software to compute P-values, but will need to interpret them throughout the course. 95% Confidence Interval for p1-p2 Construct interval: Based on interval: If interval is entirely above 0, conclude p1>p2 (risking type I error) If interval is entirely below 0, conclude p1 1.645 p-value=.0336 b) RR: Zobs > 1.96 p-value=.0336 c) RR: Zobs > 1.96 p-value=.0672 d) RR: Zobs > 1.645 p-value=.0672 Can we conclude (based on this level of significance) that the true population proportions differ by size of firm? Yes No None of the above All of the above In the same article, they reported that 19 of the large firms and 70 of the small firms commonly used one-on-one interviews. Compute a 95% confidence interval for the difference in sample proportions between large and small firms that commonly use one-on-one interviews (pL-pS).  Based on your confidence interval from 6), can we conclude (based on a=0.05) that the population proportions of firms that commonly conduct one-on-one interviews differ among large and small firms? Source: Deshpande, S.P. and D.Y. Golhar (1994),  HRM Practices in Large and Small Manufacturing Firms: A Comparative Study, Journal of Small Business Management, 4956. Comparing More Than 2 Populations Independent Samples Frequently, we have more than two groups to compare. Methods that appear quite difference from the 2 sample t and z tests can be used to compare more than 2 populations of interval or nominal measurements, Keep in mind that we are still conducting tests very similar to those in the previous section. Suppose there are c populations or treatments to be compared, we wish to test hypotheses of the following forms: Interval Scale outcomes: Section 10.1 H0: m1 = m2 = ( = mc HA: The c means are not all equal Nominal Outcomes: (Section 11.1-2) HA: p1 = p2 = ( = pc HA: The c proportions are not all equal The test for interval scale outcomes is the F-test, based on the Analysis of Variance. The test for nominal outcomes is referred to as the Chi-square test for contingency tables. In each case, if we reject the null hypothesis and conclude that the means or proportions are not all equal, we will conduct post hoc comparisons to determine which pairs of populations or treatments differ. The F-test for interval scale outcomes is theoretically based on the assumption that all k populations are normally distributed with common variance s2 (similar to the 2-sample t-test). Departures from normality have been shown to be less of a problem than unequal variances. One-Way Analysis of Variance (Section 10.1) Populations: c Groups, with mean mj and variance sj2 for population j (j=1,...,c) Samples: c samples of size nj with mean  EMBED Equation.3  , variance Sj2, for sample j (j=1,...,c) Notation: Xij the ith element from group j (The j being the more important subscript) nj the sample size for group j  EMBED Equation.3  - the sample mean for group j :  EMBED Equation.3  Sj2 the sample variance for group j:  EMBED Equation.3  n overall sample size (across groups):  EMBED Equation.3   EMBED Equation.3  - overall sample mean  EMBED Equation.3  Total variation around overall mean (across all n observations):  This total variation can be partitioned into two sources: Among and Within treatments. Between Treatments: Sum of Squares for Treatments: (Page 422)  EMBED Equation.3  Within Treatments: Sum of Squares for Error: (Page 423)  EMBED Equation.3  EMBED Equation.3  The Sum of Squares for Treatments has n1=c-1 degrees of freedom, while the Sum of Squares for Error has n2 = n-c degrees of freedom. Mean Square for Treatments:  EMBED Equation.3  Mean Square for Error:  EMBED Equation.3  Testing for Differences Among Population Means: Null Hypothesis:  EMBED Equation.3  Alternative Hypothesis:  EMBED Equation.3  Test Statistic:  EMBED Equation.3  Rejection Region:  EMBED Equation.3  P-value: Area in F-distribution to the right of Fobs Large values of Fobs are consistent with the alternative hypothesis. Values near 1.0 are consistent with the null hypothesis. Analysis of Variance (ANOVA) Table Source of Degrees of Sum of Mean Variation Freedom Squares Square F-Statistic Treaments c-1 SSA MSA=SSA/(c-1) Fobs=MST/MSE Error n-c SSW MSW=SSW/(n-c) Total n-1 SST Note: MSW is an extension of the pooled variance sp2 from two sample problems, and is an estimate of the observation variance s2. Example: Impact of Attention on Product Attribute Performance Assessments A study was conducted to determine whether amount of attention (as measured by the time subject is exposed to the advertisement) is related to importance ratings of a product attribute. In particular, subjects were asked to rate on a scale the importance of water resistance in a watch. People were exposed to the ad for either 60, 105, or 150 seconds. The means, standard deviations and sample sizes for each treatment group are given below (higher rating scores mean higher importance of water resistance). Source: MacKenzie, S.B. (1986), The Role of Attention in Mediating the Effect of Advertising on Attribute Performance, Journal of Consumer Research, 13:174-195 Statistic 60 seconds 105 seconds 150 seconds Mean 4.3 6.8 7.1 Std Dev 1.8 1.7 1.5 Sample Size 11 10 9 The overall mean is: (11(4.3)+10(6.8)+9(7.1))/(11+10+9)=6.0 Complete the degrees of freedom and sums of squares columns in the following Analysis of Variance (ANOVA) table: Source df SS Treatments n1 SST Error n2 SSE Total SS(Total) SSA = 11(4.3-6.0)2 + 10(6.8-6.0)2 + 9(7.1-6.0)2 = 31.8+6.4+10.9 = 49.1 n1=3-1=2 SSW = (11-1)(1.8)2+(10-1)(1.7)2+(9-1)(1.5)2 = 32.4+26.0+18.0 = 76.4 n2 = 30-3=27 SST = SSA+SSW = 76.4+47.2 = 123.6 The test statistic, rejection region, and conclusion for testing for differences in treatment means are (a=0.05): MSA=49.1/2 = 24.55 MSW=76.4/27 = 2.83 Test Statistic: Fobs = 24.55/2.83 = 8.67 Rejection Region: Fobs ( F.05,2,27 = 3.35 Reject H0, Conclude that means differ among the three exposure times. Example: Corporate Social Responsibility and the Marketplace A study was conducted to determine whether levels of corporate social responsibility (CSR) vary by industry type. That is, can we explain a reasonable fraction of the overall variation in CSR by taking into account the firms industry? If there are differences by industry, this might be interpreted as the existence of industry forces that affect what a firms CSR will be. For instance, consumer and service firms may be more aware of social issues and demonstrate higher levels of CSR than companies that deal less with the direct public (more removed from the retail marketplace). A portion of the Analysis of Variance (ANOVA) table is given below. Complete the table by answering the following questions. Then complete the interpretive questions. Analysis of Variance (ANOVA) Source of Variation df SS MS F Industry (Trts) 17 25.16 (Q3) (Q5) Error (Q1) (Q2) (Q4) --- Total 179 82.71 --- --- The degrees of freedom for error are: 196 10.5 162 162 The error sum of squares (SSW) is: 107.87 3.29 57.55 57.55 The treatment mean square (MSA) is: 25.16 1.48 427.72 8.16 The error mean square (MSW) is: 57.55 9323.10 104.45 0.36 The F-statistic used to test for industry effects (Fobs) is: 4.11 0.44 0.11 0.24 The appropriate (approximate) rejection region and conclusion are (a=0.05): a) RR: Fobs > 1.50 --- Conclude industry differences exist in mean CSR b) RR: Fobs > 1.70 --- Conclude industry differences exist in mean CSR c) RR: Fobs > 1.50 --- Cannot conclude industry differences exist in mean CSR d) RR: Fobs > 1.70 --- Cannot conclude industry differences exist in mean CSR The p-value for this test is most precisely described as: greater than .10 less than .05 less than .01 less than .001 How many companies (firms) were in this sample? 17 162 179 180 How many industries were represented? 17 18 162 179 Source: Cottrill, M.T., (1990), Corporate Social Responsibility and the Marketplace, Journal of Business Ethics, 9:723-729. Example: Salary Progression By Industry A recent study reported salary progressions during the 1980s among k=8 industries. Results including industry means, standard deviations, and sample sizes are given in the included Excel worksheet. Also, calculations are provided to obtain the Analysis of Variance and multiple comparisons based on Tukeys method. Confirm the calculation of the following two quantities among pharmaceutical workers (feel free to do this for the other categories as well). We wish to test whether differences exist in mean salary progressions among the k=8 industries. If we let mi denote the (population) mean salary progression for industry i, then the appropriate null and alternative hypotheses are:  The appropriate test statistic (TS) and rejection region (RR) are (use a=0.05):  What conclusion do we make, based on this test? Conclude no differences exist in mean salary progressions among the 8 industries Conclude that differences exist among the mean salary progressions among the 8 industries Conclude that all 8 industry mean salary progressions differ. We are at risk of (but arent necessarily) making a: Type I Error Type II Error All of the above None of the above Source: Stroh, L.K. and J.M. Brett (1996) The Dual-Earner Dad Penalty in Salary Progression,Human Resources Management 35:181-201 Note: The last portion of the Spreadsheet will be covered in the next few pages.  Multiple Comparisons (Section 10.1 & Supplement) Assuming we have concluded that the means are not all equal, we wish to make comparisons among pairs of groups. There are  EMBED Equation.3  pairs of groups. We want to simultaneously compare all pairs of groups. Problem: As the number of comparisons grows, so does the probability that we will make at least Type I error. (As the number of questions on a test increases, what happens to the probability that you make a perfect score). Bonferronis Approach (Supplement) Logic: Conduct each test at a very low type I error rate. Then, the combined, experimentwise error rate is bounded above by the sum of the error rates from the individual comparisons. That is, if we want to conduct 5 tests, and we conduct each at a=.01 error rate, the experimentwise error rate is aE = 5(.01) = .05 Procedure: Obtain  EMBED Equation.3 , the total number of comparisons to be made. Obtain  EMBED Equation.3  , where aE is the experimentwise error rate (we will use 0.05) Obtain  EMBED Equation.3 the critical value from the t-distribution with n-c degrees of freedom Compute the critical differences:  EMBED Equation.3  Conclude that  EMBED Equation.3  (You can also form simultaneous Confidence Intervals, and make conclusions based on whether confidence intervals contain 0. Tukey-Kramer Approach: Replace step 4 with:  EMBED Equation.3  where QU values are given in Table E.9, indexed by c-1 on the top, and n-c on the side and aE=0.05,0.01 Example: Impact of Attention on Product Attribute Performance Assessments (Continued) For this problem: c=3, n1=11, n2=10, n3=9,  EMBED Equation.3  There are  EMBED Equation.3 comparisons The comparisonwise error rate is  EMBED Equation.3  The critical t-value is:  EMBED Equation.3  The critical differences for comparing groups i and j are:  EMBED Equation.3  Results Table: Treatments (i,j)  EMBED Equation.3   EMBED Equation.3  Conclusion 60 vs 105 (1,2) 4.3-6.8 = -2.5 1.43 m1-m2 < 0 (|-2.5|>1.43) 60 vs 150 (1,3) 4.3-7.1 = -2.8 1.47 m1-m3 < 0 (|-2.8|>1.47) 105 vs 150 (2,3) 6.8-7.1 = -0.3 1.50 m2-m3 = 0 (|-0.3|<1.50) Often when the means are not significantly different, you will see NSD for the conclusion in such a table. Chi-Squared Test for Contingency Tables (Section 11.1-2) Goal: Test whether the population proportions differ among k populations or treatments (This method actually tests whether the two variables are independent). Data: A cross-classification table of cell counts of individuals falling in intersections of levels of categorical variables. Example: Recall the example of smoking status for college students by race: SmokeRace YesNoWhite38076738Hispanic261757Asian257860Black125663 Step 1: Obtain row and column totals: SmokeRace YesNoTotalWhite3807673810545Hispanic2617571018Asian2578601117Black125663788Total4450901813468 Step 2: Obtain the overall sample proportions who smoke and dont smoke Proportion smoking = 4450/13468 = .3304 Proportion not smoking = 9018/13468 = .6696 Step 3: Under the null hypothesis that smoking status is independent of race, the population proportions smoking are the same for all races. Apply the results from step 2 to all the row totals (these are called EXPECTED COUNTS). Expected count of Whites who Smoke: .3304(10545) = 3484 Dont: .6696(10545)=7061 Expected count of Hispanics who Smoke: .3304(1018) = 336 Dont: .6696(1018)=682 Expected count of Asians who Smoke: .3304(1117) = 369 Dont: .6696(1117)=748 Expected count of Blacks who Smoke: .3304(788) = 260 Dont: .6696(788)=528 Table of Expected counts under the constraint that the proportions who smoke (and dont) are the same for all races (note that all row and column totals): SmokeRace YesNoTotalWhite3484706110545Hispanic3366821018Asian3697481117Black260528788Total4450901813468 Step 4: For each cell in table, obtain the following quantity:  White Smokers:  EMBED Equation.3  EMBED Equation.3  White Non-Smokers:  EMBED Equation.3  EMBED Equation.3  Repeat for all cells in table: SmokeRace YesNoTotalWhite29.9514.78Hispanic16.748.25Asian33.9916.77Black70.1034.52Total Step 5: Sum the quantities from Step 4  Step 6: Obtain the critical value from the Chi-square distribution with (r-1)(c-1) degrees of freedom, where r is the number of rows in the table (ignoring total), and c is the number of columns: For this table: r=4 (races) and c=2 (smoking categories):  EMBED Equation.3  Step 7: Conclude that the distribution of outcomes differs by group if the statistic in Step 5, exceeds the critical value in Step 6. 225.10 > 7.81473 Conclude that the probability of smoking differs among races. Chi-Squared test for more than two populations  EMBED Equation.3  EMBED Equation.3  EMBED Equation.3  Null Hypothesis H0: Two variables are independent (p1=...=pc when there are c groups and 2 outcomes) Alternative Hypothesis HA: Two variables are dependent (Not all pi are equal when there are c groups and 2 outcomes) Test Statistic -  EMBED Equation.3  EMBED Equation.3  Rejection Region -  EMBED Equation.3  EMBED Equation.3  P-Value Area in chi-square distribution above the test statistic Pairwise Comparisons (Marascuilo Procedure) Critical Range: Conclude pj ( pj if their sample means differ in absolute value than:  EMBED Equation.3  where  EMBED Equation.3  is the critical value from the test that all c proportions are equal. Example: Smoking by ethnicity: Group (j)psjnjWhite (1)3807/10545 = 0.361010545Hispanic (2)261/1018 = 0.25641018Asian (3)257/1117 = 0.23011117Black (4)125/788 = 0.1586788 For each comparison:  EMBED Equation.3 =7.815, critical values and differences:  EMBED Equation.3  Conclude whites have higher rate than all other groups, blacks have lower rates than all other groups and that hispanics and asians are not significantly different.  EMBED Excel.Sheet.8   EMBED Excel.Sheet.8   EMBED Excel.Sheet.8   EMBED Excel.Sheet.8   EMBED Excel.Sheet.8   EMBED Excel.Sheet.8   EMBED Excel.Sheet.8   EMBED Excel.Sheet.8   EMBED Excel.Sheet.8   EMBED Word.Picture.8   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Excel.Sheet.8   EMBED Excel.Sheet.8   EMBED Equation.3   EMBED Excel.Sheet.8   EMBED Word.Document.8 \s   EMBED Excel.Sheet.8   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED PDF.PdfCtrl.1 \s   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Excel.Sheet.8   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  ?Pfopq0%4[`Z[\f 9:Etz|    I K d e ~ , 9 : з 6OJQJ56OJQJOJQJ 5OJQJ5>*OJQJ>*5>*CJOJQJ5CJ6CJ5CJCJ5CJOJQJ5>*CJ$OJQJ5>*CJ OJQJCJ,5CJ$OJQJ5CJ,OJQJ:(=>?Pfghijklmnopq$$$(=>?Pfghijklmnopq&'([\:;~ytR01defst   ,&'([\:;}~        I $;}~        I J K d e f v w x y z { | }zwtqnk  y  z{|}~   Sqrstuvw Q)I J K d e f v w x y z { |  & F$$#$ & F#$# i t x y | ! 3 5 H N n 6;>KY_BIagtBDIKTV-2wҹҹҹB*OJQJh5B*OJQJh6CJOJQJ5CJOJQJ CJOJQJ5>*OJQJCJ5CJOJQJ56OJQJOJQJ 5OJQJB #)*.D8$$\b $$$ #)*.26:;>BFJKQUY^_`a~yurolid  GHIJ  O  S  W  ]^  b  f  j  mn  r  v  z  ~          %.26:;>BFJKQUY^_`a$.@P$$8$$\b $$$.45=BIPQ\agmnty}zwtqnid       !  (  /  4  :;  A  G  L  WX  _  f  k  st  z    &.45=BIPQ\agmntypth$$$8$$\ gY$`C$$rN  $$  $'(/37;>?@Adefghi|xurolifc`+,-./RSTU  X  \  `  d  kl  o  s  w  {                            $  $'(/37TX\$C$$rN  $$(,/?jksHbg"J^c$%  g 7!8!!!!"######ݽݯݢݗݗݗݗݗݍ5CJOJQJ56CJOJQJOJQJ 5OJQJ5>*CJOJQJ0JjCJOJQJUjCJOJQJU5>*OJQJjCJOJQJUmH CJOJQJ5B*OJQJhB*OJQJh56B*OJQJh47;>?@Adefghijlmnopqrs$C$$rN  $$ijlmnopqrs\cz{|~3456789:;<=~{xu789:;<=>?@Vmmn +!"#$%&')*-s\cz{|~345$&d$56789:;<=>?@ABCDEFGH`ababc$$=>?@ABCDEFGH`ababc$% f g 7!8!!! ""##$$ % %*%+%,%ý~{xurolHIghWXl  m e f;  <    hiN  O  +,-./0123456)c$% f g 7!8!!! ""##$$ % %*%+%,%&&&q(r($$ & F$ & F$$ & F##$$2$ %,%@%l%m%%%r&s&&&& ''p(s((d))))^*_*a*w*_+++,,,,,-=-Y---6.G..../?/B/U////////\0b00000112222z333333333>*CJ6H*OJQJ 6OJQJ5>*CJOJQJ 5OJQJOJQJ CJOJQJ5CJOJQJO,%&&&q(r(s(t())))U+V+W+X+Y+Z+[+\+]+^+_+++++,,,6-7-=----6.7.......}zwtqvwxyz<  =   6 <=G+r(s(t())))U+V+W+X+Y+Z+[+\+]+^+_+++++,,,6-7-=--$ & F $$---6.7........///////// / / / / //?/@/$ & F $..///////// / / / / //?/@/A/B////0001222222333p4q4555555~{xr tuvwABCefg  fghijklmnopqrstu,@/A/B////0001222222333p4q455555555$$33l4m4o4p4q444444R5S55555555555566647<7777777888889!:-:: ;;A;D;{;;<<<<H=d=X>h>s>>^?s?t?y?@@'@1@2@\@^@@@@ AAA CJOJQJ6CJOJQJ CJOJQJ 6OJQJ>*CJ 5CJOJQJ56OJQJOJQJ 5OJQJM5555555555777888999":#:::::::::::: ; ; ;<<<F=G=H=u>v>w>m?n?|yvC9:;hij -55555555777888999":#:::::::::::$$:: ; ; ;<<<F=G=H=u>v>w>m?n?o?@@@@ @!@"@#@$@%@&@'@(@$n?o?@@@@ @!@"@#@$@%@&@'@(@1@2@\@]@AAAAAAwAxAzA{A|A}AAAAAAAAAAAzupls  t  u  vw  x     AB((@1@2@\@]@AAAAAAwAxAzA{A|A}AAAAAAAAA"$$0 $$AAAAvAwAxAyA}AAAAAAABBVBWBBBBBBB)C*C+C,C1ChCjClCmCpCCCCCmDnDD EEEGHH7H8H:H*CJOJQJOJQJB*OJQJh5B*OJQJh5CJOJQJ CJOJQJ jU 5OJQJCJ5CJ5CJ CJOJQJjCJOJQJUmH3AAAAAAAAAAAAAAAAAA B B B<$8LH,"$$0 -$$F $AAAAAAAAAAAAAAAA B B BBBBBBBBBBBBVBWBBBBBB*C~xurolifc,-.01        %  '(  7  9:  J  LM  X  Z[  \  a  cd  e  l  r% BBBBBBBBBBBBVBWBBBBBB*C,C-C.C/C$-$$F $*C,C-C.C/C0C1CjCkClCnCoCpCCCCCCCCCCCCCCCCCCCCCCCCCCCCCEEEE|w     aXYZ[\]^_`abcdefghijklmnop,/C0C1CjCkClCnCoCpCCCCCCCCCCCCCCCCCCCCCC$CCCCCCCCEEEE$E%E'E-E3E4E6E<<-$$F -$$F $$$E$E%E'E-E3E4E6E  D  J  MN  T  Z  ]^  d  j  mn  t  z  }~              "CFIFJFMFSFYFZF]FcFiFjFmFsFyFzF}FFFFFFFFF@@@@@<-$$F $$FFFFFFFFFFFFFFFFFFFFFFFF@@<<@@-$$F $$FFFFFFFFFFFFFFFFFFFFFFGGG GGGGG G&G'G*G/G~zupkgb]                                                    "FFGGG GGGGG G&G'G*G/G5G6G9G?GEGFGIGOGUG@@<@@-$$F $$/G5G6G9G?GEGFGIGOGUGVGYG_GeGfGiGoGtGuGxG~GGGGGGGGGGGGGGGzuqlgb^    "  %&  ,  1  45  ;  ?  BC  I  O  RS  X  ^  ab  h  n  qr  x  ~          "UGVGYG_GeGfGiGoGtGuGxG~GGGGGGGGGGGGG@<@8<<$$-$$F GGGGGGGGGGGGGGGGGGGGGGGH@HHHHHHIIIzJ{J|J}J~JJ|yvspmjgb                                     &H@HHHHHHIIIzJ{J|J}J~JJJJJJ<8$$\ $$$$H?HHHHIIyJ~JJJJJJJK?K@KDKLLLPLQLRLTL\LLLcMeMfM|MMMN-NNNN0QQQQQQ"R4R5R6RPRS'S½ºȽB*CJOJQJh5B*CJOJQJh CJOJQJ jUmH5>*CJOJQJCJ jU 5OJQJ5CJOJQJ5B*OJQJhB*OJQJh6CJOJQJ CJOJQJjOJQJUmHOJQJ6JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ}xtqnkheb_                                               $JJJJJJJJJJJJJJJJJJJJJJJHL@@$$8$$\ $JJJJJJJJJJJJJJJJJJJJJJJ$8$$\ $$JJJJJJJJJJJJJJJJJJJJJJ>K?KAKBKCKDKLLLLLLLLLLQLSLTLULVLWLXLYL|yv1234568nopqrstuvwEFGHJK-JJJJJJJJJJ>K?KAKBKCKDKLLLLLLLLLLQLSLTLUL$ULVLWLXLYLZL[L\L]L^LLLcMdMeMgMhMiMjMkMlMmMnMoMpMqMrMsMtM$YLZL[L\L]L^LLLcMdMeMgMhMiMjMkMlMmMnMoMpMqMrMsMtMuMvMwMxMyMzM{M|M}M~MMþ}xsnida^[   ./0#tMuMvMwMxMyMzM{M|M}M~MMMMMMNNNNNNN-NNNNN$$MMMMMNNNNNNN-NNNNNNNNNNOO OOOOOO#O)O*O/O5O6O;O@O½|wrnid                               !  &' "YZ[\]^_`%NNNNNNOO OOOOOO#O)O*O/O5O6O;O@OAOFOLOMO000000,00$$"$$0@OAOFOLOMOROXOYO^OdOeOjOoOpOuO{O|OOOOOOOOOOOOOOOOOOOOþ}xsojea\  CD  J  OP  V  [\  b  gh  n  st  y  ~                        #MOROXOYO^OdOeOjOoOpOuO{O|OOOOOOOOOOOOOO0,00,000"$$0$$OOOOOOOOOOOOOOOOOOOOOP P PPP00000000"$$0$$OOOOOOOOOOOOOOOP P PPPPP"P#P(P.P/P4P:P;P@PFPGPLPRPSP¾|xsnje`\                                      &  +,  2  78  >#PPP"P#P(P.P/P4P:P;P@PFPGPLPRPSPXP^P_PdPjPkPpPvPwP000000000$$"$$0SPXP^P_PdPjPkPpPvPwP|PPPPPPPPPPPPPPPPPPPPPPPPPP½|wsnie`[  /  45  :  ?@  E  JK  P  UV  [  `a  f  kl  r  wx  ~                  #wP|PPPPPPPPPPPPPPPPPPPPPPPPP00,,,,,,"$$0$$PPPPPPPPPPPPQQQ QQQQQQQ$Q%Q*Q/Q(,(, ,,,"$$0$$PPPPPPPPPPPQQQ QQQQQQQ$Q%Q*Q/Q0QQQQQQQQQQQQQQþ|yvspmjgdopqrstvwxyz{                               $  )*&/Q0QQQQQQQQQQQQQQQQQQQ4R6R7R8R9R:R$"$$0QQQQQQ4R6R7R8R9R:R;RR?R@RARBRCRDRERFRGRHRIRJRKRLRMRNRORPRSS S!S"S#S$S%S&S'S(SSS~{xurstuvwxyz{|JKLMNOPQRSTUVWXYZ[\]^_`abcdf mn-:R;RR?R@RARBRCRDRERFRGRHRIRJRKRLRMRNRORPRSS S!S"S#S$S$$S%S&S'S(SSSTSSSSTTTTGTHTJTKTTTTTTTUUU8V9V$$'S(SSSTSSSSTTT=T>TBTCTFTHTITJTKTWTwTxT|T}TTTTTUU7V;VVVVVVVVVWRWwWxW|W}WWWWWWWWWWWWWWWWWཱུğ5CJOJQJ5>*CJOJQJ5>*6CJOJQJ CJOJQJ jUmH5H*jOJQJUmH H*OJQJ 5OJQJ5OJQJ5>*CJ OJQJ5CJ OJQJ>SSTSSSSTTTTGTHTJTKTTTTTTTUUU8V9V:V;V_VVVVVVVVVVWWzwtqnkhstu               LMNO FG'9V:V;V_VVVVVVVVVVWWWWWWWWWWWXXX & FWWWWWWWWWWXXX\Y^YZZZZZZZZ Z Z Z Z ZZZFZGZHZ[[[_[`[a[c[d[e[f[[[~{xud  yghi,-/0,WXXpXrXXXXZY\Y^YxYzYYYYYYYYYYYZFZHZTZ[([W[X[Z[[[^[a[b[f[x[[[[[[[[[[[[I]^^ ^"^$^&^(^*^8^:^<^@^ƿҵҵ jOJQJ jUmHjOJQJUmH H*OJQJ5>* jhEHUj%D 5CJUVOJQJ jEHUjꁖD 5CJUV jU 5OJQJ 5OJQJOJQJ*CJOJQJ56OJQJjOJQJUmH6H*OJQJ 6OJQJ5>*OJQJ jOJQJ H*OJQJ 5OJQJOJQJOJQJ jOJQJ<:a;a,c.ccccdddd!dFdGddd[ee7ffgrggRhh%i#&d d#d & F#dddeeYeZeee*f+fffffegfgggEhFhhhiiiijjjkknlolllDmEm^m}mmmgno@opoqoooooJpKpapbprpsppppp@sdsfsgshss5CJOJQJB*OJQJh6B*OJQJh5>*CJOJQJjCJOJQJUmHCJH*OJQJ6CJOJQJ CJOJQJ CJOJQJ6CJOJQJBgrggRhh%iijjjjjjRkSkzk{knlplrllllllmmmmmmmmmmmmmmmmǿ}ytokf  XY  b  qr  {        WXY  {    V8(%iijjjjjjRkSkzk{knlplrllllllmmmmm$$$# & F##&dmmmmmmmmmmmmmmmmmmmmn n n <d,$thL$"$$0[ xy $$$$x mmmmmmmmmn n nnnn'n0n1n7n:n;nCnFnGnOnRnSnWn\n]ncnfngnhninjnkn¾|xsnjgda^                                 '(  1  DE  H  MN  Q$ nnnn'n0n1n7n:n;nCnFnGnOnRnSnWn\n]ncnfnL(00(("$$0[ xy $$$fngnhninjnknnnoooo o o o o oooooooooo$#"$$0[ xy  knnnoooo o o o o ooooooooooo?o@oKpLpMpp qfqqrrr=s>s?s@sdsesfshssssss~{xu0\]^=X l     VW.oo?o@oKpLpMpp qfqqrrr=s>s?s@sdsesfshssssss4t6t$#&d$#sssssssss t"tttuuzuuuuuuvv!vKvLvMv[v^vlvvvvww+w,w`wawwwwwwwwwxlynyvyyyy,z0z<{G{I{ҕ56CJOJQJ>*CJOJQJOJQJjCJOJQJUmH6CJOJQJB*OJQJh6B*OJQJh5CJOJQJ jU5>*CJOJQJCJH*OJQJCJH*OJQJ CJOJQJ:s4t6tttPuRuzu|u~uuuuuuuvvvvv v!v%v.v/v0v8vAvJvKvLvMv\v]v^v_vcvkvlvpvĿ|wrni                          "#%&WXYZ[op/(6tttPuRuzu|u~uuuuuuuvvvvv v!v%v.v<$$$-$$Fp\    $$$#.v/v0v8vAvJvKvLvMv\v]v^v_vcvkvl$$$#-$$Fp\    $-$$Fp\ $$kvlvpvrvsvtv|vvvvvvv Ppkkk$#-$$Fp\    -$$Fp\ $$$-$$Fp\     pvrvsvtv|vvvvvvvvvvvvvvvwwwwwwwwwwwwwwjylypyrytyvyxyzy|y~{xurolibcdefgij !"  +  -.  7  AB  O  PQR`ab          )vvvvvvvvvwwwwwwwwwwwP0"$$0 $$$"$$0   $$$$#wwwwjylypyrytyvyxyzy|y~yyyyyyyyyyI{J{$$$#"$$0   |y~yyyyyyyyyyI{J{{{O||||~~tuwx2Uxyz%&Ŀ|wrmc^  34    :;u)}-.YZ[\]^_`a"I{J{||}}}~~~~~~~ ruv&'(567IJKXYZlmn'()*,-./0jCJ UmHCJ 5CJ 56CJH* 5CJH* 56CJj5CJUmH5CJH*OJQJ56CJH*OJQJ CJOJQJ56CJOJQJ5CJOJQJ5CJ5CJ*CJ655>*CJ 5>*CJ OJQJ CJOJQJ jUmHABц҆ $$$$ & F ц҆ '08>?NRW\]jnsxy½~zupkfb                            #  ,  67  8  F  GHIJ& ,-.456JKLN]fghjy]^deϊЊ֊׊HIOPjkmŌƌ͌ΌԌՌ܌݌'(34;<BCJKQRýԵ͵5CJOJQJ 5OJQJ5CJH*OJQJ CJOJQJ5>*56B*H*OJQJh56B*OJQJh5B*OJQJhB*OJQJhG'08>?NRW\]jnsxy҈xpT$$8$$\ d%$-$$Fd% y݉މVW~ȊɊA8$$\ d%$$$݉މVW~ȊɊABijklm+,kl~{xu'(UVdefgcdNOuv.ABijklm+,klRčō !()/078>? "$8:<R[]_u{?X̐ѐאؐ6789ab9:jCJOJQJUmH6CJOJQJ5CJOJQJ56B*OJQJh5B*OJQJhB*OJQJhCJH*OJQJ CJOJQJHߍXYZ &,-<CJQR_fmtu{~ytokheb                        &'  -  5  >  HI  J  X  YZ[\78st&ߍXYZ &,Ȉ-$$F,d%% 8$$$,-<CJQR_fmtu{56ǔnjp$$$8$$\ ,d%8856789:;<=>?78:;<pqrǓȓɓIJKLklɕʕ~{xu  =>?.6789:;<=>?78:;<pqrǓȓɓIJKLklɕʕ˕͕̕ߖ$rƓǓғӓՓ֓Lilɕ˕ו&LMxyƖǖ֖זܖݖ'(67M|~ŽŲŧŜŜŜŜŜŜŜŜ5>*B*CJOJQJhjCJOJQJUmHB*CJH*OJQJh6B*CJOJQJh5B*CJOJQJhB*OJQJhB*CJOJQJh5>*OJQJ jCJOJQJCJH*OJQJ6CJOJQJ5CJOJQJ CJOJQJ3ʕ˕͕̕ߖ56<=>?@ABCDEFGHIJKLM|}~ݙޙ~{xuvCD     lmnopqrst89:;<.56<=>?@ABCDEFGHIJKLM|}~$~JK[\ܙޙߙ 4>?ATZnoqԛ՛+46MN'({˞012NPq㯢㗍B*CJOJQJh5B*CJOJQJh5>*B*CJOJQJh CJOJQJ5B*H*OJQJh5B*OJQJhB*OJQJh6B*OJQJhB*OJQJhB*OJQJhB*CJH*OJQJhB*CJOJQJh4ݙޙߙ  %,34Ȁt8$$\ -$$F$$$ޙߙ  %,34AELSTZ_fmnopqBCDEFG}zwolifc` m  no                    '  .  3  <=  C  F  N  \]  ^  t  u$4AELSTZ_fmnopqBCDEFh & F 8$$\ $$$FGћқӛԛ՛,-./023456MN'(xyȞɞ$ & F Gћқӛԛ՛,-./023456MN'(xyȞɞ02MsOP֠¿}zwtq}Ep!#BCD+,PQ !#$%&'~     ,ɞ02MsOP֠rsȡɡ Wcrsȡɡ WcQRߨUĩũ<=V([#LM~{xu0f+_`jLMst6<>I7.qrsȡQR45ߨũMQZ[]nݭ3RϷOJQJ 5OJQJ5CJOJQJ>*CJ 5>*CJ OJQJ CJOJQJ5B*CJOJQJh5>*B*CJOJQJh6B*CJOJQJhB*CJOJQJhB*CJOJQJh5B*CJOJQJhB*OJQJhB*CJOJQJh4QRߨUĩũ<=V([$#LMQ[\]­íĭ456ˮ̮ͮή$MQ[\]­íĭ456ˮ̮ͮή'(]^_`abcdefg|yvsp)  * U VW  ;<=>+RʮˮήlofgвѲԵ -.01HInoDEʹ˹̹͹ιϹй !ܬܴܴjB*CJOJQJUmHB*CJOJQJhB*OJQJh5>*B*OJQJhCJH*OJQJj5OJQJUmHjOJQJUmH5>*OJQJ6CJOJQJ CJOJQJ5>*CJOJQJ jOJQJ 5OJQJOJQJ/ή'(]^_`abcdefghijkl$ & FghijklӲhNMNOPǵȵɵʵ˵̵͵εϵеѵҵ|yvs    Q4~,ӲhNMNOPǵȵɵ$ & F$&d$ɵʵ˵̵͵εϵеѵҵӵԵ -/012HJKLMNnpv$ҵӵԵ -/012HJKLMNnpvP$`ɹʹ̹ιй ӼԼռּٽڽ|yvx{|}~FGZ-mno  !"$GHI}~-P$`ɹʹ̹ιй ӼԼռּٽڽ۽ܽݽ$&d$ڽ۽ܽݽ޽߽QRSTUVWXYZ[\]^_`abc̿Ϳ#%&':;NQUV{vr        *  +,.:;pqrstuvw+ݽ޽߽QRSTUVWXYZ[\]^_`abc$$!CQRW_`abc˿Ϳ#$%&':;Zeitv|}DG9:stv&(*վ➕wwB*CJH*OJQJhB*CJOJQJh5B*OJQJh5B*OJQJhB*OJQJhjB*OJQJUmH5B*CJOJQJh5>*B*CJOJQJh5>*CJOJQJ CJOJQJ 5OJQJB*OJQJhB*CJOJQJh6B*CJOJQJh.̿Ϳ#%&':;NQUVZ_deinsl<<-$$Fp :8"$$0p:$$$$VZ_deinstuvD9:suv8:XZ^`@c|yvsp@%{j NPJKcde            +stuvD9:suv8:&d$-$$Fp :8*~>@BHJLVX^` ",.0"$^`'EFG~········5>*B*OJQJh5B*CJOJQJhjB*OJQJUmHB*OJQJhB*CJH*OJQJhB*CJOJQJh5B*CJOJQJhj5B*OJQJUmH5B*OJQJhB*OJQJhB*CJOJQJh2XZ^`@c "\^` !&d$ "\^` !"#$%&'EFG}~qrs4O567bcdfg~{xufhij)HVWklm?.!"#$%&'EFG}~qrs4O567bc&d$$9abdeg&')*ABDE^_`defgklmnz{}궮ꮣЖЖЖЖ~~ jB*CJOJQJhB*CJH*OJQJh5B*CJH*OJQJh5>*B*OJQJhB*OJQJhjB*OJQJUmH5B*CJH*OJQJh5B*CJOJQJhjB*CJOJQJUmHB*CJOJQJhB*CJH*OJQJh1cdfg12GH g12GH 23HI~{xu#$%}~<=PQ()*jklmnop)*<=. ()3FPQWXYZfghirswxB*CJH*OJQJh5B*CJOJQJhB*CJOJQJhB*CJH*OJQJhT 23HI !" & FIJqr "Xhu׹י5>*B*OJQJh5CJ OJQJ5>*CJ OJQJCJ5CJOJQJ6B*CJOJQJh5B*CJOJQJhB*OJQJhB*CJH*OJQJhB*CJOJQJhB*CJH*OJQJh< !"WXtu|tqifW  X  t  u  $%&  .  /   OPQR:;=/0^_ | 'WX & F$ & F$$tuWXY678$ & FWXY678pqrs<=|yvs+,-./56YZ[\    HIstu,WY68?ns <=i&')*,-a?ef*$%&#$%&GZ] jEHUj׍D CJUV jU5CJOJQJ56CJOJQJ CJOJQJ 5OJQJ5B*CJOJQJh5>*B*OJQJhB*CJOJQJhB*OJQJh@8pqrs<=abc$$ & Fabc>?ef()*%&4w|ytojg+n  |  }x  yz  <= c d  ?@A *'>?ef()*%&$$ & Fh & F4w'()*+\GHIJKL$h & F'()*+\GHIJKLMNOPQRSTUVWXYZ]^_¿}zwtqDEHIJKLMNOPQRSTUVWXYZ[Fw  xyz{  ,LMNOPQRSTUVWXYZ]^_`U$ & F$_`U|}`abc,fg()*x>¿}wogH  H   H *xyz;<v?@AB%&M        "   B C%]`_ffj)*x-.!"5678ؿصؙj B*EHOJQJUhj:—D CJUVjB*OJQJUhjw 5B*EHOJQJUhjD CJUVj5B*OJQJUh56B*OJQJh5B*OJQJh5>*B*CJOJQJhB*CJOJQJhB*OJQJh1|}`abc,fg()*x & FH$$>p !9:qrs,-noh & FH>p !9:qrs,-no '(*zuql  z{    34uv/01hiH  H  2H  dH  ('()*KLMNQRo'(0婙tlib CJOJQJCJ5CJOJQJj 5B*EHOJQJUhjΗD CJUV56B*OJQJhj35B*EHOJQJUhjΗD CJUVj5B*OJQJUhjB*EHOJQJUhj̗D CJUVjB*OJQJUh5B*OJQJhB*OJQJh6B*OJQJh& '(*0139:<EFHQ$$00$$"$$l0$*0139:<EFHQRT]^`ijltuw¾|xsnjga^ v             !"  +  -.  6  89  B  DE  N  PQ  Z  \]  f  hi  o  qr  x#019:EFQR]^ijtuͿͿʹ͕ͪ~jB*OJQJUh56B*OJQJh5CJ OJQJj 5EHOJQJUjSٗD CJUVj5OJQJU56OJQJ 5OJQJ 5OJQJ5>*CJOJQJ5CJOJQJ jUCJ5B*OJQJh CJOJQJCJ/QRT]^`ijltuw00,0,,4$$"$$l07V & FI$7VZ\^ef~{xurolif34bcdI  I  OI  I  I  nI   I (Z\^ef$$f)*56ABMNYZefqr~øήΙΊ j)U CJOJQJCJ5CJOJQJj&5EHOJQJUjݗD CJUVj5OJQJU 5OJQJOJQJ 5OJQJjB*OJQJUhj#B*EHOJQJUhjٗD CJUVB*OJQJh3f  )*,568ABDMNPYÿ}ytokfa                                      !:;<=>?@AZ[&  )*,568ABDMNPYZ0,0000000$$"$$l0YZ\efhqru~þ|yvspmjgdCDEFGHIJKLNOPQ  Z  ^_  h  kl  u  xy                    &Z\efhqru~0444448$"$$l0$$vx   4 5     & F$$vx   4 5     9 :           N R        ¿~{xurolifBLN    l  m    n  o     7^AB&vRV|~   ! ) * + , 7 8 : ; < = @ A B C E F         , -           & (          CJH*OJQJ5CJOJQJ jsCJOJQJ jCJOJQJ jmCJOJQJ jCJOJQJCJH*OJQJ CJOJQJ CJOJQJ 5OJQJCJ 5CJ OJQJ?  9 :           N R        " & F & F    $ & ( < > @ B F H J ` d f h n p t x            mx  "﷯ j5B*CJOJQJhB*CJOJQJh5B*CJOJQJhB*CJOJQJhB*OJQJh5>*B*OJQJh CJOJQJ jCJOJQJ jsCJOJQJCJH*OJQJ jmCJOJQJ CJOJQJ jCJOJQJ1 "VX>?B !"#$% >@BDF|w,-./0123456789:;<=>cd@A,VX>?B"$VX$&EFIJVWde<=()/0 68:ޤyljJ2CJEHOJQJUjoD CJUVmHjCJOJQJU5CJ5CJOJQJ5CJOJQJ CJOJQJ jm5B*CJOJQJh js5B*CJOJQJh5B*CJH*OJQJhB*CJOJQJhB*OJQJh5B*CJOJQJh j5B*CJOJQJh* !"#$% >@BDFJLNPRTVXZ\^`bdf$$:<BFHhjlvxz,.0HJLdfh~  "$&tvx68:RTVĺĺĺĺ̶ԮԮԮԮԮԮԮԮԮԮԮԮԮԮԮ 5CJH*5CJOJQJ5CJ5CJH*OJQJ5CJOJQJ5CJOJQJ5CJjCJOJQJUmH>*CJOJQJ CJOJQJjCJOJQJUBFJLNPRTVXZ\^`bdfhjlnprtvxz|~Ŀ~ytoje`[!fhjlnprtvxz|~$h$0z78 ! V W ǽzukf\WMH  :  ;v  w  k      `      tuv0z78 ! V W     &&&&'''(((CJ5CJ5H*OJQJ 5OJQJ 5OJQJ CJOJQJ56CJOJQJ5CJOJQJ 56CJH* 56CJ 5CJH*5CJOJQJ5CJ@W     *CJOJQJ5CJ5CJOJQJ5>*CJOJQJ,...//////p00000:1X1111111111122222222O2i2j2l2222 3詡{{qi6CJOJQJ5>*CJOJQJ5H*OJQJ5H*OJQJ5H*OJQJ5H*OJQJ 5OJQJ56OJQJ 5OJQJjCJOJQJUmH5CJOJQJj7CJEHOJQJUj@ CJUVmHjCJOJQJU CJOJQJCJH*OJQJCJH*OJQJ)-00000\1^1j2k2l2~222333333444446666$ & F$ 33M3N3O333333@4p4444444445555555Q6R6T6V6X6666661727@8B8D8N8P8R8888888F999999Ǽޠޠޠޠި 5CJH*5CJH*OJQJ5CJOJQJ5CJ>*CJOJQJCJH*OJQJ56CJH*OJQJ56CJOJQJjCJOJQJUmH5CJOJQJ6CJH*OJQJ CJOJQJ6CJOJQJ83344444666666666666666661727F9H99:0;;v<===~tje`  5      ,  |  })*+,-./012345679:TU#666666666666661727F9H99:0;;v<===/?0?$ & F$h$$99999999,:.:0:H:J:L:f:h:j:~::::::::: ; ;;";$;&;v;x;z;;;;;;;;;;;<<<<<2<4<6<N<P<R<f<h<j<<<<<<<<<< ====??o?p??j5CJOJQJU56CJOJQJ5CJOJQJ 56CJH* 56CJ5CJOJQJ5CJ 5CJH*L=/?0?????@@=@>@f@g@@@@f@g@@@*CJOJQJ5>*CJOJQJ5>*CJOJQJ5CJ OJQJB*OJQJhjCJOJQJUmH jCJOJQJ CJOJQJj5CJOJQJUmH6CJOJQJ CJOJQJ56CJOJQJ>*CJOJQJ5CJOJQJ&GqGrGsGGGGH2I3I4IIIJJK,KPKKKKKKK4L6LrL$$h$ & F $$&dGH2I3I4IIIJJK,KPKKKKKKK4L6LrLtLLL`MbMMM N"N*NtNvNNNOZOOĿ}zwtqnkhebs ?@op>?@l  mn()`  a  !%rLtLLL`MbMMM N"N*NtNvNNNOZOOOOOOOOOOOOOO$LLLLMMMMMMMMMMNNNN"N(N*N0N2NnNpNtNvNxNNNNNNNNNNȻ펇{nfa\aOJQJOJQJ5CJ OJQJjKCJEHOJQJUj]D CJUVmH CJ OJQJ CJ OJQJjCJOJQJUmHj]HCJEHOJQJUjD CJUVmHjECJEHOJQJUj}D CJUVmHjCCJEHOJQJUjk}D CJUVmH CJOJQJjCJOJQJU$NNO$O&O>OHOZO|O~OOOOOOOOOOOP.P2PxPzPPPPP4Q6Q\Q^Q`QԿ|ocVjQCJEHOJQJUjSD CJUVmHj(OCJEHOJQJUjk}D CJUVmHjCJOJQJU CJOJQJ CJOJQJ5>*CJOJQJ5>*CJOJQJ5>*CJOJQJ5CJOJQJjCJ OJQJUmH CJ OJQJOJQJ5H*OJQJ 5OJQJ 5OJQJOJQJ H*OJQJ!OOOOOOOOOOOOOOOOOPP`PbPQQ|R~RRRR0S2SbSdSSSTTTTTT T"T$T&T(T*T,T~{xu   2Pno56789:;<=>?@ABCDF.OOOOOPP`PbPQQ|R~RRRR0S2SbSdSSSTTTTTT T"T$`QbQ|Q~QQQPRRRvRxRRRRRRRRRRRR*S,S0S2S4SZS\S^S`SbSdSSSSSSSSTTT픇zuzoiozuz 5OJQJ 5OJQJOJQJOJQJ5CJ OJQJj?WCJEHOJQJUjD CJUVmH CJ OJQJ CJ OJQJ5CJOJQJj5CJOJQJUmHjSCJEHOJQJUjD CJUVmH CJOJQJ6CJOJQJ CJOJQJjCJOJQJU)TTT,TTTTTUUUUUUUUUUUUUUVVV V"VHVJVLVNVPVRVtVvVVVڶڪ閏ڶ{nidi^ H*OJQJOJQJOJQJj]CJEHOJQJUjD CJUVmH6CJ OJQJ CJ OJQJ CJ OJQJjZCJEHOJQJUj݈D CJUVmHjCJOJQJU6CJH*OJQJ CJOJQJ6CJOJQJ CJOJQJ5CJOJQJ5CJ OJQJj5CJ OJQJUmH$"T$T&T(T*T,T.T0T2TTTTTjUlUUUUV VPVRVVW^WbWdWfWhWjW$,T.T0T2TTTTTjUlUUUUV VPVRVVW^WbWdWfWhWjWlWnWpWrWtWvWxWzW|W~WWWWWWWWXXYYY~{xu567]^_`abcdefghijklmnoq67;jk  .VVVVVVW&W(W@WJW\W^W`WWWWXXY(Y*YPYRYTYVYzYYYYYYYYYYYZZ Z"ZZԾrjj6CJOJQJj dCJEHOJQJUjaD CJUVmHjaCJEHOJQJUjD CJUVmHjCJOJQJU CJOJQJ5CJOJQJ CJOJQJj5CJ OJQJUmH5CJ OJQJ H*OJQJOJQJ5H*OJQJ 5OJQJ 5OJQJOJQJ)jWlWnWpWrWtWvWxWzW|W~WWWWWWWWXXYYYYZZZZZZ$YYZZZZZZZZZZZZZZZZZZZZZZZZZ"[#[$[J[K[t[V\X\\P]R]T]]]]]f^h^j^^~{xuwxyL%&'()*+,-./012345678 =>.ZZZZZZZZZZZZZZZZZZ#[$[J[K[\\\\\\\\T]Ŷުޓމ{p{dWp{jkCJEHOJQJUjyD CJUVmHjCJOJQJU CJOJQJ CJOJQJ5>*CJ OJQJ5CJ OJQJji5CJEHOJQJUjtD CJUVmHjf5CJEHOJQJUjJD CJUVmHj5CJOJQJU5CJOJQJ jCJOJQJ CJOJQJCJH*OJQJZZZZZZZZZZZZZZZZZZZZ"[#[$[J[K[t[V\X\\P]$P]R]T]]]]]f^h^j^^^2_4_____$`&`V`X`Z`\`^```b`d`f``$T]Z]\]]]]]]]]]]]]f^j^^^^^4_6_8___________"`&`(`N`Ǹwk^wjpCJEHOJQJUjD CJUVmHjCJOJQJUCJH*OJQJ6CJOJQJ CJOJQJ5CJOJQJ5>*CJ OJQJOJQJ5CJ OJQJjm5CJEHOJQJUjD CJUVmHj5CJOJQJUj5CJOJQJUmH5CJOJQJ5CJOJQJ$^^2_4_____$`&`V`X`Z`\`^```b`d`f```aaaaaaaaaaaaaaaaaaaaaaaab~{xu LMyz{|}~JK.N`P`R`T`f``````>aDaFaaaaaaabb b+bbbbbbcc d ddd3e4eϽ׬פ{skc[kc[>*CJOJQJ5CJOJQJCJH*OJQJ5CJOJQJ >*OJQJjv5CJEHOJQJUj̮D CJUVmH5CJOJQJ5CJOJQJ CJOJQJ6CJH*OJQJ6CJOJQJ5>*CJ OJQJ5CJ OJQJj5CJOJQJUjs5CJEHOJQJUjED CJUVmH#``aaaaaaaaaaaaaaaaaaaaaaaabbb$bbbbbbbbb b)b*b+bbbcc4e5e6eeeeLfMfNfwgxgyg$h&h(hĿytoe`[3J  45^J  _`J  vJ  wxJ   J bbbbbbb b)b*b+bbbcc4e5e6eeeeLfMfNfwgxgyg$h$h$ & FJ$$4e6eFeGeeeeeKfLfNfPfXfvgwgyggghh"h$h(hFhhh iiiiiiiiiiiii$j&j(j*j,j0j:j@jHjJjLj|j~jjjjjjjjŻ򞖞ܭ򞓞CJCJH*OJQJ CJOJQJCJH*OJQJ6CJOJQJ >*OJQJ5CJH*OJQJ56CJOJQJ5CJOJQJOJQJ>*CJOJQJ5CJOJQJ 5OJQJ CJOJQJ >*OJQJ:$h&h(h iiiiiiii&j(j~jjjjjjTkkk@lBlrl$ & FQ$$h$ & FL$ & FL$ & FJ$(h iiiiiiii&j(j~jjjjjjTkkk@lBlrltlvlJmLmNmPmmmĽ|upkfa\WRM+,-. Q )]L  ^_L  L   L KJ  LMJ  jjjjjjjjjj.k0kRkTkfkpkrktkkkkkkkkkkkkkkkkkkkk llll&l,l6l8l*OJQJ >*OJQJCJH*OJQJCJH*OJQJjyCJEHOJQJUj\D 5CJUVjCJOJQJU CJOJQJ>*CJOJQJ CJOJQJ?rltlvlJmLmNmPmmmmRnjnlnnn&ofoooooo(p~ppp$ & FQ$$h$ & FL$ & FL$mmmmmnnnnnn nPnRnTnVnZn^nbndnfnhnlnnnnnnnnoo$o&o8oBoDoFoToZobodofoooooooooooooooooѾساؔ >*OJQJj|CJEHOJQJUj\D 5CJUVjCJOJQJUCJ>*CJOJQJCJH*OJQJ CJOJQJ CJOJQJCJH*OJQJ6CJOJQJOJQJOJQJ H*OJQJ:mmRnjnlnnn&ofoooooo(p~pppppRqqqqrrrrĿ~yoje`VQLGQ   *^L  _L  L  L  Q   @tL  uL  L  L  oooo p"p$p&p(p*p,p0p:p@pHpJpLp|p~ppppppppppppppppp,q.qPqRqdqnqpqrqqqqqqqqqqqqqqqqqһ˸˻ҭҡһjvCJEHOJQJUj\D 5CJUVjCJOJQJUCJ>*CJOJQJCJH*OJQJ CJOJQJ CJOJQJCJH*OJQJ6CJOJQJ H*OJQJOJQJOJQJ >*OJQJ:pppRqqqqrrrrrrrrrrr@sBsssfttt$ & FL$ & FQ$$$h$ & FLqqr r rrNrPr~rrrrrrrrrrrrrrrrrrrrrrss8s:ssBssssssssssssssttt t&t.t2t4t@tHtNtVtXt򫞷jGCJEHOJQJUjD 5CJUVjCJOJQJUCJCJH*OJQJ CJOJQJ6CJOJQJ >*OJQJ>*CJOJQJCJH*OJQJ CJOJQJ >*OJQJ>rrrrrrrr@sBsssftttttjvlvvvw6w8wwww6xxĺ|rmc^TOJL  7L  8DL  oL  p^_ wS x2L  3[L  gL  L  Xtdtxtttttttttttttttlvvvvvvvvvvvvvvvww w"w*w,w0w2w4w8wXwZwwwwwwwwwwxxxx x&x.x0x6xRx\x^x𱤽jCJEHOJQJUjD 5CJUVjCJOJQJUCJ6CJOJQJ>*CJOJQJ >*OJQJCJH*OJQJ CJOJQJ CJOJQJCJH*OJQJ@tttjvlvvvw6w8wwww6xxxxXyZy\y^y6z7z8z$ & FT$$h$ & FL$ & FL$$ & FS^x`xfxnxrxtxxxxxxxxxxxxxxxxxxxy yyVyXyZy8z{{{} } }i}}}~~~~ ~ ~~2~4~6~~~~~~̽šŐCJH*OJQJ CJOJQJ6CJH*OJQJ6CJOJQJ>*CJOJQJOJQJ CJOJQJ5CJOJQJ CJOJQJ5CJOJQJ>*CJOJQJ >*OJQJCJH*OJQJCJH*OJQJ CJOJQJ CJOJQJ5xxxXyZy\y^y6z7z8zzzzz[{\{{{{{{{{Z|[|\|]|^|}} }ŻxsnidZUPu"  vwxy " 789:;w!  x!  E!  F ! $%&' cT d8zzzzz[{\{{{{{{{{Z|[|\|]|^|}} } } } }c}d}$$ & F"$d$$ & F! } } } }c}d}e}f}g}h}i}}}} ~~~~~*(.Ŀzwtolifc[X&%  'Z %  , >% ? $ ;<=jklmnop"d}e}f}g}h}i}}}} ~~~~~*(.$$h & F%$ & F%$ & F$$~~~~~ "*bdf  DFLNXZ\ԁց(,. }us55CJOJQJ56CJOJQJ5CJH*OJQJ6CJH*OJQJ5CJOJQJOJQJjCJEHOJQJUj%6@ 5CJUVmHjCJOJQJU jCJOJQJCJH*OJQJ6CJH*OJQJ6CJOJQJ CJOJQJ CJOJQJ-  z|:<>BDFHJrt,. 8:>¸¸°ҝҝҝҝҝҝ 5>*CJ5CJ5CJH*OJQJj5UmH5>*OJQJ5CJH*OJQJ5CJOJQJ5CJOJQJ5CJOJQJ56CJOJQJ5CJOJQJ5CJH*OJQJ6CJH*OJQJ:FHrt.:<>@$ & F($ & F' & F&$ & F%$FHrt.:<>@אِؐzwtlifcVWXD(  EF(  ~(  (   P( QR}~'  '   M' Nc&  d y& z#>@BDFԊ֊XZ\ӐԐՐ֐אِؐ  >֒ؒ68`bh踫ϣϣCJH*OJQJCJH*OJQJjɋCJEHOJQJUj.@ CJUVmHjCJOJQJU CJOJQJ6CJH*OJQJ6CJOJQJ CJOJQJ5CJOJQJ5CJOJQJ=אِؐ() Jfg&d$ & F($() Jfg_RTVX\^`Нҝ֝x|yvs   ./~űƱDZɱʱ˱̱_`aO۳ݳ`aQR67mU(  -()efgΘИ1234opqMNO*,ƻơƕƻzodY6B*CJOJQJhB*CJH*OJQJhB*CJH*OJQJh5B*CJOJQJhj+5B*EHOJQJUhj݄@ CJUVmHj5B*OJQJUh56B*H*OJQJh56B*OJQJh5B*OJQJhB*CJOJQJhB*CJOJQJhB*CJOJQJhB*OJQJh5>*B*OJQJh"_RTVX\^`Нҝ֝x$&d,<>HJLXZ\`؜ڜܜĝƝȝ̝Νҝԝ̞֝ΞОҞԞ؞ڞܞ*+;ͳͳ}}p56B*CJOJQJhB*H*OJQJhB*OJQJhjB*OJQJUmH5B*CJH*OJQJh5B*CJOJQJh5B*CJOJQJhjB*CJOJQJUmHB*OJQJhB*CJH*OJQJhB*CJH*OJQJh6B*CJOJQJhB*CJOJQJh,ufhƦȦʦ̦:<=@$%~re^^ jUmHjCJEHOJQJUj@,@ CJUVmHjCJEHOJQJUj+@ CJUVmHjCJOJQJUCJH*OJQJCJH*OJQJ CJOJQJ6CJOJQJ CJOJQJjCJOJQJUmH>*CJOJQJ5CJOJQJ6B*OJQJhB*OJQJhB*CJOJQJh$;<>?$ & F)$$;<>?@$:;@|yvs:;<=>?@ABCDѨ:; ) =>?@ABCDEFGHIJKLMN-?@$:;@ <$ & F+$ & F*$%@ ?@$&,2FHNTfhntЬҬfqï˯yƼƼƼƼ洬5CJ5CJ 5>*OJQJ5CJOJQJ6CJH*OJQJ6CJOJQJOJQJ CJOJQJOJQJ jUmH CJOJQJ56CJOJQJ5CJOJQJ; <^ïįůƯǯȯɯʯ˯ƾ~{xurolifӢԢ*  )*  *T*  UVh+  +  +   +  * 9'<^ïįůƯǯȯɯʯ˯$$h$ & F*$$ & F+>@BCDprlBC$ & F4$ $ & F3$ & F3$>?@ADv  AC/HTUY\ (,NPRҸ뽅6CJH*OJQJ CJOJQJCJH*OJQJ CJOJQJ>*CJOJQJOJQJ5CJOJQJCJ6CJOJQJCJH*OJQJ CJOJQJ jUmHOJQJjOJQJUmH CJOJQJjCJOJQJUmH3>@BCDprlBCζ϶UVYZ[\|yvspmjgb]Xno5  5  5   5 3  ?4  4  Ÿ4   4 13  24p3  qrtv3   3 "ζ϶UVYZ[\(*,v .^`$ & F%$ & F$$$ & F5$(*,v .^`%(ټTVĿvqiaYVSƗ5'  |'  Ø'  Ę٘&  ژ&  %   eh]%  ^v%  Ҝw$  xy ҸԸָظܸ "$bdfhlnv .0VXZ\^%'(ټA鴧אא556CJOJQJ5CJH*OJQJ5CJOJQJOJQJjCJEHOJQJUjD 5CJUVmHjCJOJQJU jCJOJQJ6CJH*OJQJ6CJOJQJ CJOJQJ CJOJQJCJH*OJQJ4`%(ټTVPQ\$$ & F' & F&$ & F%$$h & F%ABҿԿTVJLNPRTdfTVXZ\^npĻCJ 5CJ 56>*OJQJ5>*OJQJ 5>*CJ5CJ5CJH*OJQJj5UmH5>*OJQJ5CJOJQJ5CJH*OJQJ5CJOJQJ5CJOJQJ5CJOJQJ56CJOJQJ5PQ\]HIKL<=>¿}uroliVƎ/  ǎȎ/  Z/  []^`w/  xyp/  qru/  v / ėŗ'5=\]HIxckKLbiOW5CJH*OJQJ CJOJQJ jUmH j5CJOJQJ56CJH*OJQJ56CJOJQJ5CJOJQJ6CJOJQJ CJOJQJF\]HIKL$h$ & F/$Xf<>8:BCTU9:г쾤쾤쾤쾤왯6 jUmHCJj5CJUmH56CJH*OJQJ5CJ>*CJOJQJCJ56CJOJQJ6CJOJQJ CJOJQJ j5CJOJQJ5CJH*OJQJ5CJOJQJ j5CJOJQJ6<=>89:BC     $$ & F/$$h89:BC     TU9:½|rolb_Y _- `,  ,   , -./0123456789:;U/  "TU9: $&B$h$ & F,$ & F- & F,$$h$ & F,$$  $&jl&*RTZx|+6Ay}:дЬ5CJOJQJ5CJH*OJQJ5>*CJOJQJ5CJ OJQJ5CJOJQJOJQJ jUmH6CJOJQJ CJOJQJ CJOJQJ66H*A $&Bfghlo  467¿|yvspmjgda^  ,  ,  .  Ά.  ц.   Ն. ֆ׆J,  KsŇ*,  +8-  F-  S-  $fghlo  4678$ & F.$ & F,$789:;<=>?@Ayz{|}68: `bd*,.02468:~{xu}~56\]^   -89:;<=>?@Ayz{|}68: `bd$$djlrt|~(),4,.02>ʶʫңңңңҜҌ 5OJQJ5CJOJQJCJH*OJQJ CJOJQJ6CJOJQJ j5CJOJQJ5>*CJOJQJ56CJOJQJ5CJOJQJ CJOJQJ5CJH*OJQJ j5CJOJQJ5CJOJQJ5CJH*OJQJ2*,.02468:<>!"#$$:<>!"# a*+,-npqr %&'`Ž}zwtqnkhabRSTo  LMNO1  1  1  o1  1   1 VWX'@ALM`abcpqr    !"EFYZ˼xmaj_D CJUVmHjCJOJQJUj5CJEHOJQJUjTD CJUVmH6CJH*OJQJ6CJOJQJCJH*OJQJCJH*OJQJ CJOJQJj 5CJEHOJQJUj&D CJUVmHj5CJOJQJU5CJH*OJQJ5CJOJQJ5CJH*OJQJ# a*+,-npqr %&'`,$$ & F1Z[\`abcde&'Ƽణ}qb}VjD CJUVmHjâ6CJEHOJQJUj״D CJUVmHj6CJOJQJUjCJEHOJQJUjD CJUVmHjCJEHOJQJUjxD CJUVmH6CJH*OJQJ6CJH*OJQJ6CJOJQJ5CJOJQJ CJOJQJjCJOJQJUjCJEHOJQJU '()*+-nopr !"#$%'`atuvx߼߰߼ߕzkcYcY5CJH*OJQJ5CJOJQJjë5CJEHOJQJUj`D CJUVmHj[5CJEHOJQJUj|@ CJUVmHj5CJEHOJQJUj,D CJUVmHj5CJOJQJU jUmH CJOJQJ6CJOJQJ5CJOJQJjCJOJQJUjCJEHOJQJU"$&(*^` !"#$?@STUVij}~ôxl]jǷ5CJEHOJQJUjѵD CJUVmHj5CJEHOJQJUjk!@ CJUVmHj35CJEHOJQJUjD CJUVmH 5OJQJj5CJEHOJQJUjD CJUVmHjǮ5CJEHOJQJUjD CJUVmHj5CJOJQJU5CJOJQJ#,.%&WXYdefgh%Ž|yvspmj$%M ] YZ[\]2  ?2  @h2  ij2   2 *+`(,.%&WXYdefgh$$ & F2$htwPQRUϾšφ|t|Ͼlhhbh 56CJ5CJ>*CJOJQJ5CJOJQJ5CJH*OJQJ5CJH*OJQJ H*OJQJOJQJ 5OJQJ56CJH*OJQJ6CJH*OJQJ6CJOJQJ CJOJQJ56CJOJQJ5CJOJQJj5CJOJQJUj5CJEHOJQJUj絞D CJUVmH&%Qlmn:$ & F6$&d$$$&d&d$Qlmn:<\N Ľ|wrmhc^Y]Z6  [()j 6 "89# n:RTV46RTnp BDF\h$&xzûûҫҫҗҏҏCJH*OJQJ jCJOJQJ6CJH*OJQJ6CJOJQJ5CJOJQJCJH*OJQJ CJOJQJCJH*OJQJ CJOJQJ 56CJ5CJH*OJQJ5CJOJQJ5CJOJQJ5CJCJ8:<\N !"#$%&'$ & F6$h$h&d$ !"#$%&'defZwp)*1Ŀzpka\U ; +9  ,1:  5:  ;:   ?: @ f9 gk~    'defYZw #BD_bbdjPQT<=>z{|ĵĵĵIJĽĵjCJOJQJUmHCJ6CJOJQJCJ 5CJ CJOJQJ5CJOJQJ56CJH*OJQJ56CJOJQJCJ5CJOJQJ>*CJOJQJ5CJ?'defZwp)*16=C$ & F;$ & F:$ & F9$&d$$16=CDhiot{ ǽzpka\UKA,>  1>   6> 7t9  uz=  =  =   = 9  <  <  <   < 9  ;  ;  ;  CDhiot{  R$h$ & F>$ & F=$ & F<$ & F9$ RHDEHLPTU{|ɿzpf\WMH9  @  @  @   @ 9  -?  ;?  I?   Z? [9  <!9  "'>  RHDEHLPTU{|$ & FA$ & F@$ & F?$ & F9$$h|    <=>z{~{xpmebZWB  B   B    B   !"#$%&'A  A  A   A      <=>z{$ & FB$$$h$np|9 T _ b        e f y z { |          > r      24BDj꬟×5>*CJOJQJ5>*OJQJjCJEHOJQJUj6@ CJUVmHjCJOJQJU5CJOJQJ5CJOJQJCJCJ jCJ UmH6CJOJQJ CJOJQJCJH*OJQJ CJOJQJ6%cd_ ` a b                 24ƾ}zurojga F EGHklmNO)*+\]^_a9:;<ND  _D  mD   zD {B  C  IC   C &%cd_ ` a b          $$ & FD$ & FB$$ & FC        24)h  $%Z$h$$ & FF$jlnpt()KL_`abhiwxŸѱѝш~qdjCJOJQJUmHjYCJEHOJQJUjD CJUV6CJOJQJj$CJEHOJQJUjD CJUVmHCJH*OJQJ CJOJQJjCJEHOJQJUjb:@ CJUVmH CJOJQJCJ jCJOJQJUj̾CJEHOJQJUjD CJUVmH')h  $%Z\HI#8GHIJKLMNOPQRS-./ $)*+,59=>?@F F  F  PF  F  U %:;NOPQZ[\ *,RǺǮǕǕǕǃ{s{i{i{i{\{j5CJOJQJU5CJH*OJQJ>*CJOJQJ5CJOJQJ5CJOJQJ5CJH*OJQJ56CJOJQJjI5CJEHOJQJUj⺟D 5CJUVj5CJOJQJU5CJOJQJ CJOJQJ CJOJQJjCJOJQJUjCJEHOJQJUjظD CJUV!RTVXpr01DEFG³ХЈyk\PjD 5CJUVj5CJEHOJQJUjB@ 5CJUVmHj]5CJEHOJQJUjA@ 5CJUVmHj5CJEHOJQJUjvA@ 5CJUVmHj5CJEHOJQJUjA@ 5CJUVmH5CJOJQJj5CJOJQJUj=5CJEHOJQJUj{D 5CJUVZ\HI#8GHIJKLMNO$h&d$h  ~Sgxܨܨܨܗܗ5B*OJQJhB*OJQJh CJOJQJ CJOJQJ5CJH*OJQJ5CJOJQJj56CJEHOJQJUjD@ 5CJUVmHj56CJOJQJU5CJOJQJj5CJOJQJUjp5CJEHOJQJU2OPQRS-./$$$$hh$h $)*+,59=>?@@LPD$$$C$$r @FJNOPQW[_`abcdefghiD$$hC$$r $$$FJNOPQW[_`abcdefghi  #)*+tu [ bT`C$$r $$$$h  `TP`$$$C$$r  )*4sw34Bwz{諞蒅jCJEHOJQJUj๟D 5CJUVjCJEHOJQJUj @ 5CJUVmHjCJOJQJUjCJOJQJUmH CJOJQJ5CJOJQJ5CJOJQJ CJOJQJ5B*OJQJhB*OJQJh4#)*+tu [$$hC$$r $$$T`C$$r $$$$h #(-34567yz|}~  $ % & ' ( ) / 0 1 2 7 9 = @ F G H N T Z [ \ ] f l q r s t z                              b `TP`$C$$r $$#(-34567yz|}~  $ $hC$$r $$$    ' ) / 2 9 @ F Z [ q r             !""v"x"z""""""""/$0$_$ུ|t>*CJOJQJjCJEHOJQJUj-@ 5CJUVmH5CJOJQJjCJOJQJUmH5CJOJQJ5B*OJQJhB*OJQJhj=CJEHOJQJUj @ 5CJUVmH CJOJQJjCJOJQJUjCJEHOJQJU-$ % & ' ( ) / 0 1 2 7 9 = @ F G H N T Z [ TTC$$r $$$$h[ \ ] f l q r s t z            \TT,$$$C$$r                   '!!$hC$$r $$$$$ '!!!!"""##.$/$0$_$$$ %%%%%%% & &N&O&P&Q&S&T&U&V&W&&&&&&&&['\']'|'}'''''''''''''''''(((((r(s(((1)U1V1 G I!!!"""##.$/$0$_$$$ %%%%%%% & &N&O&P&Q&S&$ $ & FG h$h_$`$s$t$u$w$$$$$$$$$$$$$$$$ %'%(%)%Q%R%%%%%%%%%%ķ픊픊|ocjdD 5CJUVjCJEHOJQJUj @ 5CJUVmH5CJH*OJQJ5CJOJQJjCJEHOJQJUjA@ 5CJUVmHj2CJEHOJQJUj"@ 5CJUVmHjCJEHOJQJUj^@ 5CJUVmH CJOJQJjCJOJQJU#%%%%%%%%%&&& & & &&M&Q&W&&&&&&&&&&&&&&๬xmx`RjLD 5CJUVmHj5CJOJQJU j5CJOJQJ5CJH*OJQJ56CJOJQJ5CJOJQJ>*CJOJQJ5CJOJQJ6CJOJQJjCJEHOJQJUjD 5CJUVjCJEHOJQJUj@ 5CJUVmH CJOJQJjCJOJQJUjjCJEHOJQJUS&T&U&V&W&&&&&&&&['\']'|'}''''''''''ܔܔ$$l  ,"$$$&&&&&''''B'C''''''''''''((2(3(F(G(H(I(s(t(((((1)2)H)I)οܵܫܫܥܥܥܥΖ܈ythjv@ CJUVmH jUjo5CJEH0OJQJUjSD 5CJUVmHj5CJEHOJQJU 5OJQJ5CJH*OJQJ56CJOJQJj5CJEHOJQJUjND 5CJUVmH5CJOJQJj5CJOJQJUjw5CJEHOJQJU'''''''''(((((r(s(((1)L)M)h)i)))))ߌ߀$$$l  ,"$$I)J)K)M)N)d)e)f)g)i)j))))))))))))))))))))))))))))))) * *****(*)***+*½ j/Ujw@ CJUVmH j'Ujw@ UV j"Ujw@ UV j!Ujw@ CJUVmH jUjv@ UV jUjRv@ UV j Uj4v@ CJUVmH j Uj(v@ UV jU jU2)))))))**,*-*I*J*b*c*{*|***********++*++*-*.*E*F*G*H*J*K*^*_*`*a*c*d*w*x*y*z*|*}***************************yj탖D CJUV jEHlUjD CJUV jEHUjD CJUV jEHUjiD CJUV jEHUjHD CJUV jEHUjD CJUV j!EHUj逖D CJUV jd9Uj#x@ CJUVmH jU.***** ++++++&+'+(+)+++,+?+@+A+B+D+E+X+Y+Z+[+]+^+t+u+v+w+y+z++++++++++++++ j=&EHUj 8 UVmH j|UjQ!@ CJUVmH jUj@ UV jTEHUjD CJUV j EHUjD CJUV jN EHUjD CJUV jEHUj_D CJUV jU jlEHU/*+++C+D+\+]+x+y+++++++++,,,,7,8,P,Q,i,j,,,,,++++++++++++,,,,,,,,,,, ,3,4,5,6,8,9,L,M,N,O,Q,R,e,f,g,h,j,k,~,,sj,@ CJUVmH j@EHUj!@ CJUVmH jV=EHUj @ CJUVmH j;EHUj @ CJUVmH jG9EHUjT@ CJUVmH j4UjJ݀@ CJUVmH j 3Uj78 UVmH jU j)Uj8 UVmH+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,----------.-/-0-üwp jaUEHUjb@ CJUVmH jREHUj@ CJUVmH j PEHUj@ CJUVmH jMEHUj@ CJUVmH jKEHUj@ CJUVmH jHEHUj,@ CJUVmH j.FEHUj6D CJUV jU jBEHU,,,,,,,,,---1-2-J-K-c-d-|-}-----------.0-2-3-F-G-H-I-K-L-_-`-a-b-d-e-x-y-z-{-}-~--------------------------|u jkEHUj҃@ CJUVmH j hEHUj:@ CJUVmH j'eEHUjL9@ CJUVmH jbEHUj2@ CJUVmH j_EHUj+@ CJUVmH jZEHUjJ(@ CJUVmH jXEHUj"@ CJUVmH jU-------......'.(.).*.,.-.@.A.B.C.E.F.Y.Z.[.\.^._.r.s.t.u.w.x........~w jZEHUj-8 UVmH jrEHUj@ CJUVmH jb}EHUj @ CJUVmH jyEHUj{D CJUVmH j"uEHUjyD CJUVmH jqEHUj xD CJUVmH jU j$nEHUjyރ@ CJUVmH*..+.,.D.E.].^.v.w........... / /%/&/>/?/W/X/p/q/......................../ / / / //!/"/#/$/&/'/:/;/0V0W0o0p00000000000 0 0 0 0 0!0"0#0%0&090:0;0<0>0?0R0S0T0U0W0X0k0l0m0n0p0q00000000000000000000x jEHUj8 UVmH jEHUj`'@ CJUVmH jEHUjJt8 UVmH jEHtUj(@ CJUVmH jEHUjjo8 UVmH jȶEHUjo8 UVmH jfEHUji8 UVmH jU jEHU/0000000000001111 1 1111 1"1#161718191;1<1O1P1Q1R1U1V15CJOJQJ jEHUjD CJUV jEHUjD CJUV jEHUj=@ CJUVmH jkUj8 UVmH jEHUjr8 UVmH jU j%EHUj8 UVmH#001 1!1"1:1;1S1T1U1V1$ / =!"#$v%`!_i NA꧖9J N`-xnE𙱛P%BT[ N ǻx]) B ^Io o#R{MߠnEJٝ||;B 9ʿαQWL&TeWU|$'ɘ, 'oy 'q_(i-}~]={Lꏰs~O_3)5 Z j-yf0Z3&hѪv)TaODZERgI~h*n͗Wߢ:0g?.f EIڠr)erh ;ua{YUo-f__ JFa*Fk3K:sXf%A.P+M:-=ˎ%$ TgJ@kcV]ea?TqGrA2_UStgCF s:gl{f._;lFDG~0d%YHMOYU~oB1BsUUq},0Cr-{]Śp W OH!\n$>GG㔦 /2P•eK:G2W( t Q)Q'3І+1+KsKWϩ/F(RRvZZ5DF;ۀ8,1jO@%MJ׉1e[VIH:쓇'rfpc%X\[it[vKWGDݥ޽433^:7:gD ̲;.={N{(kuݕm oؽFI9|H-CzcБ:wǎ9O|=g4LH3ֿV'sSO3'ŷ9/fI~'NSNsjPMPMjcUC| 556@mXՆAm[նvAZծA[վ@XՁAZա@YՑN@XՉNAZթ&&V51T+U6)EPVh6U 3T*P֭jP[jP;vjPcPc1VPO@='zU=7KP/ꥡ^zmU zcY,YrV5@5aU յRPUZfUkiUzU=4=3BZMܞY55A[ձ1ąmJ=jԪUj jlUcC:cC͓Z/))m'f!7`! M]JFc &^l84xYME~gwfd[6d9fԋY&!}>߆agWN;}vď>0ׄ-1O[곗z ¤FF-7`䏛w\o{$δ(?=5`P_^:~כ\+Z9ºcj;nqO$xR%-k+Ν_yV~P:4``di5Ǿ5}0ɬխLVf NK򪖃~νTaD9LFOnSs?ǐU˖#&Y6&.s -*TׂhSqEe$ޣ`kTQisߚ߅]dܡ!I7#&h/0oF{cB}G Q˼S vfp;ظyߍ|i`!(w/9c3(xMhAL6M?VPAh!mŃX) ("T=P/Hoz^C@ ۙݬYc`oy7of6a yIˍĸ8`Ib{Ǒ>]g )"?v%{g?0P_E-uZBu /?٩ (5hU|䕁9#AJ^s@mД>\ُ۶[PF&'#vܸŅk7yV}-3Ώ,/ݞiK6jϨkM06] qnj%BlXX&Mb}YNǘn5a\C p"Xk'SW5 1Ogw6*gݜ3 q&!Ls$S>#ߑLLq:G?xz?K.dnٚnzFz}e+\͠^'Ҝ_\_Uf0с> h'qwb-Ʊ5Ο;n{s}܏㚶ki/83O Rɦ`!PdJx󫎁Q?,@BAxN@g'@č ́S%ތ<Uz@P$>6(c\ +`ި7dsbg=-[Vֳx;*@XUWY(yP0& ,KUiDM6 9{+yN>+O&~knZqom t'\W<ݏu1I|.~5܏؛%;bF[q#c-- -eZZv-e6mܭ$orgJs?&<Òj5vfj XMsVq:'ҡ3Ƥ"٬cU\Q/qb.WJl\K~{`XYӮsS-\q~=3dww$.K|j\xrͮq)+5q ?Ƨ_hT^ٽ4DmmEIjZחϒm`!(% G6!L~0  0^qqx lTE9}ۖ#,B04 vKy116J"P" ZtJPD"RJ~k`2&:ʎMq!`jmIr9n5-U 8v $fN|nMRȎuq!0^K5CX Q-*gرJ䬌P@*8v'93DuiQwhE7%GKG7CujG騇+j׵T1ot$c=DCT1@u Q -U f !xc!qjBCTqj>WQ:8JCT}8 93D=vRus'l! :@HG<͑~R貖x&#~3DFUZ*AR bjWGU ytc 18hT-ޒ1ճEKV=S QF c20DUDA-UfZٴ CT+VG*T:-" c q\T-|ڔ= 'j7CĦqQ31Llttn)"JcQ"0g^狧*6b ? :jG7S֏; i0!J)Jmk\Z visioZK˶!.-wi5`iC]Z c5kc!!Md|ȯ(YT/OeY:z|1ut|Y:*0-Y1^ ;1Yt_4SQ޼iEӋt ̙%ʼnl$K'ڎX33 ŝ!2SMh2TwgO775L07vaM6b}tnwaQGe6 yJ?Uղ"G{V!*b["3MiA~h_9W޽>Znos'.}`!=')|$Q .@8HxڥKhAgg7MWVTۭEEJA<@QVɡx ^HlBiƝR_a773}# B29T4Q$%bH[4W y~nvGVUP8#~u98\ kה:Ԋe%cECq:ÕBNk@)k\ε3 }?lLi1I5bTõZ+f]o+\ƪ=/Z䌂fCQh3E~4mc""o7E*)az]uW-8W; l_;6㡵<g4D C.o0?ATřGdcɖ6wxhKy wy π>2DHX&*k r,$r 3*$`|<ͅ\GT#ljXp5z{>!ho6{m)o r4s |"y"`|VΕ"'3%gq-ǯ(z:yL˿?.ߊ.c|Xfp Z՚7PD1֢Mܺ5m[(Ld?(.DP| \7Q\Psߪ۾Uo37!E&n0r-JM>(+-6q7?|7JMQvA'锴ʝt[K=}:ww\ ^=!tм;6i8SKէ+H{=%&Lڔ=,Ze{@c˦|g oٟ3m{rRΚ=|yo81}tsY2m=z'W^šId-q~$Λ"\Uپea}ꛡދ3? K$&/|u\'<~#[%M5l5OTV?C-`!c_ 4<4]`ZyxYklTE;wfn) RBS[YHPP%j4Z(-h~%`(FӠQ& G $B$MHФΙ;sw޽h􇻹;s;̙3{fWA&Bj;E(& xebS ^Hy'G \ ge戱/ HfhkZ koO&% Z|D.u;V0ZlY&y;IPK1h f]]]Kr kǩatMZ[̌ Ko-b:۵ X,1a.^#4~r܉"y{E q:*|'^m${S/3Vjt/n@/5 tq\nvXu]{0@':Se VQ= t-2v Ardv es@dEG@/ ,:­2*Ӆftc>qp4{f,=*zqyztӋ}:cE/z.WYO~KFY[g}@{rI[g JTg G#.vf^[$CZnk@x1pП@\PDy_f?Xк"FSݠ%,>#9Z$wǶVq&d^9vv\n@sI7SZ7=AޝcA96sR׏4a+K/V;iV[YcEވ6瑹3T,i3\vm_T#c}d/vdoTwwId E%}\=OS'Ho*E./#yVҤv~DV;O@>@Jc>}LMTyGx&=L+\4wfq1&? rP-"m ej& ]@i K(ިdF8AQI_oD|VGkSd 7H=9;=AJyJ\Z95%d.yT(XARU) [u nѫzsfiVkFT^ª_*DIcU Fldzd~6A8>yA[#B=wx x?(X- Y& (5A dsC¾}Gd mm︠VۺCBAY&wO䤙7>g|?̙y}yy#E {[Ξ精{ϫO{h jB_='<}5:ֱG]>B_-+cǎyE?D/XٯkF] WpjG_t:՛_րY',{9"Wf Hy4dGC:nh1FhD_}5шA=f Q` <Եߗ_1ԣ1{h@Ckң 7y쾂ћF}#@.4*zsՔU\50>O0Ш` |4c \5s,8Ẉg>4/+H:W)|RsB)0_0Ҩ` >W1F@uR\*Sa8"Gq]Ҍ4*zs*-8Wit F?],*ϕf QC|97y?. u՜ϕf QCt>WJtE}`\DKq]Ҍ4*zs*#8Wt󸈳/ht` >W1F@u2\e*3asq1. uJ3ҨU\eqξL0.e\i@=zq΅{E q yh` |4c \s28W-%R h` |4c \s*8W2ξ\0.傁ZҌ4*zsu2su2 󸌳 2#=l:Άy @@xfցGk:n 󸜳 r£ { hzoiFrxK+zgF}rxK^r/f p8Ξ h4<2KhpD100s&y&sP0/khc<&sъ=t9FÀ}xΞ(i4P/9h(ٓc&ZفG6 =I0&hU=` 8{`L$`_;KCD!v"gO4 kgx~v3h4Njm{a3F}\/wya1FEC}X|-U}T/?h*(QDS8gțBE@0hG){)4 죊8{`hR=MQ9{`̧`A63ǙJ>j.g \(5|β=ޞMǸ>Jci4=/ fhX%{뼰7XH?7' {4sz7XH?o?{uB-sMq=nʣ {}!8{`@Cy4e]/"`~#g/i(f4Zołq7XHEQ9{`,`j1g/4Z|`_<9]ξO0}Vy^N/9ѝ^ξO0}v/k=4 ƽ4*T=4 ƽ4%{;缍Fw8~FZќ=tyqRFK}HgsF;}}T0R`_{5ožVk~=kG&3ty+ŌEǸ~yPBb6/nm>j)g?(Ki TCQ6`4z%[F8!xFAفG6>gg?$!`?=#4z> 4z*&^r^trrxFˁ}2/k>=2= kZK΋9`_+y#K΋92`_Wr#=t0圽B0hUCh]+c9VZ|Fˀ}r^!i* =4Zks XNVydk2`_Wr>Ug~ {iyB0 `syh?shy\=O@uT,ѣ}'9xF=}Ghp)^.Oh9pDyǽp=gxih*goWiw zsv`ӨC[u4Zܿz7 k4܇;cv=9D0iTPьCíz>kQ0^F~⛜S0ޤNG1gF1Jj>Rx>~:'5m;Mzsv`Ө#Cz$h wvzsv`ӨCu4Zٛc6sN@b.b=|^OF8{`l&~v!iQ%QLϾ h=p}#go4 |nm4zQ%QL `l͂F?qFo=9D0iTPz󏍜Y06h396.hQ%QL,gl-F[ϣ=9D0iTX9Fϣ6qF[ϣ=9DӨ%χ~Fϣ6qD-jop6xFۀŜ]"4*zh󡟫zs͜U06h+뜽M0^6G1gF1Jj>Nh=f*iu&hУKF%@5^r>==t\~C0 b.b=ԹjJ?^Gυo7c >w.(Q C6| ;o7c >?/(Q ]/?SK{kQ)pW1>b'Qp?m/7Mwppv`|@2>hL-M=R.RԹJgޓFe}5 Ш Ϡ!wC2`\za?6]&4*oqR)RAK΋?ޢFe>Jyb]Eu]&Ш (Uz-ֻ23ʀul/g{>Ϧc\[ F)>ֻʣ53]'M?RP0Ji!U뇪8C(ч:qyaFuǜ`|LOP)kѽ4_9+>ֻ#=tFG`|DOPi|tFֻќ}t-F]P5>/Ehbd0Cע{i\M12kѽ4|Yu^։{9gK}:Q1Z2CW{iX_)F+fg/+8.K}D10W(Fkf|/sh >zK /Y4:yhȌ!>(`4c13t\b4aFфqVh01ьF3:WsW9։#=t}Fg`|F/uHe]_`})g.s`9"o?rgqFw xaoFr)>ʣ{c?O9sFw{4:hRxd1hpWyfݫ`|N/hO~ J0Wd{`~KǼ>Dc[/Y]~F>K֠F1qFwGs>gF]˳W~%koF]5NǸzWyd0Cע_(]dFfȤcbd1#+`d1Nsy_`-2`c\}U>[`}V<W_)U|煵74X_lhJC63=~d1RFku!5quU+|5w4X{ǵpߐqb4bFшqb4fFјqb4aFфq_b4eFӀєq ~>u ~3:ՉŒBǸ::Z\5\-j:UE}Uj5OmQԢۇ5OmQԢۇ5OmQԢۇSh0Z1nJ1Z2ehIǸZ\uI-IjZ\+5C-j uDPϦc~I-ꈚ u|~:Gmk~:3dgmv߀q]#E]A5I{`]).P5 .z$S%Tdv}`S>y<ɠ'=?o=wW\We*jL`}u gwS(@nˠ-} P~`a3F։pv8;dp )j C3}svhOn׹͠Z7XSfp-}3T=E3n/j 3n+j 35uә 3EMAt&p?1kLQSgP=35uә>n?1kLQSgP=35uә} 3EMg1n.kLQSgP= ܇Z:STOg}^csZCl.p=?; Ʃ+Zgsy',(3p=56W9Y0:R~gzkl': Q9v_+ZgsW9抵6\Ub͡u6klXksh_+ZgsW9抵6\Ub͡u6klXksh_+?qW9抵6\Ub͡u6_}|?ܿ1nJ12ahHǸ+hČFV5C $|Z\xkm<|:kxkm6|:kxkm6|:kxkm6|:kxkm6|:kxkm6|tkxkm6|:ku<~Mky,^7.Yv\\?Si \?qX?qp8{ v㵩X^ٽ\]]i \{rv/I}mW^c+݀}mW^c+݀}mW^c+݀}mW^c+݀}mW^c+݁}mO%=)p=xww;p={  ؟wZ,Q3tz,`~>g)'.HOQtW.9j u\'꒳&9Xz2Q2`]ҏ.G5`]җE]җjˁuI u(( .õB_Q3z/. r/%V#jT/%9@0R~.HKzSMRKzs=R T\7$7#}D]қj>7 }DЛ>}zއ| g@/%}x/>8{` / EϦcs~\+\.j~T/\|nПkDП|s\+5PFZaS0|0 cD01΃x"A8{`1dz>|4g h/%x,Ax&Ay%Cy#󡴖#y/HZ ^ z>Bz>bD`z> c Ak Zgќ]()u|Xt[:>Fi-\χ:>JCi-E_|4v9s=߳[_Y4*?Vs9ܭ{+YǨsO~.`~>`ߐqwb4bFшqwϜß=ߘq?{yU~s!/M'/ɟX?ߔq?"׍D"Վ{N+n\%ǕT;9qWR 紒kU[Iu*`mUZI5*`mUZI5*^J~[%ꮕ^ ~Ɍ̀IǸRUZIﻫX*Qwk+Z%ꮕTs>{Yɵ*Qzh&Zu*QZe&Zu*Qb%)DjUg+NY%ꕕTq,qA ^L+\+l5+T/l~p- EͰ]õ+fXC+%[c#o%Y+T%9{`l-]5Qg pl3go͔X_jVSmX_m쭂WsFp~[[c o։>\#T#։9{`l\jC7sVL[s_u_ֻ9{`l̵\Ko5Z72pVB[u+\ou+To[8{`l=ul: 6dpVB[f;c3[j;ֈc5kf") poy56kD5ME06Q`WDzW}F,)3poy hEϦcZ6^m-i{G zuԧmqϊqնQ.pwh^miwqնQ.pwO>jPyG8oSz=xz=c7o`7o\-T K~K%TOK-uwbAwZzwׇ%NE5b Nŵ[vQVwq]U"]T[뫝\Wj'Vj'<ŢIuO1HKvRMR KvpK ;^KvpK ;l:ZavP k]fA.[ʵBJ^(rP&jRʀpP&j^(ϵBާz  efx2{\+=ʀ\+2~b) ef(z XʵBJ^(ÕrP&jRʀp\+2U) ef(z UʵBJ^(rP&jRʀ>\+2gJV(5C) eRDPJBpk2Q3RPg(ZL ~6JV(5C) e}RDPJB}ϟ}~ϟ}L_Y4*?FDXk:n(aqZ Q1j'Zw#\c|/j#Tg|ܟ81D"dhBǸz01GE=sjzF͵>)!DS:Z鈨StϢ{hFD3:e;"Te#=4v"qE;"jT)TЌډT:[d]yRB[4:[ >H4c\}әt uV"/DK<1֗^"qL&(וT[z \}yE}yj+ՖGa+0ՖGa+}0W_R1Zt/s]mF:=jaSQ'6)Na3Q'֧NaQr@pu~kՉM9;U0R~*N˵[}Qե~W'6ThF:.nD Wz \٩B:nD Wz \iBi:nuE W귺 \iJi:ׇuEXjĺ:1#u+j:TMrv`R~U:Tֻ&=Uk:TOM}R9;M0R)? X+Lu{TNTOKW+,нA &pϫS9;M0R)? (S/~u?R8;M0R(?-{%{n%㞻pv`P~jG)?O}TD+:Q8;U0Q~jzW8qh٩єS~P]O^r=d1TsݚEl-vцA{ԧN0R~;`?-vіAWkN}Z:l-#=t֜t`?ؖ F[oG&{^9i~-g*,нZsҁ`[n'm)T*'#~ͩOm8`v~PCKнZ:i~ g6*3jԧe֜V0ZS~[`?ӽA6A5׭y^t?I`k`? 61H?L`'s>0/ AP}`~"s9d1j9ԧusv`N~C"٣&^i~0Fwʣϋ:P9`S~`?J3C8?&GHy8z<`qj"{S/8tUwNu{G;v~ۂ}K]{-'7ww.աzW,]'v1X'*FϳF6Dh͌5DhÌ6 ~o( 10c3h(}<c39ĘɌc&1fB=0cJB)P0c]f0ޥw;`P0vx=`NǯC=62ccH뙱>`PX0Vj}23^}2PB\xz<Ɍ'Ɠt$qf<0ǡ˘,`,ePX0R̸;`MwC=ncm6: 걘b:^ ϯcuϯ_q 3 χzf1gC=3czNӡ1)`LIPq08(f xcJ2>=}_^Nz7K `oWMxWz /ıkF>}0g%q&ΜIcxkC'k-PZoNµ@cU7(Qt]oNf1FWQ31C0fh`ˌ1Fs P0h!Zf\+Zbf,4Z w Ɲ4豔Kc)=eƣ(z< 'W˙\x,rKxI0^KXˌFkQ06h#c;3 vmzfnM@`G2CF=>a' >zdzAkf|-<@#8"Ght8'IzՌ}G=fzĨ<1`4#F3G&32#@h-ˌ\%F.tf.tGwfW}w>>? |G_f  11Cc(1= Q((zLfdL@Y̘%1 QČ"("F`?z\ό}~=_oa-q73q7p>bC1z<Όqb<x3x3^c%3V JbzgzXO@-"[豓;}w=#<x_0' Gq1=>g91>zb!8DC@c8&Ljq kPqRFΣ!3 FCb4L<Ҙ^}Fi }ޒ-GKh hnjvюq*3NSq*У 3Fbtz`FA@^% q3.qУQ@fό>O>o@?o]O}=s|9_}1⌡A_qKpG_01c1b@yZBGQh1zDaY0}o^WQ([,-@[[,F\( ǭVǭƭ#ξbb@yn1nz`n1zDa<w=p0q=tNǝƝ#d,0\gᡲw=r0q=tn#Gq# qŸqqň롳=s0q=0P[;[Q(c)cb@y<`1z<`<`1z-ƃ@-F\ɸ`\P#CC#C|*2p@-*a|eccb@y$rҌR,F\( G(z:#Gʣb=2# _X:C!CC# Qd0p6EᡲZ@^# bzs0Y:#### WWʞo1Z_I1`<>=>v0>q=t,߀s0f1zDa<>=>q0>q=0Pzňo1=T<15ɘ#G@3pו6 £*@|H?J2y//ǿYQm1M &c@x `{`9wl1` w3Hwud|Ɉ- bPHW#H2pdᡲO'=Np0Nq=tDljƉ#GqQbzr0jY"~{ʯ |߭`Զq=TNnA2 W Ge[NlbįIl$qqňqdb#GFM񸂾X:0:ʯ&c\!<.sU8u#GʣŨPٳ,Fr,d  ʣ1˯ʯYc@y4  #Gc1 5ɘ7 #Gʣhh`4q=T 'cC1k1o,q=;-F\( t'c:C1k1o"q=hR͇63 £*ʣh h`4q=0jG3wudf# b1R)FňqdZTGj1z4Hs0,F\({GlṔ`#b=t# a12Fňȴ@L#b@ydY,G'JFV9+3Lhax[CgKo( ߮MQbzt0ZZQ(VУb@yl1Nz`l1zDa =f sgm0fFň窵h ւ\^WQ5ɣx]lPmUʣh h`q=0P,F;G;ňho1=;-F\( z:b|=E0ny>}7 btztp0:XQ(q0r,F\( Gz s+7cvxtJwGGň롳;YN@NF'C2f uO5SƩ#G4q4s8$㊀q@yt#GcۧYc@ybC#ʞdh~G|s`hyc<#b@y[|Go1zDa*2pוf++.:8b@xu@>F# 1ɘPdL F\=3pMTg1磟`PQ<[@F# qŸqqň롲';*bb|\,r}WgQqŸqqňQ<.=.u0.q=te2ee#Gc:dL3(cc1b¨I[ˁ;[Q(c c1b@yXGQ`1zAcc1b@y #GcK$cjj01j'j?U`>b1zDa_2b {2q= df}(1Ya$0 /q=tp11n1zDa0#,Ljr>&c@xTŨI#-HHcňe1F=F9,F\(1bz+]JƤ1`<1ْc {ZB| _(1bzcl9c"C2&Q1bzs0YQHIwc?xw0[Q5cŘוdL(g7c">WsQ<& \}Md4|nիʟy7cz܄d4&c@xp2p?W$3 Q= c,I@Iʟ&c@xT8a2&[@ɂK&3pW:d45ţ|4Su)~%S\2QCբS-T`;PS:dL75cŘ_1܌i5Cω\tcň![
~*z>TOg,F\=}Y2&IqŸqqň롳n1~P-cc@zT@y\guP= ʾx\q}9w]IĀ1`*2pוdL  ^q/CeO3pudL  ң2qqŸqqň롲'8}Q7cCeoy-F\( c)RC/zl1` <4c:dL2x`r.uJL ܌)1ՉQ5e>89Kx`Ϸ?X}N_g /q3UGe&C!CC#dL3JgiᡲÿCH? {v0T@ɘ00c\a*cXX`,q=0j##|H# }.4xb< xxb¨)S \]"STxb<xxb@yb /CgiQQ\Mx\̹x])r?)c@xLv2p>19`L6HINn_T2&IQO۟ ߮Ga <Գ_Z_R0M_d͘Xc=~ey +sTx'cL~>VYs x]r0VY1`Cg€1VgBQ=ƹBx0cm>*c?X??8*@m2 GU j豚C2V3pWnh}V[ QF_5]-* }7 xd+Xk0k:[2TZ|Da ^s0^q=^{"wK1`<6X @ Cer2p?$cTe01# Ah0F !#Hdzhƈ1`<6YM@M&# 㸇l16=Tp1!pq1X-0 /q=0{c+cbM𨊁xbxxbxߧ*2pﻒ1<` 7aNac@yax ·Ζ p>0{mcc`|[9~f6n>+·{*;pa=$cDa01Ý !pxb xxb@x s2pud  ң2c`q=$c]Wnj\`@0 Ge . 衲Z]匡07c(>Ws𨊁bQ b /q=0CbC`%1*_2zDa$c7cb8qc1ok1K0z`e1zDa <91(` 2(-@C&cƹBxv5c1zH`tl0cpHQ՜wz%:[2T;G0 'w*]]|`k1zDa5^W |ߍ82 bg0 9*=dQ=Bx?YO{HƟp3 uU1Pgg|Pz=(0}rn>td01`D^Q:[ C{&=BP\!<\}kuC|>MP𨊁f0pϝ݌aps>ZRqR}^`Z1`CgQQf1ʀeFňQs^$cHb0Pzňj0pוxoW:d 3ňd+0 ^k1zDac'Vdw%cHb0PY*{%cpl01{=j1 Pك,_+7c>W£*c1cW3pnƐjlx# 1j o@-*o@x*͇Q= p>>q0>q=0C NԌ!߁w0T߁:d 1j0p}6C Fx o~|w0[ڭnq@'FIƁrnt3hARN QCfl9([N@b_?#?ZQ5r'rf\0.7H5__@=b1!ɸ QqqJ<0R?dRf\0.5K%0 %PCe_d\ ȸQչ\!<.CVx.zCVq1C=4o0~P~pߘs|T68hA|~P#:d7Q3磯}F_qyy]Os`aʗ*QG%unF遻%gp==>*3||s𺒌>@zT@z55o>z;}W2zzχd|Q]W7`&Jg/1pW_p}h2ןKF` =*c ds}͚U_a߃_X Dx\臬ыdVq 񥃡zʣ'C3zƹBxTug>.:=וb|? }]`8>yt2zB=*2z?dq|?d%|y5`*\f .8qf01NFf= ң2 M9w]o>I2!ƹPsq^8A=cߊ{gI }f\Pc=Y ! ң2ңyq\C\3hVq>1pu!}4+8J2  9NԌP,F\( G'JF` =*c uu?$q@zT82r2΂zhY,dtztCVѝݠ*qJG8ݡ:l+YgU|Tƨ),c9>n@zT82q 8`#@7<]g:gB=CVq&1΀zh 8``✟Ycuu8G 衳su7nFUxixY-1P[=p>tbT7[yt7b0nj9^W:[^M:de0ql#`M ѣFzpzDaCg_ 4JGl|/{~Mƹ#`0 f \!\* £{ycx߯|\+-*·H2pfS#=z!+_YIFbw%q@xtw2pUvW?d%݉on+1p6 £W(gZ=+}O3ƙq{Ukg C1NЬ_ q=N qB9Cg3 £*£:棋1* x;PdA=D8P'=܌gxUGe!t'tbVq_+CG-!3TdPٝPdt&iPSdFSdJP0:ԣSNƹ8՘s9睠*Gj6УQbnE@x:P?d%ȁzhFN1HH\s *yU $$Ζ $ uu_dw9p\GnGG[?th0U1a2󡮫XsPP0: Gec0)9?YI)hЌ@yԱuu:#GqG;'C3vh Ќ@xq2@=*2=Tv]ˣ.p>:u-F\hg0pή+(_dB h0 QչBxŒƹW:[0:߮s+0N#Ќz?W *PCVёzW2rF@zT@z uUO\W|s9^Wq=8ո݃sޱڮ\Ũ U~}}. &@x:몾c[s\c>N\!<:8>f40@@\* {o\?tz=-`t"m8ϗHƩTQ= q\aC3: tl0U1ו^Wlh] yC%*!lWD2:Q``þ{ Ѱ*EFgbzd!+hA,G,bdB=2dd#N Q"P:8W=p9WוHp29GxT5=9*}FbJF` Ζ @ң_l.`&:[툁O-UhkꙏƜ#=*s9ǭn6ha[߯tvDw~8|nZ(y:S-2p%߽.Q 2l-1TI4X) C4h)R t}M\Y==fG:_IFa]% MģL <5n^IF2-pXqP2-a xDUg̑YJy2ja`DzXvtcty% ܯ:4,X­xc%%=p/qEA'd8 G0pr<"F1Gx Jb_lw_ݮy<~pnExH3XMv9 J`E}X c#RTssTPTg̑j!%|> <|!A}dwڵ|y{Z>p5a-x^X{, jsBp^/@= `wDx1x ,Š|C2-yǼn[&a<Ǹg?,`1:0Sqn, 1F`)Tgͫ3EєG88Jmf)qz|7?yHǖ!P,a|bP}xc|B>0XTG<CPwK1Q8(XAހzƻջP7豖 Wezxm[oS0ÖaTϫ#4%էPSP8aGN@=Nv2NPMB=&u{2&nB=& V>app6_Cq8x?s?~󐌓IIqa < cawun->vr߫ln;¿`& w(ͭ,޷W=FqxD^,XR2%o*2$#HW#v_OS.8 X w~$X@og?v83 9xg'(%=psΒ2pU38{;&{XW? ve0>&RH ln,1ܖ[F1ʡ QV<8{S_ 7<$26QQAzGf4[F=1pMe4#h9}h<Bxdc eYbtA=Z=F1pm2,Ũ|ǣܾkwwQuQE:Gf4XF1dZF@xq|wc8gXnvs \Qi#Jf<jz<:7[F1>>1&<*RGMx` wk{ľ{GsG\F#1p.-@Gfd,GF3r!z"gg=F>Ml?? 8y=\F;1Pt#O ~u@sR410?^)d{ow_k_*. ԛw+*W1C)`! iDBM_4oH4<4]`Z| x[ lޝyݕ9X`PuKQE5 NbLk^  j ATnm(U**-ɈJ?;6ģZ}7w!#q "tuu9^<7TDȸWܮ@T9|!5K]i'Ҹ ck}O;Й'}nh9_֡E\B*g?ly/qۡgBNev?~<}XNNKzJ:^qgI8ևm.W8$@kQn&b.p춹iw- Y^JW/YZ1#w"w"wG\GrW\,uqJmnݺ|0Jq; x %ϻ>z-{;֮[e ښ ȝ[}Dk/ wm-9vQġnGX!#j\;D.FMI##"#{@ZGʿ-Hc~I?"I 4H~!d[pUs$ ƲC$Ӭpyd/XrnPm/x(a&W[(}W0\z}P*n sLk}[ZK[j\e O*o~ER0*^ &WNom*Ԃ01<b[+\u{ǒ+\)(藩ے+\5QHA1:" 츂Y~ZX\zK`nru _(ĵ\XLg/i<\-e3t3W4drum'(~C6KyK}3 ^K䪚_`l-gR~nHUTS촏itgtZܹݞLQ)O5,hSpG&>:ӃE%=g~CbCB3ϔF`B. Z(<ϲxl}{smnX؝H *ѴL!QQDI!ô6Q|HQB MTs/ÕD1d13E9`dG:?  `eIM7"$+< H$6=7U/M["mQwc[6ָmU /Dp$i)?_m[6oS3Ǘۂ |i{|T;U}p75SUg(~~"ֿ }MWoDYqܙ#>_Qwo}O?1/ӧi5i?>?G7\t?꺟N|{g$9i-ķCIbP(wA2 ;H⯰=&KI<,.99ò1~$/.â<,i۳xbEGyXP3ykïâ<,iEW,!g r#3k^|_t}eٝ8cvˋkհ(rj nc͵]aYNC\tOʋٰ(rr o%# [r3EW57fXt}UJcSe fX^7iaq &e1>ayݤl2uss>_7i.=6dwUwUe9>/֠:꿫6P&˵P ;.Y[t6=BI6dwxfmUFn3&Y`ݙڢvn3&Y`ݙڢvn3&Y`ݙڢM\ 3ڢqfE6Y\oݙ?2 C,mWUF(gtq;EGqQƨANmhp$emcTF o ')k2Z1|1*Ӑ D'(Y(k2Z1 2@d esͣN^%(_%[q|pbݙ?1:ژoLyB@=f{5`}̿|V5>ZҐɃꭉe W(k[B^'UoMl:(?k[g},| qY/S#PdwI^_µʿ(r$&6Fm&d9Av͓1uŦy+ 9k]akjkl→ܺ9uX W n * 3XKɵ4Rj7XKa\( _KPJEC-}@yI\BMIvڴiS贓N[άA<3Ua2_YN :L F" \Sw @ٝY(/OBB!4ewfMc8@YgwAg䃔jo7,w'Qs ݰJYU)vîr8emݤl ɓ¾h7l ')kBJɧ(kBozg(k՛B(kBwsWQ*rptNsx+d4} 9xv'kϴg{ ?$葜I^`=34 dTn zd$lQI2hC IF&ɠGF M2&$14 dTn zd$lQI2hC IF&ɠGF M2&$14 dTn zd$lQI2hC IMA M6qIF#9C*uZUaB~ש zתZuשZu*_L˯*9-_%O*yp\,>כH^]ڜLs6'y}(_O@ٝY|713˯ 2828i(s1\/ 7I=r$ ݙz9\/k P%tPC ݙzS8xܙz`' v;&_L޸\/ MٝY|r2/zj{@w^2e lP0<sq^ۆ%ʲ;\o3ˏ'ʟѵ%ZۃT3:'7R>$!}tNǖD+dNEYؒhOQ>$@}Eג|ZRE䃔]ՂM2*'QM MʰnJY+~PNv Z;em'OR֪ś2TlAw SlILC/@'uX ,eYCmr R*-_q5\~ByZG4ȝJd7?SCP|}Ƶ,:g;Y/eЍ2xP)dy wIrtGz-w\)du wr uz 5trI u wruz vrIP r' 0-`7 P)dy w=r{v;{ yrIP r'7-Wo7P)du wrow{ }rIP r'K?-~7JP)dy wrcэ}k)du wrݤuӍekX7)du _꺹1n 7CucaW"WXKB T~2/? g8mPW+,0TKJ?K,-+t _`3kGXPW ,}GiPێz2Z/zUeT5C^`{:Jkkt Bt'k[WB]eݙ PA_=,3k Vix  ]NZɷӸV ,@Ws,] [vcX 4{ 2t}a:A5tؽ:֢6d9uA5NC =MKBob kh5JPCOSP ;[f=GGC6̇yfW8>GGC6Gyf=Wc8{>GGC6?͟Y3ۋUgv/x\kQ5*~r, *x-aC?AKSdWgXZנz;6F ==>?d<+? |Ҥ*_zc_daL$G,oCH\?c9bX?F,{dYLɞr?wרF ,wdyɲ^mMHj\|Yy `2,eyy^Aښ\5oݫ}&W$j\Yy`2,eyy^qښ\y +3j38Ky^砞7՞䫭)r?Vda݊eyy^<_mMHjO xq9!PUZvn&<ڴ؛0%փIpĔ$[;jK+-'lf4 Ņٚ刳"2&qO k>'{_@b /gsSn+s $oɛ@b /g&[ y96K@b٫{ t\CZΎq1V_:-};}A6 --֛ z#yL^&F=w>ipBzv/ )V^pvNH i${#wBrdw'lNH#ٛCl;!9V;!doNH\Ev' w4}sL?k0Y3Xo;Mw9.3mpal#i8L p8./m0al#a㲇 64Jlh64.L(maиlC#0d՝rV- *dսrV]~1TϪLV}3fU8'8l!ɹnfWՔ $' @.2/0Z>gcf|v3;Odr%A"v7з_}`v۝p.oE2y#~',`A2#rMG6NX~K2_wOLowµ{ A푭e s7b;=vK nAC e0zZip.uЖzd[)ul#;K'|,4:a]s9 k2C͡G6NXC]"[B}KG.#Y۷ 6o9EgqdYf+g98f3Y9L1Yd!ߎ {֐SoG{=k)#k=5ّ|A6yZ> g <|vd-gͳ|b>;3YC>O1Yd!ώ y֐SgGyWsE߉dl>نEv"HNIH=liuv$drԤfvB6=eL.5i -qd\ZwHNU ' F2{l"1aLN4η)aLN4-Y3EW0aLN4Y"af.1)9i ;9nEcf.1)5i xzբ {$d*P˪^ά5őٿ=@rJ&5i OI.86ĿKCrJ&4 =4p0)5i 1Ďe1ly3G4+nͦn6c;F9Ec?#~,&Am1Ȧ_/7Adr->~l&Vvz_ `ރ$&P[ 䵴-z {6rֻIae=HrLn@^M$[02$M&7b nqoz&Am1Wz8`/&@n1z[}`I~]&7i o[3X?Ln@~4S&*_]z4Svc4S)n EwʾUwʖE7n&yL~rhf.7DVF֒\%@0fvWDeQ $Wr+ d7ߍyitW('ߟ&yL@z4o}IҨsw$d{IZ~[#7% XGn"YkKGAoB%D_ٿK:r;Z[} gF4;er;urf-iw P d7sJktK'UnU2 9I/Dkt9ѓpF*\j5DF,v(/!yL~rٙ M_kt+ fzk7<}نaB]mh@0~ =O:"PL`C!]ꠁ<=lau{wer?ȃv^k` &ɧer?Cvg5:{Ei<ꐁ[/[o `o|Z&:d o[3X[$Ƀ ix ɧe CvZ^zk` &ɧer?C޽xwMߕz[m4XoH~W&wC?',`2d7F^;ghM ?c02d7F^{3ϲcILKbۃY#瀺@bYgfsM̿ݸ8FuvS"B4bCkd@0nJvSfSlh7S&@gyHn7h6 iker5r>̓r)Dn*I l7%r)D)6Jker55r>Mn l Z\ jvS"B4bC$V&WZc ݔn*I l7%r)D)6Jker55r>Mn l Z\ jvS"B4bC$V&WZc ݔn*I l7%r)D)6Jker55r>Mn l Z\ j|(=^j_$kנ46 #o%Y9 d+Adz5[lb_F}b.+wɗ#0tCltrkFk54CUrjFj5Cl7riFi5CrhFh5tC4vaFV~A|~7[ >~V.[q3`Ň6K[ nz.b3`i5Z v+R3L -WvVge օc wӖd[)l= wߐ9f~݇7w7{@>w!r~! on}~C~w3|C>< sߐ9|f>܇l7s7y@>s!r>! |o+♬!fUZϪ3nY`VjVGN͏gշrL6+ Yq5%#%?43\<32g|D67 J^?=O>K3`Vp`n&άWƏbfY:#kN2 =$g$9$N2 9optYI&# Nĝ4'9$Y#ٟmefCaC6q'@ݧlh6yX6a?m=p xêd+U<,6lC0 6$OZm ӆJd* lidrن aB!d$G NWTNr?|$y&IcpxI& )~G=q~'.'~G=$jP3$%G NRIrL99N$p N4u2 dwYbP~L*OaBO 6t3ey ȺUx\dB?d莏W+]n{'WI⚯Y?.^"O2+7Ͽ,շϛjaAb>}ſ⚃Wyͥ_qo]{ՇWW_s̽k{^or˿tO W#s̽k.]?Zs߯?[`#f5ɥ > _SsX?K^3/?~/;~V[Ļq />l ߚp^ gc =?>_/ΕXYɞϮkc%pfvkR+p ^J<#C­#Nk%nf!ɛe&PCyo/yi\PKO\]%{A3$Cf>K9< a $#*9 ֑E^z+s@1|+wA0=$"P 3 Pfy-ɛdre]zm}Bi@rk1dWrD2ĶXAA؂Ni"ẼyЛş9K#<8,Ȑ]}Wg26Po[?*V& f yL~g 37|T&af/jg  б;+}~4@:"Zh+IC%:!4aBfB$tD'RIb tфԁV'j$Tΐ FUw{Дt ך?{s9tLoMQ]ɞ7:A k<&DH_Tdo9M^B[)r>Ե6?5lr5 ii*>LC&|HCuEbcㅍ95b[#t?pBk`WӠM~'' Ոb?,rGv W} _o1m>.FaE~EpG`Im8yHy3'ua6A*2}c9Q&?5lr5ȕd ˭H,rk[͐.YEeAg@~Mo;vS/ m"V&GS6y ӎ~/; D?E,J> s~"r',Wy>| )뒿N&/u[M"dfdj@]גlbK&橾Bv[\Zms%uɏ*rΠgS>O#t`^xqM|m5U"zQ $JER5AWj_^dȯ%R-巺(I~A~ UJ;?vJF}R88>F'EeǤ z1tء8T+Ƥj*|T#4E Z" p$4w$`Jހ*!ƌlM,rjR5Ky_g8jL3@cW&LE*KMG6.?ͷKrE#ih tpe ([2w%PDGbJZR,"E?1'E+d үSD!eD2 <1Q.[0"'(Xr16'jEisHL_d L_`A=.zЀܥ/Dc@ިϱ TC"׈tؼX_`@>j@VG ȿ&'O,.y8KL֓Gn2Eng"3Z"ӈ|=#-WbhWժl# *2E<9mKL_\Jl[(dW+2oTLP]EM3 _&d<&,m2E: ry"&o؀L6*w]MNQ%Bm6҇VҪ&P (ӟ}2yX * XYZaZbE4ȭ"CKvt#| !Ry~Xl1MiE~YdCy@+~9}HɃZ5j 6ᑧ?o9 d筃$W"gt*K8lG6!}~}q JEE:^ ~~KB4y6K.my1;|_+}?k_|"bk3鍉o0)xC]̳9xKO=G!25Q<}"ZjҤ( lr*6ܾ}jPˆȼZj\hDGyOUlPWX Vy*ٜ!cMܾ̋3 ),U-3gA^-^2=dAKBQ.S|}3 D6!ߣ"{^&*غ*-{=mwQj6 U M>ȃ8y #,Ȑucm7L@5N2KV{dT8ϱ"mjEۡ |c&6Aű"`J3A,rJ41?dDV#a|# .9EN.K_ z%,iW"/9F䯁x+LE׃r1"6Z˙6o n&A\rG*Z?#xOtqM,hln&gЂ됾I[ߋjU?PnS{^6!(l`AnL#}e4iyM6F !|ӑm?qr %_bd6yVϚշQ/HcS@6c1=Qkiy'L>Rܾ=g#]_Gi=.m?):Ӗ$_w_Q춈nqq$I_g?Dc_O.2Ow[t]p#Ƀ^ B(>HD?%[p8?JГQo;r-tRL\]Cf+l_&߮9l PWK"A͏)r#8"K&'6ȧ_~P,bms1fvپy6sbٱۅN#~QKA*6.ȣy1|hcys._`1uAA1!k(Pl.2EM+ٜ!C6sݳ܎>w[-ҧq%O4Yorjy}摏:d@T6o{ZQƧ}ů ?o 3.wK %KແXp<@5ΆbR>aCPL-R  K`F+&dTZRӣhSNJebtAsAe:봃m-QJCH- KBw"W-͡1w6IEw1wsـ" 4 ?5lr5ȕd󠲹EqSd*a<1P[f)rl s/*2P#vwVN62'Unea͡jH"7D3 ?{dv_Wd*< -a}2*佼D&ZzY<"KWi%nCh#覞AF* m3,kժ:Ym~TL䇙 Mn/iyMv}&SInScع݋op_TclaP7t݆7tĠ {SLR &}oE]SN6![yM Z{e3#? Xk%rꜰ)B69 4oV[e.3NE^&zw+r\zi!oV ԲmCMvPf663ӟV@VZ'SBjqshPǻBR\-{11yY@2zqqE k>N7#J+B;6#UHOY>"_KFfҳ!>Ռ:Wd[p6n`OoVJ{%cR|U{ҮrE#mr!yN-?2W;#{zDVv)u0# fLq w'ّPХlI$Mҧ."w9MVۤM~ޏ~da9GRnU.NUIa{?=h-rߏ|'l=HfR۩0"?1 wڰCH_bր߆rm6r{t];3l l"Ӟ<P/L{69MZ5ˠZ׀L{6R =_an6ly%rurƁxЊ>y"Z^#]y\ȼUBجj i aJRج:$i0w~w_yJDLYV sUy*{VO {sGZŀND={#{Yw6;irѕ;7K"Ө ,0k kY ?KX )DX)2M_Jc"-͊|+ e6ߩȫP2*b @]hq&E?Uza_yGkB%mTCAU݊ q-zeDDu#]p; }#[R}[78_ށ9'">_'z+>WǷuz_J$`DGoDimp=7]Rv1îeo.6ERvT'^-Ub/K%pIH8L(%2ki)Bݓy7KO&'o;v䥧h' ȻtДLY/WG@brԽ9}DW`''dS‡tBYiS"_j2e//"S/L/1YVDrڼ>SkDc,J5&)g3˭υ1Y6cfe3ͯOX&<"?ɲu^&Sza}1e\%RYx1Ƈq\(5/mZgr V!ha`RkfZ1.>#Zw/y5)&٤|aʘraRiK/ʃSEaەq j0rkه8)cw&MhkXlYo{nfgR岥4 a k*he(k\8L8wƅS;Ycpt)٠GXaG< _sW;Ρת\4}ed85|nʊ| Sy-|&mAlBl NAXkڔɏtfa:+MH19d4iup|Vkk^tF~R3g'h99ŪakS#W}:{Jϐ{4u=O<+Lz^@=O`NP l6naBV;d;>:>ߑ:UA ۭiX㍃ƄdyCXpׇ5 Y=5;{'Pwr=,ۂ6Ztא:0bt]㸝2IZU9FjmMZhCWX:w By.|vqm꠻8e4y\  r ܞam2=Tz'ZRѻ6ݖt׭ͩQmR;m[>!K) jEpU%&#E }ݺr2/PD;R$#OWw̌$b2$6Y(b(b]DNFOy1oʿ>!?QuEmF*&W%S6l[*PT.4U#FmP.8J$55PƜj eNw 4ýMgsvvKXl+Yn-렻ns״=(p9ݎ&fc<'A)q'J1S"wИt/ "p*܎D1$>/ ?/"N qf#229V9+@d[UN{cedTڲ,#cZWHYFh$ey\H\ PcfI)ΕY=2糉 |ߡ'!'dC"9r,c:1@s½?}gQCW} 0mCV,Ib;[U8chc4ưE䱠uȻ4Y6Wu@0ZB'p< y\UJ,Z؝-KD>OuE =HA_.hb}<dz~LMY.6J389*??.\owbo~UR6A6gf@^43WCOÊhZR3"=Nw>G}mm.SPEqJ4hz,30 xq;uH`%ɦz(ᬃPVIqL=;YM8nYp8"- ѵZ{* +vґXZgtysbY K,1,hGv,] *-s ǤpLߴ[t;h'JEu^!yv!{k4I1y ,|ҮʂF_@ omu15 rWʨWU{5=1 r& &wmAv8KRr TڍaDͶ? sy$gv(g39&$SX4|&F=չUtI8Iy[gA@E<+yγX!. _K_-|^ϋՠVۼɯu_[o,5,9&Vˏw>mǚm3K~_!(K@$ϠX)] q|}(T t5O5^9$s"$N_'IY,T2|J'u9ϷF*Z #QF4.H҈dϧ${'N6|$/wvF%{>fps;fta5ܛQ7 n)llrGL/gYn?O'tV{f$O㘔@T@ кIhyH%I Nå$kZBҰg{<G,vfJJ *&gr _Xbks ;XD3/?%@aT*/|*S}?J;}OmLzwVUdEx'`8!w iaIՁ>Cjyr8ɬڣ1ܻزjS-~)qNG㒅dzrb33DkA]r7*JI5&*sɻAs'sozrQǏD#>3yɾyiħ)oh1I5ڨ&畺mϚ\PۢFh3z:5P-z (E|\*Y%|=";5/9*q)~_GE(r.Wx)2B_m o5%=&'o K!z:~$W}EZI%iȔ *+rf)*%E(_R)Ks%+.k 8:LV˓|׶ݫki^ee.΂\nd =.,2,FoZX3/^K8`%Zjǵqs>sv{%L(:žfSͰ")g 6v"3iҴ i'J넕`V#݀h9FVŠqVIF%5S*r\#-cK⾼q_K %wKᚐSyK&n#G'6*Ofl>oDv!"'(QBW(r,/gEksÈp7vA\*rLNKT)"lmb9WLuxo3E{brA|"!&S4I뒿w:y< l"/KA-5 E^#&S4U6?ț䍠n27X*&G0bclu)EK.FnnnQ9 #Nr;&%Oy;cdzoSODn5xAuEnkf%bD~K mN]E ?PSBOAXDX?l>cDT#V$2!%"džFZbFAL )"rasFE+yt-VM鴤>Q z/~M&P@7:C27|RL}?Qd:-IևAufWCŽُ(r|-uY鴤q\-9 M':o)}bLPg0"e.5AL^ S&_(r%F,"7/Ϳ=`brK{TH6?0?{[&%#?6*EEs Є#kQD;~lDauzD۵o{-ܼ"r"zԩ;tRz:y$='zF݄~0ur<~Lg-÷^Dhj$ɭQ)&Uꌆ;(S'wUF(r|A%^RyPCrGL_yjoh7O@j|=OuߪɳAm`?("E#|Ad!YQ"r?j#?)[%&o kk\'&ׂZ .@}INSģt95ţgk⚴ =HRS =jwȿ+;,H]\;/DV7z)x)[w2SI-§8$aW{"Wt+ .wK?RdWDŽWD>J[lȎZ}A_p̌Ad2&cE,AyYLn_+r|C6FOݠo`'|^L>Y툌y~Lk9OLTUKY=I!z[ l3E#E>dP:g"''b7$}X5㔉OBe^FF~86{pwp^FF.uiL,"_2? ~ҘFoSo!PBrȕF)Y=Gװ٠6ۏ)r\ _P:1ZC?,!&Am3R#s+aswknس[ {lP.Ǫu[A+x?U}!W!RUwCoai*g iBN+Я ˃rë55{,f4ZFoG+gJݬ%n/tgvP"[NJX[`/&rПNȩ 9m0yrŵ3ְ:ݝPvA=ڹylii/Cˍ|P}`W lן$9)۞l#I2ɞ\7W\ga4V\k`<2O>\e{.ˬoRO @5rPQe"ӹ/>d?M.QAKy:e`s"%yjwh=VGf>{v)Fn?xMD9M'U0xTgU wA+1(:k$Y䒴ee@HZmkkU|Z>h?k)HmDKs[m1}"/M>r{>=$KUo*b Pg9KA] #jԁ|"%rJm^Amkf,fwKUݧӛMPſ+2Y]9K!֟[pc9H#@~RDrWϩ&_ׂD1CD^]M=>Ŋ*"uC?ZMk0FG+Im \D.vZsz#2ES_s=D<7ƕm>hrڍt"7|ɳV+KǍ7k49 ML.]_*/^&"w9ft'/W[\?r"?)&WZa`ӊ\j͵KLjͯ(/}3Ma l~OO]vjSD6SN1ns@qWk)fNLH_52SPFB-:pUK'?IwZ羒H:,Gv*f(pϑ%Ŭ-ƍb~Z,haMe=-1W9sZɫ B͍i^'=Sƃq W/"=G{HU >8I:ȷ^=t]Y lGmΊDCl+YFdawFR ɓye؝qv+nfn7zSz֝n=voZ5H+XX'Qujxw["ǭh)k(뜕iMl(d@^4&mn(Bj.D=qFdJq4"Q횈~"L{"X/5evy;0V9;:ǮGm8ŗ+mvQt6JPf1d代~|IWjqdFV:LvGdeY%ͱYP;]ZOlbt=ؾ̾ڞZVW)ţچFZvF<5 &y- p"WqJeL䲠b#2vw ml g:#"{t 7чD`Tj#r=w mށ\gD~Y.asIe'f8>"N+ a~D>Dž6'}EWf|Ze'`~D?EMU qOMEd D~[ 5"SLH=WXڻḵgD6[rr Br @bό @-mU< 6O2||73 <_L 6K_P왗D4;{Y#&uUp65,&oft.}]GL ,mkM<ѷy=t.isqg* A^mD^Ȳ9Fwd9܇+<r৐cIfCk*rmu]N-k Dy\azf-5*;*nj\J 3*f6f*;7,aQ'KdJڙHqh-dpL4tVDъ.3Hxъ(:slP>*RS^\|?m|b2QOb2Q]"qU.MP~ȃ#4]n[@?BՊ*eN| r#~T*~kuȯ+v#7.9VyXE}v_:d~#E>'$oDXGֻdzԇQ,\qOBˈ]>p`*2Z$P'FY{N A=W6Z(ynyРPdZW*Џ'y-"6ObFh*bez(Ui^ϳqɢQ6L}u^7~sg"+(~~>TzP?fo2dӑB·rqO6+ԁŌ#~RR'0*YnLs9l,O?ռٗ=a55G8ʪ`3w^3wfyj}ޅ{[_qԋ6ΔӘ{`wjb(Gqt9kaH]Bwz{VI o'ZVՈILuwұ"|}/YUdžf[jFcEy*xʛ8ln3M,xmcU=)ocuiM 7n ҀG1V51\@T3p@{ҏD@{XVAMV@ţD%ZZQ15o0=zJ(G$MLѫ}-Tj5QJO~\19»nbP7TdjDži'3߅t݉71 &:x2:;ݟLij—,7XjG22QWr=W._ Aq+ S\Hp=2+]fc3܀{ﰔXD&-ffd4Husmρ6b`$o#dJes 496ǧl)~tdmd:u8Gtb:1u^Y'=4>un*)`@yBL\9G*) ΁'ӌ}6hgԛm)ɲ *7<דZIM턼wZ;㵄Ncr+7I5Nv{=jbE51y_{V jU-B7\k>ZQT 1j@,[jAS5mr!66?K:Qvbd^y'=#hwЪ(IGYvO|0 .# ~E^5iav?dI+ {M^{ډ|ڥ=9 +W66ӹ2jPn]&x6{ݠ6at:7p@L>8Ow+;@D% ɿS&ofj=A )ת ^h`H^+` -:JuFDݎU<;5 ͷI=7)w9uI^L^ Ruɫ$J5i,O(14$OV"{+Dz>XUEs;쥹1pn/c h=/Fɦ/鵦;euXyNJ6ͳtW+\sn,k2zXQVγ%>ЅY_J+{,RѭMvpB;`orTu Q$ᭈ-tZA7 _b^,j8֟uUOi):DNNVlMs/_1V5wp^J{О#2OPY;IeW߉<=_  9 jU ʳ&VZ섶JnKpE.:ŬEN)e# %?@fv2Xh4ӱ)ssd7)R45bB%bvH5sXvoFlؙț"ֈv=Vx[ğ!!sK/_g$w/gWM2sЛ}+f9L> FW=ς0ȷ(yyxp׌#21Jd8GF~2AޡDv'dZahG'^j#9G^D&xȩ& {2yvUao=]%~iwS#հo: Q&/ M eu%% X+X!.}a̳千eMgK8jcIs;ҝ iM# Ez>Z9p9}ـrb7kQN@'%8 g3fO8w"LobE[F֔Ti&$id;&#~l/8&)PJgXQ(5`Q~O 돨-L\aw~ʵ&kSt 6ZuN- ^>77 VFTnW: Amߵvh=3'p:bmS/ EU!Z?w0ywv$<7Dkxndy"g]^Ϭ5LkĴ1I<5i |i^ۘ8icZx9tjc:Y#IV.(ltO9vq9Úc8T8>n~ޕFw+5*:mnn!%r;NBk%y2IL5ˀ[ᑧ+:(JrP%i@'8+=2,DFH_+3mH5dB$W"WHs#: >>kp$35ةR"3F;Q9=4hI{It3 gheIP ݺ$y2r4Wm;sIdA_zPA%%m uL.Z*T2"$"ԾH' Σ;nCkD-ҠzBfyX|"ϟ- sO> ;*NY% SSaq3vPy'$Ƃ[-8ki=ğvl5Q%yD܊<'9OCEO7RMT(fOS,:Oߟ{KM7(32mXmw"&5=NEa~A39AH6(ma{Xh'3)F+Wwo$y(0/'Զ>gzl4 Vz'^tc@ {OUۍۏkYW#W>UM%$zjd+6`1}2Jd,QG؈^&_'ƀ%IުLȀL"1APm7t?Cݠ6_bd.)3q z;L\$)FIuu*bFd]UD#jV%]]hf|$Z5*w"򽒬kը0۵lF]F -ZwTkw}wj{G_F{LZ3Nk%9_< 4{rfV*׀ZiJ2[~{V)M"A\9[ r)P&ׁZg (2ɇA=%%KX29v ͿSP{ jՇr,y^|#X]Q۵J,9Ib45ݵ do*L%S*Q&ϋ9Vf2۵j6AI]4z(6݈#ji1~YoSŴSSFvgmV7 y쿐"jlw w5+B$+D5kĮe9OBfEݬ4Jø>YiN+kĦv ˛]F I0"'MFIӝ 5JW9h{Q*ޞL,LeG^K̎aGއ9VIM_*}mdAbmdVb#6RX$B#_,=g1qv>O;*v.v z.O{Mky1%rN|_4'ެMX; N.F&$o](T@vTEŽ䃓4\۔A=.v&)"sB na-`!WJDEXǓ IY0wr^Oi/:u2:-`y ðh):v jGδ]3ĥj#cd[Xsq=ZqR5 9Q!HڣvZ\%r}Hk"iDKIV)o$$;Sܞ!MdiHxnD 4*{`OA>$-KkCPybd|Rn1UaK%1銿*8wE8'G8?S$2DLdC39[0)m;lW"C,>wtN$Tp1#G/IU=JlR9i7cF䣒|BUL{CKh L. VG6A 7:VۘmpMlS|,JVw^h%NJcNX,/$)[.sbu*=rJpJ8V(p Utq̛w~,?B!N}AwۤS@d"ۉ:hpa]X:v.?=5-I|C{:}bGVj:_>>7b5󟞜{ɑ(YC_ENZ Rܛ$H[8i)6ڹ Q-'chkW#աaxcqpRJW'/]YVJx'5 xD.f ́(UgXm7I XQl7ث 䱚r7$TOA~t(aGY&b-.KdA *E-gWd455I"IW;cV|oՍvueχf/&7qR>lR>%VX)Xkcp +χyh@sy,@ӗp JG F}[_\S+2kfү_#ϥǼ~^ay?G`qyDc⠥jYwwgVʋ ԏ #dVjRo2=N?&Kѳ/Xտy]ܫ+7GklVpe?B>F0 C$D-DCU[otf.~Ҙ9J_#XcR8akd{^]ȁX姲P6f)lԇ[&S~>bBþ q(?? 4A ,ِ_1I1wPK^?mǠ'^ra -Rpmh =_˙'5c=h3Kz'Tk`3zg|Z$&yWm"rTF/-AT"ϋ5/azj%" 6*ӑw&߮'5׭wufFY{-+Fo7~įZ:h–J[{?7%DV®cnme@k"L¤8 ?t# O۪`">K/Z/׷k+U]gyuqebjm!r$zºjDVL3H3FdW~Dv!/h,O!)o[~B&I?(-[$i>43"$yo4iKi#}<~5QO## 8~,per5d5ZcI2dJ36`]>ڼ2 +dZyTBwXsCn/wE2+ B؆V:IXI$Qɭ&-I*i!vCP[!ay 1&;sb!DVCɛ$lfP7k$yb I~-X5 vSM'װJ7t,'c9*5jf}ɉBG@؍4(bJgtiV 2ջ`e{M0$W!m" OJrǽ+fְ9Ͽްn4"'|O6@:C4y7NcDKF>waZ$>CHe|n;ʹIdZIAw/ʪo3y8aP|,DNR|B'i yHRz&#O zs:J|^Q)ʴ25BxNً=_5K3kYk4;èf!~s 5PJ=H6'b^ջp?Rc*…F'*9E{Mމ f[|>&r#ƒ,rm0F-q|);EƗkA"z`gZdױ,7 S.Fo od7&2U\&WtUaZ#6+L{\[>EuteiM-iu73P6A"n~tRf3%KQʥ7ID4L~$oXdiBGX<9kFl?k􇒜& B694l~EK䝠Ԝ).g ydP1i AZUcQ͚!T! h҄`Gh-j뻅VcZCDž[4#wDž{-B|͘ ʹ/4X'QX1bMmd# $>nx)kRj 07]t{@kc^ {9zDH=2R*s=. bOٷErmvtn7:5׾%jǥRFD#)Q)vitj_[٫=\m c.2`< K%epBQfWm}'hf?K#f6}tVR-ot U CݥjՆOy$fkY}BIy3qgUV~5`p3إV>aȼZgUVNK~E&|m' ՠVǨa'2ȱpӰ7|Mn8%9 [j͞4'Jsm$gǂ:QvI&Ouߔ<6y.s5z%6y4l'I^&5Ku_esz &BP AW%&lre6RNcϓ: DLn@Z"ѥBJLJ[LEbB;0 %se3.D 9(/I}vz{M5B%>GM7nқ\qsL-a]or_f69jnLorM !~ 'di+ɧvP5iћ\mk/KW%, jg\&PL[2C69G>Q6[ՆȩX]w3 Eȼ|ܤ1|&zI.b(r ,Ƀ E^j-RI^$Y}껲Vy;˵n Gȃ-^ނ-kI@VoI>>wzN>'zI"eKAP9v݂#$ᓬzQ▋ 5j`FxF|'ڃmPfe ]:-JDH9) ssgx aU؉v]~/nM(B嬁 5VeX)TϡBxCPE} [] K? mr\y0iW4-bb>ТA7[}Fri(F@7qdfIwsΊ>KJdwsǝ*kn_M;[uO%e }'@^bVZա')(qguHG$r5^4[?Hglr7ݎg!].96[nޣtm+,c6Y4v>Ca3XqX#gqnij&i4l~IK"PkY= ZU6,:B<-?EKf:VMa'zbfk~Oy6ӞMcr Dž?\ԧBN~OuvN&M@'L'Itb,vjv^;[9bw ƅXB.!I(nMJ)N9@:% &P@Z,N9]$v#Lϲn9'InH2"g3 dG|\|;b1p" 2(6F@PsMx*7 "w/lt{2XQ)bV Q(g E(\"t9K(hNǠz#\2cLA9YWYe2J0Qɛi8 82Z=?gnWw6݃@`Sr6'7Ҽrc`Ҽ#`Bs.Yx*Tv#jV# mZT~VK`"w9OzȈ -0Q՝h2(>iz{@#z#,˷I";AOk#_i"4H|fn߲llE&0q,鐇ӂ0is}(2B5`N^&kGt7L3$-2>5%fQ_|$OPCr M!!䩠Nհ$^6PѰAI~*w} q:Ed=  lrOJ$A]a%M~5l~M+2Pa󛲯""סz+2^N=|C5t[ΤAʠ=MDZ)D#A#ĨNhDBRΝ!fhCpUhZh+@ΑYi A9__fe'w1mZ,ֲ o@o0m.*jI~E{ hk`lnP&gLVTJпSOBI6y'4L#wzgH)ɟ2L#xV|\)y3Hmur$f_Mu,`OR%wI!.SP'ɝA,Y"۬{'ƙ`Py^%dė IyDfw_L}~mf_6tpvL |N"S\b%"o >^9Q ʶeAF7eDnOX]tK4˔ɵa@=v><>ц%C6G4JX\|>|> /l#x^'V>wFF>wK>jF9أQB!-z:R}KmgJH=GHo`zحJ wAa"Yhn膤z3}tAm8T>EyC~x:O#A҈# DHx>JҙAbTՀQ~|sЉ1p,T gyŝVAk4A:zJhq!zōj$ɟZv,lV$GXgu;͸ƭB=&D:|\P,D|KdҎ.b' ( 7cBgͨys%T՝lU/9{l]RfVdIGXf]Nc hu楋V],5e9Ie[b,)dVQ1Y3e,lFd9/[} ߼^{?}{yAQ{h,0xl-PoYZhMfcnyYouKۏ?:VEM )5_@>,*ޤ"/NUұ0cKfRf.T֦w<:~WE^GoRI;8*TzS}TX;sPޫۋjT5[b;ʟ+A=c\US&o'J4?*!EUV5U\/}"znZ)wHk[E` H6+OW@<r4K QR,y IQҡ?BDuu k\*zKTI'E|{S 8;'u:kMyCoK<( :ygmn&?ĶgRm``oͭ|9y^aw(ۜ oM~CGE~7(͝䏇}gTiVh?|^PnU7`ٚ|@A;&ȖKEnhwӚu8w*-lwaU^Qk@\ɽVy8hݰ&Gh-(hPoM~%QUl߀ `w<9$lɠƚ!< ޑ cru*HC6_dȵj}1;m&7|@mphCn '.e=khQh'fSj(vn/m6vWW2ÉKkC?5>5y3[BP{{=#_gXOI?7'Bny/#Nh5~JQ5\^4D͢ah@<6z`zBwv2%*Л'^ ez:haQ AVV;-RhXԈ{skdQ-U">&vq0!G+ ,xW6ўg9 %.,+{)fQT;~)se|ePBЖP6r(k<Q拊4dY7T EseQ"JQr!KQVKJ^4ݚ@:&}ƞ-g3[V f2Gj5'JTQ<}5VC֑kdm4;/z#wWz]Q#v5/75t8?TrNEegK/q^MT>9&Um>˕3! "cN(TcBTt"s$ȑhbdWގzfȮ3?m~|C}/bK֐7ā|!&3u78n9JY̒qtr^MTވ),9ol߉!rAGlos>kT-O7ZmȜe&yG:? OĜefueW|]fI&s=wI|}L" y|4d)r7=>ɹKݢi3?oUgYƙ\ ʰ,s >fȳ}?mȜeyFN1S#1Ӝ|O-|r8||b3{k8 F?{UU>5W/:(1^U_~1[S Z؞iXW͐{b]QyϘ2+w2uA"=';!xGʌwJΉV2f+5E}=#㸈nwKw:q鿚{|xu6ou}_W2c.4Pw>揟_2ߐu1 f&ɺbf?~r MnNړ%MaRm؟}Æ|^%&²VMrtS\&_%d~6v긨iAOE1d~2[O+zNOȗ)ɵNdOd wwLNKf/i!JNS"bԝI1:mYf[}o0ɉO$|Cvk6dthۆ(]]6sFT* Tg.zsUC(&:c@=)|tX *@BG TJ|[9iVO V(e(4P߫jATIu8j%NtkΔSq T-:!%_r|v 4!kw*{ܯ9 '^ߨl:s?wӄ&'z3| np~f;41Sh˕OiǗoXh Y?u|63u9-4Gsam1=56!Mb;OtZ܇ l7ζŎ==!{;d0_#zvy.B9AY:cmE^V4"(JB*. S)I`!GCc!J圄Y!wr`0 J0, xڥKLA}l XTy! $&XP!5pN&ƫ4.M4Gugfw!U1l@!Tz)DAh * l+ ˲s16>y@(PcyVw cظA>Je+:'謃фӔ}~.KIe rܔ5LYȔ)S5elbmܥE@۫. _.rvh3 ]Oz>J]5+D2,syfՊ/8 qp^sbp,6PW"g|qK>r~-sxzK(,d>l`ovo5壑g M5|2 oni%$U@\TJ}H'y=|z850v*i< vXLC$꿣q2S/ *!y;[s<2hk?eD+hWV*eբ}=D~C.E_P-1!ȟt vWz!9Q!gkD4*DUwyϠ[G// VQqV3(r9RDh[}s,bCw_o]wwNpV^sy vCj{d "3(vw3_=`! ƀT$LrAL xcdd``d 2 ĜL0##0KQ* W@HRcgbR ~RnĒʂT/&`b]F"L ,A,a K&v&"rofabM-VK-WMN.10@aoĵUMӯ/bGT+ّs"72K@('=L`]o}ȬgB~ "2ksCCq/(Bܤ:Zv.K\qx?l`G2Wc[8kE{X&@̅v_geC7,aBv(/3Ǐ:+Ϳ {|C\+J &οS%?EĹŁdnn%4rs88X v 0y{e#RpeqIj.ŠV "z> 1C3X?½<`!Re_r@I)sz@ hOxڕQ?KPwI&-tԩ-Z'tGpKHA\% pw.^p|*dʑ,Ej?ʼ:αWm:r 'C@|IY*)t4`AFS&k+s.^Ad1!؅o{T?K6B0Iix?dtG}YZO:UKX/K0+_(UGMX]t:zsN^=A_'-=r|~9m]γ?~%_6)+V_Ogq̇Cxd>}6`k3ZR \F`!c|dO#Rz>+` oC xڥMLAtP  *+pp&ihb55`(7&Mx41&&^W/5* M4&:b}y7|/hd#!ElfM] :+D5BB@>AI&@vQT-PTj&҉[J+{ͧ |"MO(nLA~( 22[{yN 50]ek1;M]4:X <zf6JSҡ3gj\@~k 0c`!OE ~q~H;,3T~<3PL xڥKhAǿټ6I!v[C$CA(Bmn!`Xi& 1^=yR| A=((gJ zR zy.3!HԅI7fײ CL~ qC )5SPe.("{bХb^!6gsm"[̗e/Ydd~Տ^: ٵNks9Oo1UBW%ֈ4Y(ik9/YgnOm}b7wLԛx#*M|`X%ڛjHvYR'©2X[Ĉo8wPdP/p^vEf*x`{e~2Ws-|tx~ oq 1m_V]W,ͥ mauaJ9݉$>Oò~nH֯{E}S}MuuH {V)akyk+/B0{3Eϭ8~3)g&deU'}A-,?KhzAǏиh7q~Tfc;I ~~sן$7X=ޡY_7:hOu3_ԏԪK%_"f}g4-ba}&r\8dbj\M_7l`! b>1AgX @0# kxڥkA~o&D"X[l"($S"T[ X0]M^BJ*xUTD? 9 xu>v⤍Pu$潷#3,e/hdYXHXB8"&ñ.ѵs鄞#Qcp o%Y<@dƁ˳;S @Dsd ;}Turn2't< i3\7-{"{[{u~߼1Sʢ_ F#4N9"!屘;J;!]e >ϧo`ךP$b5|&/4k(cn)&[cP>UbThE^OL>n9w1A :+q%[;f-޴9x}`1.brL_=gM4H :}RfP: ^>_WMndD+>Wק 1qbkV۳TX+= SŝD_5u`!2TKEŃ:ϵzby;X6xY]hU~/_|Yc u4Ԫ04R(tTE].& &X*{!^BFī]zd t0ط$_%mraBrNysޜsBpO9 ${9.lֳ)'Z,1.:%X)?Kĥ2B=} uVWh4~mkbk{r1檎>hߧ-' Gq"b} B )a=7ORfώWLV/uˊs'.?(az'aFm$qnrܣY:"fyLՕjmRsb+l7nGbf *&_*_zwCwzi2%1*g܃E6wֻe8];m'nʋ+c؋/RyjMlVA٨8G!Wo`!-D!=MҾt` 0xcdd`` @c112BYL%bpuy\c.`2¶g`!(eZZǀ{3PQ _eCbNN*H G/6^1XB/`2Bn/]p 3#DKx0`pa 277 >i=] `pgdbR ,.I ?:t?3e쨉Q`!y99`^N@3PGxڕkQ6vm[i-鏰/BElФJ</RDAɈ (!PPiyy|f3ov} lk ΀ebv*LԺ$~anMt\ίW5xdXK+7@{PFy&s,m&}9K eFqQ^wV-jd/G٫k:iʶ-/ޛ@ɩbeDydE_M\nV4$٧99{Jڌ5-8_ݿO1PC_M5$8%?gq\*݆ϗxN"܃_ M҈.-ӈA#:,Q2Yr.OKb; e9YQx"4g4GiDSɈv$!"q?vN@+ԙ\O[ւF*6FT~N'LlyBk*Tli=cboFi9HތC:PR=2md ?lzL坽T,WWSN(!|Qؼ 6VR6ۡ: `2nfc"kU"jχlx2V+ ĽeJEbEAUD\ YTD%z#t\I<S/Ke뻻po;H~ӫ=H?dpYil} iީ#_[%+l?9 Vޱ|j$t]>0|rZ9j@NDgiM^LwL='l\|_AYPIV;QL{!'yo@?Ъ5w0 A1 wVuZwX vNN=wXטL5^! vtokw$ vG;49;)FB%ZZ3>6܄d=%y]#~2sع8>SD;ZTmgMO5vag \2av,UAxeu<8iʛZ) QaV^ZiZ)'(N/ޭT7pxkR6rf{w۠>{$iHK+T~1D t*ߧSX?>l=K)dCک|^wQT>//d?YԂ}yڟG Q܏Gp~,ep~ y֩֩ah؟֫7f&|Ǐ[\ ᘪfo8+2~Q.#@`!A|e4~x3O90xݛklTEs-}`#cL!_$QSG @1%mˣBP 0_(!!1B X.o#4DMsνwv`9ߜ33;wV!y/PIb9~I9T *ETHG9ryImPET_T*Ve6|m2v+{\#H=O?HBʫS-'"+(8kxZG#˾H[~=ҾyQ-?Rn7Óg7srd$KA]+߯r47%k|rƩd2t=T /HmѴV33 nżE=l$,*L.)*έjՌUzxg-&T95,qد[Jf39 YQf^?]^.)̾T9״кZ׭uZסuZ7Wj]h]M?UNDQn֝ҺSZBN,$;aXSO1ߝRurtꑗ"fe!+?Q[V!~H/5K̑pHuh.-Ȅ%f7Rb\[,겑2GR"ۊUX [, 6KU24 )l28g:&\[4&KT e,:+2nj%Du5ORC'T'Bcvbj9x:h͖ZB[ ۢ:a:A}`8*28$r%%LR٢HUf\N-AB|o8 H[֫CFC9hGH[XRآbRv]awG5YPj9*UP ]h5`4VIѾHbRCfzA[tzP _[Mk:ۡHQ0QxEDAhߣ DPm آHA#A[t;}-~>#>"l{DD`ڏ x{EnD>vv [|Dk#2R"`-Qmv#v|/uPoBL`]&씺*&r^ۡ>qsQaznꄷEFU`>څ>o/j-]sP#ln[Cȓ{E?)[ܤRCub.8н<[ܠ{RCuT)> `2Xo7Q'Voaj-~~LTs ^%_1H53l;^ykĩFpN?R9z)T\Oc \&28;A 91NgtLp'Qd:˔_;<~:JZ;RMSavvOǛZyg?239zNӝ/=K<҂NeGԿ+:b]ّ̻eĭ@>RmB3g:skv~vP~Zw|ҭ*=_X;;Q+X3/Jf|w0靍p;q_:U3ܹg샳At+W"=N7;ҿ3鶃9ik3Vҡe`!ن,4^$ LDJxoǫ^8!&ZfEzwHfÞVZ"YhF" .CnD{`?`|7aߐH%zU9(1ϫOQ4cKY>݂WX,ز|[UZδ֒|Ţ[1r}=FXߟ.ׂ-_=\9\WP2}:?9*h^2BZ+uyYYWRqK}UJ\*ِE' Ldb*Q/ѽqrWu5[rIJaD/~us{+|TW.Uu˄zkq"29aHT@2nOe\.Fƭ)jzDL)=Q?Qhh=+JPvF@G)N,V;y{g ;TeZEGF؛i5Vё> $Tv,79< d-vhbu%6QXægsȺ̺9( l]c](̱"N Q8*rfJ/L81ۧYxJ-,pxJmV,i*e,]6AY /Se=vS)Kf֔)0-p^iCyUKyGXkἓ{QJuVXgj 5(!FKCUHؔx̪*oO"!m繐>Gez{5MǙmZ:ZUvu&zwߵzZjzzbyyK_Ctb۩#hz9,O>VO6 ܥfjL7j!!bqJH$"zo:ĜK⦪R%4UIl tr1 K#xJˎ`5ӷ:]:R=pn|VӘYUԫ5Ѻq&2k{x@&,!sDfDfݸtMkR)Kn9MAu.rJꪲM$X.zo1UY3P%deZL s[GAKl9:˨ꚩꒇ}w.ZV*kuڣ}EFpG+2=BwJaM;QE&oC]K;C6k =M MYSϱDN='cyc< iœM90>Àg*ճ+~V_|?3Yٞ,3yU}UܩߞqhlϯV-ϯ6tRj#vRmBHN*$TTI 5Dj褆!5rR#B;1&HMԄPSNjJR3'5#>RNjPH:CB#u줎 @j@mpMw2=?Om! ?x{q3wz0waQu sџ'qx'.=H'{Fďś H%fݵf V0/gĺ`!S|A5dN`,;@S:!xcdd``cV8c0Mcf@=)F}!0謐Iio`:)x՝yUՕԀPM*@D@;jņ*14EQ D" q1EI;$p""J4^{U}m>sV{^{}n 7M:InHӓ/L3I&F$I'yJxj3hyLOdžGpUAa_3VV6#|m t:4 AT46 zk|hL9Q8own^8A^qZ'p$f׭]IvX~_]z7#璴7i~B)b DfT?+|x]@srFF?h$0 ~h8 ~h0 /~T?}7/T?y< T?_P/T?U|S/SSSEt*sb9xHsrE?ËOt~+OGt1T?m.i /vT?G(xI('~xT?k'I8?s0 ~ /:Hۿ=h/N~v"/Erގ~¹m75yyVוuLQ9ȋ6Qq.zgr~l@^;g>yT?O*IEOQ&EN5k ^?b8o]pՉ秫r5asϱIVQ"ˏqIV8y < .7<x׿)8ƧE唒_ FدKCbXD\WGgi!qbco4VGgCbMxF+Ljk ^?(5 9NK.M:s5=ߑ7R/Iÿ%ߑRs0*dTN9bϸ rN}u(l\7F}=x#;O9e'lAKS0axVPN\7a(ǧ>,3sZ8ү/1%b?(d~匉Kp; 7u|.30匍1Av^q^~Cֹ u(sb]D]u.!uu=SlU2pB zWysx~$~ l\7Fp<ؿu="ij պmh5:h 5*dsZ#u롱ކ st_'0rɩ_a B3\9#"GB#~UySΨFY衢Q~=2m#]/eo}HS)%Ylc?zr΋Da 4y0C_f<Phu|I3 E  ې4:85=?y Gysh~zASEɃGyPyZuP@/?; mEDEaF8Jr<9q8}q@}~(2[IZog:苚p7qbu{Rf?fR?Ox~'g8 >i8}駔f<F ⮧)q~i8Eb)?Om)&ן u^9w,fk8yPo SNWyFα~zsrf_)xFԛZo_~8;}Bǟ= T?t|zE3b?蕖Nvrn=qD'SAbJdowO3@MwzPﷻk]u5 A]0r`uc[]>3r0"G'B'Q#G=~z*)ЩT?Y_ϐsj995-E?TQOA9EFщ秨SL:S?8YׅS" 'CP;=gt,Ҍ9=qNzR4&YoQ"^+`8;>7룟^u9S ?' ' q:>ÞJt$v(KsrGgaO7n^YoYobp#SZ*p~/ts,u׹XDh~z' LopzNKV?EFщ`t2~ 1P'iǧ5FԆZo%|z碏iO[0V}|֊:>A8>)j@Ԋگ?)V0R]ZN 2rނGDgai##3Jssr#PGBGQOz;*ցQ4?$oGs$lON9yFΉ'pra9"xbޟ~LJ.|sa?8`9"O9gg_z#2)0 NoC8Es݄QHhakH>h8%0J.xF  r":>4~gaΟq|9~o2n=~? z7ESw&I#N»S@/ξ$auYoזg3o@[7PoCgm#6ߊalޢٮ;T?ok93u#>8\ě?vN͟?NpQt)QBdq]c|3El5<Vhalޤ9[B͆ 7CoPf0NoP?iܘy'F??NB2u|gsn~~ I\d sL?8 ?%7ـSwgaΟ+=^?u axZ8Ysost`OAOSS:>#z$XØ?Ou{pJ`a5g(!܏>2L?q}xF|n,c|Us,gGInfIOȋx~'g8 g /<")y<#zG8I|0NSuSpkoqy@<_$ 8q~ d]e!%u=_%a2(fG"3J0a<9#( Q }4pw)'o89Npr8 ?^yEfݡ$.29w SjTy#uFnzC]I!s{"GwCPܭg#{Qܫ}T?%sK8;䔓7ax~r:>0u|7~%|m0>#ON}Hla\5P\1r1j)r1jdG Mn~p #OA9EFщ秨SJ:F ْ`yIX1j׿AW?s݄Q@`c8Y][V׌zXW۔AT![L[|/ 6ZpC6gc0J'<'}pzD?}p>D?30~0L9Ìa3 aD?3pF3 QH9E\ED?c3pƀ31D?380ơx7~xLR$Ù$p&ǧ^9SN=8D?ӕ3p3D?33L0ffArFigr"p8>KpD?+pVD?*0VVl<59y G"*"+Ь4v+ȾI9069ͦ٨M c#ukCu 0QNCxFd~+\Wa 8 \ӀZͥ٤?0GB~0W9^kiL_sG4!|H92iR΂i̇P|q|WP|ׁ#MN3c5>q>$}b`O܏"37Dyʙ9>q[}gA4C{c='mr3ar٩]#Nӌ9sP!۬4:)!'DivԔŇİF֟D}H9@Gؠy# ̦3?rybrDg$)_i34uT9 Gx~Zq1aviԁ0`ڤ[4[<?mt?mc[Zk <ϙ ֟Vگl,ie8HxO;3P'p?i^9LiсFG0::f~|t4g4BQZGGciѬ F`i`t Gs|Q~8]iwZg4O>qr sTDizc42,?Շ0c4qk#3F#3U9uaOr#5PSN>AjZTO?u:c]ax~F(<'a8#*p~jӌqjG~?\ϕI6O!sT _j  L gcOSo`lj6S4?3ipY~>d2i~NX焱kB@8-2a,r⭧*pUOub8Xl "qjXLCbmxbn3LUN]xbGSȩE A~P},2a,t?+gz'ʹp'p΂ 4eZbY#}jdr⭧5sCV"ˏ~uqSϘ4y0&CkҌ9¨x4yN jߤ&A5jz"[*gco}|ꯗİ.֤^?l|-t3<~꣟npSWsQXL?2ÏO4kaOyr08mr&` $@a9ќR?3Ե01a]A)t53 l2ۏTSi?17aCGouzGx:0t,N~p:S~rq#qNzW3~ S¸+R¸H3Hys+R+~!0Hy f cr1pq|[903V96au758 ?i83<>̡og(8o8-#OI|mo~YwG!QO%G8:iNLw@C{h~"v]h'O%tԏN?ӯ]~F#;;gw'a{7ig&83 ~k|O.ޣ9O|;o~H@mJ ΑR?O&oIܩ_{'A}2͟uu8zgh~*q:dNK=ܛ:}zAsud.6ηBh~*q&s58WNK5wjܯ_ۋdGzYG=#J8ڍnia1?!Ϗ;%8ڃdO%@vG›?g$_0}BsAVkĭ]n#_xJs8NKtM}\t?t Okq~:HSs<8d8-_~o~Pc$YRc8Z캤/gC1i>Rwnr*ԋG>YI#zMGvzrO%t#[o8-U5O׾Jj4mNgD^ hmT\ hpFNKx2d!sc{rʹGˑ]t[%r݈썆R?ć4>_>;m|yDwU Vo85\UR?/'Xljyoޛ|V9MdL[%Λ8zW ~'׾ZzG>w׉~ߐ>_CVo8g 8c Qo?h#У4?RNB=_T⼉W}pZg\m7B6 9| O%0p3x#Guƽ]adX~ }dp1߯G=I?Wbw?A'X ϭPIG> s:B]q+TqZ@=q).9AiȈGio`:?)x՝{UřWEThlP77@\5 :':ry2#@ݝ38yµi#3 DD5x^" QPQP·>Y8ON9?GF9o?kp,z MW:yǢT?V ϟ@rV?Xgr ~V".zy<ESr ~q#T?)!y@9˃-YRr ~qѽT?(gasE ~+g>⢻~nW8Asrf?".M3C9M ~)gz3 qtIʙL±h2-EJ/Y/8F9O ZuG[#:xz>:ⴳMN9GEae5(Hz}LhO{!yC1#"Ώ8D8 fݠ<F;P %8L?c?5; D#Vc9cmU8F{D~j[8TiB|8*wV紳k\u\?yp"a\?rVcÏVt݄-0Eo#0qъ秣rN884ʶ,?r~ɷ4q"'뛜:m}v rD.f]9INOrFatFѾ\?G$ ݢ}o~i~3އcGcT?c F\TXī U}Hgt% ќS[94;$nf"N 8ϑ89+^})'qշr0 `>qxc8=I0Ntb};]}HcxM4F9U3ԇ5Q ַSx5ī98yCĭ󜜭9N~h1n_9iO!a9asօ8V$?8Gc8ьD-^LtCG?,b8 ?2>YyNl*'q[N9 #F8>ys9Q#aFX CZמQ u^}Hkvt5u|&\7#œ?W+gD\9:ַzFy:c#sW:weX'\q~R].8V$GSq|}s zUeq>^_C4]7Fؿ]y}I닑ӢK6XoӅ3Pmpĩ@"DݿyΥ!1!f}y=8@A`j*?>!OF}HE~.R%#K~.Vh(\8!P9*Zιw=T!#O^ǧy ߛ.VΐO^.2)qq=&c0t!u|8|hRYwB'\FjOgq?_诜ΈtO:>\\?c~r9G|d8Zd8Xdt|<ϭ?¸~8 O9N"OIy)Ҳ~8cYL?5Ntou'uo_ԇZzk= 7jNZyo{??p*qt]u1Ar P?pQ}0O> 3\b}UOW}7zbO%.ϙzLx9 ::>gu;'\7 sr ?h9sO#~\=BQr \ԶPOja(OA}hao{Nf 硈SNqƧWr@ԛo4ztEgPsV~2'i`tvJnpDPAgͷ.!ߺ ;i۝Ls8'3~uם #F&n|!#Ot aT@]~*sJt O7ta >OI8&s9-::9-\/ON9#O^'yZsp ΩXNNw)fYL?]Msnf]n0)]7aTX3ߊ9"^}*'qu4q~sgd<`Z.t-Zu_:{*ѷ~:hOn'& T?88 ?k^w8YDχ*'qHSN>nȃ'O^}HĐo&H]NP?Q Cړ1 #>8Q&9I9$@''TtŘ?}a}be"N$RI|sGu=8T?GjHxiM:׭=樈''YR0>-16#%}c!"NP߾B\>3^ɗ0h^80 09"c}H*p92p@<~ie\MhM?uuTwrVi~Eadx~8Y.x~<'q2%8 ?LK9,yEօ_ k{ ~@m}q϶L?Ŝ}EO}'h-s-\FjON9 #F8>ys9Qi@)Q ~GA}HY9F<AS3m1098=i[zq'X vgxNmK\]qr4uG{m>){6 Dͷ~|s "L?8L[GA[~>v[c+g12Q^3lR6x'S@8#O^ǧy (i5FA[h~DF<ƇfMϽF19&7B!<؈h#ulP#kGbj.{UyGqr撵 }#^~]7_G߀^-^Gٖ駘ZU}쵈E4oEa^puF_ѣpuFǨCxcaix8FއO8F<'q=OR7[' y0V8xz$_9ca},#o'i8Ce/VC8-uB[?cm~x5+ZA]7a,-3ߊ9[V9#Ϗوu><#σƒEc|i |geaϝؼ; ͣ120q "L?88 ?' F#qևhON9RGN|} :z~M]O>9 8 -ByN^/OV9#OF9و#Ϗd"N瞈r-qq7ī1ׁswgAK9 lsWr[I'k~a~veַO9q,D*ǘg[Ͻfsyz}gsm.wBsvn6+I멫;Qف+[ SHuaMw w@wRu1oos7\esLv>dD9&'{VF D+)4?g>1񙬏y?YD2AoSdԇ#P=tmG P#OM#Pό$'8cd֔iqx?/N.djҽsGax~:>$a-Dy4h;#F P#͏hzsaL~F<aLQgJsORmrsy dd8ZM%D-N8V$7TD?Ȫltݲ8BU? #h.nf[σU-5'.j"Njy͉ϷEyh[ 1"cm~98 ? 'h6F=N.#g˷ruFIJ9"*V!eHeD^FLo*ϛ轉D?딳.CoA9"6 grD-mAt nH`7"rD~wD?s(B#M.i=eZa/# ц秣ry ^=袜...D?ݕ=t;8݉srΈ8gs8gPN TNe`T"*  RΠ  !q3!\9Gˉ~)gX0pT(F1y0XF+gtg4k4Or"N8UTǧF95pj~nVfpnfIʙ$0&!&Ar"? Vl̎8 lSμ3y#Yg!8 YHX93QEzkV0,g}֟c=яlHT8xlLTH\O7MGxMǯ۞)Z}H[8z6cl"Շp95RA1ArZ[O+gCz+yZ|b_{Ζ$Ǩx9}#y5I0"8*g[6+zrGMNcG0v8vi>3vއM;}" AlW™8MȁoM}l8fۿmUζ#Vlƙi/UWV_'ikD&?rF1!#7)qQ94G #3դ<Ǚ ɷ)&<֟/UƗVG~/Q8L1L7g1hk#`5ڪim xa4B&oTNS8F[i!39mGhzF[朙"n=sZۚӼ'4/vrz"jO?h,^<0q;( DU͏H{rctϸ"θ"ύxqyNDe鯜G P c 3P} J\~|g1_iA}H[81 ʩ s|jS85ȁZoou!jӢ:SsTO9#0YxNӯƤScY,?W%n Oyz"í&(qu:D~ΏGgth+"?u06meg""~>d"VU4?ǯs¨TOGֹ#+zPPSϑ6>4"0>H7Cښqhfqƨ2ƧF9uSx^}4Da[֟ʩ8˜n3M9#0Yxδ3ƤScY,?92Kyx|\qʹG_eRʲ~9UEY%z*e.#˔@VP c9ˉ\}h)׏w+q(ֿ?X>sS܈k6wiLJˉܯ>S87aBz-ZnuablDg^^BE+^~-q&qk9ԃדt uD_'ַ#ǨxS9"? /;M8RwvF;S}H{Scīrqʙ8S8¨xkS8oo)շ]qۊgr>8؊cO4 r1k98G rjGo#SeRT# MmIy.c!K9#0vYw8G/~:t|ьx~:F_ՃwtzzކLsaTYO1#Ǥy->>7(2Ҋo]7a/q|9`Oii_!F\qo?W}H;>\7kxGߨ 7[or}98ߊVϨh^3ʊWFD1ax##0FZmd rxΨ(1 Q?.N ~&pC>͌grj1FH~&[] j!^=>FFaՃq1܊Z 8¸֊[߄smĹƤScY,?::)PG9 û?Gx`R.3 lq|UΒh[νV 1?М3#}0>}8co?X9K&%`,!4ZR4XѺK -Q~4;XX},8Xlūo)gq}V[}*? =C˷Rp&3)H>CS#Zh=>O 㾋E~y-xO)N[pZ"6i=3ɗW4?Y_c+[O)*V"2g ^cc0GǘWh~: ϻ޼?A)ũzp8k#}.Dva څˏqdݖ/@ێD`c)كvDGr=6DanCGO&DmFo';?Do3#N~~`\ ?0m̱PRt%tr.5ǘ+M;(Jp.҈SV[X`Bi޸إkovxo8ǃݠ{'+yǠޭCo%+ES},gXZwP-5[)ZDkb)ũES}Ywyz z6>Cu.>6ԼU|+Ź 8WD9q",?7#qҖ6{4?8w7 Y\?7WnмkiO@Bf1q?xZ{ƱJRgt)׏Џݭ{XCGek>VhO)_[ڈSkǵ}Fwyz0LZ~>6<'7 g8"#vk{fPۧ8osZDFr3}~Ѿ>h?Z@Gߏ?3Jp*[GIZgwsNPAR/ۋq q'z {#qER{CY/QƿOlw-?+imn%O)V6"1G~z$s 94#q5^S)řTS}^if^ނB /\6 }^7WCNè׏itcS4?ב}uFo?b)ُnDwGr̸ٕu}gE5uy"Wyu%A)Dp8ԩb}߷5)o Vѹ['/ k::hQ`!)f=)hF$$f4%VxXkWݙ&lDHJHRS1CXǦTVͣ>[ H!tRP,-pg]g93w\>8 a k|߇C3 bX|,1fG>|?zĥ&Z3/ eWV:j^÷Ῡcg)e\|5Ձ٢8F9*VYPeDRu?)^j_g g~o}#MūƞJX.0:1;I,҆wShS C.][\WTܶ{&d%]_`btBLD(C;yUi^ژl. ƫ8Λ+<¨Y547]1bY6zsrƗxZU|%gl`6ؚ V0X`y 0Y/Cؼ6 i],N226ī.j?d~Y{hW #kQWYyEei1"p7%i Ϭ\' _,RV *ҭ;K7 |-=AG:Rq a"߸(e OEp,'yU|'yK U%zU梁ѯR2)=Nb?k9a1]ޚVWn[VJ]gH$Ey?$]섳lqIZ"O2cBf RT99 %s3ճ.UR3rai?,4{ 2Gxٻ L=j{A=Szډ3E+vV{ё^4#E:fě:7wND鱗ƃjM%}Y ٞo4wkwq55J2Ǚg:_͞|ma\v_U>b||`!ן<΃Vm-EL# `H(PxJAgu_a!*(1hVQ/$`qEP+<  )"qF0d;DJ"b"{dXu.VWiRZcBZ-4#wM1iu*63SI?CZr{b^.;ʌ|f.Sd'rSqZQ32R1jE΄Ȋlnr<=VVCSJ*U.G.FW} 2)4Y[3am RlZ83T%3[|Ԣ֌Kl&Wg6\Ŏ!Bi#P$ gpi!wUFAV$t,1]C~?uߣ4|`!dkN7b 2dxS;KA19BP؉v>06Z%b@B`J{?BK (*qw…o"X A&-RoBz.5c%5)BB_P0 I '9Uqlyc)eQTtRKNtUGᛪ?9BvB"#<\c}<^]ݞDžnPSg.:Z|l 7#${ueg}#_g3N lqg|$y\#dhCg(P=|5v5̊"fEoVcE!Y53+x@YP|9;])moySzJ)4)vEX9n% ӕR9Wh7&yxv`!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PenW X3$PNG  IHDRY'gAMAIDATx[v6(P Ě9CF՞8i&<_iY/{?ŲiRPhO+}S?7`yͿU s48BEi@Hjswɟ;ALm.LG7u(Opht:.G?n`|N)E 0AUrk0$QvHpDW/ZPjۨ?;'eds^ PC1`_$!' sJѓ*'S, .sH DVa= s*MP3a. s*lIP'a.Z^*$ a.Z_עRJ>eU|N)N8.\v r:w(J? |>G"oZQC46hj5{6w6 aAP0. sn VaG;M @@vm零m+l58NInbcy4 \`tɍA']`3\`tG a. sUJ']`m; I1`UTK_F}s#<7w2@LJrC1`%\KqBXN @uڶs4i  ZV$o-'!!Vo%h@yZ+p{\`>a.P^۶)%se`):-aYy6I"397tZv, Y' KЀqy`XضxU-_jsh]>6Hm.L[WYAm.P(^% @@,q Z'Y6a Jr/990ql$a.S,.L0XgZ4G\`Ey*. xL @@6VԶG+CZvq`V5]Оd~~>`AE g1ʴm BeRLbaA<4M*|x8&a. sR @Z8 a. sYyF9u]#=0j0L6ji?n{MWe{9Wa-/˕v{-l.mӿ{`GrͅP >}~~>oH2YFv|>%7a8Z&-@+lIJư`0x^sBm.mu#rl.ղj%;EX6'6T4`#BQ sH <'v!i0v ,"]G @@\|D%&B0\/] q%80mSJ@0-P-Ys{'mo00򥌯AE x) #;a.pD ξh0,AR۶˜.@k4q+"~읤U)Fۑ\ T@@\@ Y&̅/ 0pD] r5;LQ۶)%MIҶ#TK_F}sa3/ս#_ʃF46h$WzHovDXyPRP˅6v^OSzB8/ȗ7Ms\RJ[۠R +ڶU8|,}8Z&-zr1j#_ʓ#^SJ+QjJ,w})bKyiMC?P j)Z Zf[|]I\YdK1:Å,Nj( jֵQ@@L)Ze-RLcKyo䗺Lpo,y†ϑ.P E $ A E6zr5ͅE,^ ]/e+҅̿ *Z a.D4zs!i,z8a. s!r Am Dʒ Z2 YIRy=ta}s8y\`qmh*6YJ\ijK/e_#&ߙ1*Z a.EXh\X؀"]$ @@[F$'#_NG>mAuU-IuZN¯:/P#GtZ \<[^@$jsaB^z,^KH$ qwA-'N CI-R|)Gyw?Lkc=+҈A @u]+N sHmVH)-v:-PyyYF#?pc8`0kr a.ǥ}cZk\rP<|}KDbwds>#^h)F#7O6o.m=Z;\r̗tZXЦt: ,}D0M6jYOjF=VbK1O^)yvc-0h0G~)F#<7XVLLztҥr\b> `+ҥ6\Z-^N\BQP Ҩ0lK9 P}07X(bk/ ҨeKCڶM)].҇OK1Ex|/Z.fa.okrĸk/ȗb$e3l.o'؀V)F#,7j&qui$ a.cG_}j ]$eg*' a.V`T/%a.V  lF @@\jV  l@KRڄ$̥Rj@R#}C+0Uu]׌|}}}}}=xmmN-}||mJw'Woӆ4D9)} Vij9Wf.Զݿׯ_$ݜ___g~aJiڮ놟ǓgsiRzN45P|WC9|tf?e/Er4Γ#_G^l.Ւ!=?W[ߥo 9s}_YϜ"]vqzorKwmB6)ˇȖ4awy:m|yR6wk*nGBRoǢmߛj"ɹ__'!_ScxA:͏t|>{c?!o{67㏨퍻kR@@s;O?v2<ȜEM^3a$/U< p7όa~wVqY͞]6tw}?i+[cp;nnp 252Oǟs׻W=SP3&ߦg^8w=qw OahjPc~-8pꙓ]n0ո~LܻnJuZ}|<^ϴh;-#z<A{s:*1̽ވniM#6Ziq{N6qA3MǪU*n y򏽫Um޸g~NuCZ>^79nL56loH*^ *S&iF!ߍ߿w{9'_[ >癡͓^1hj)"h҂#j|po.cF#_̑R7CP+q=xJ1R|)K>a&U6wJʯbK1ReiVʲ=Jo!6RejF¾liR|)F#_z#oQN)Z[?#}OiT}#ҎI65}~~}?<ņڰԄhHL۷ b{MP69Mu/ZbK1RJ-Ӫ5P@@P8MejFBqژF,ȗbK1T2h,5,?5@{Pw|J ǻhdsE^b/bUciavV|)F#_.FCLh!>-`NܰD.(Z h$q`3Һ5]͍HhdsFP?ń sw1t6MrwcH6w2ɧ@؛۞ ~>SJ&fW"̭ U~w]wOKS@@M%K=4GcK1R>  sZ2L~P@@[ 6o]W32{@___wܡm3/vx^]u]7a-4.MH @@jiav߫_[/ȗbK9M\.zM)Ng6_" =N#| <_\{ s;*i˓6Lܽ6ׯ_w_ewӃ#_/ȗ2?̽{z=NAr_o-}W>Q>oŨhjZ6GnǂMȯbK1R ҃*?-b'ĭ`7<R|)FGG8r.uPX$wG;`qx⮒1sg>qax*C0#ixJ^.}/Rj6>8Nm'c3jYh6*7ӈR|)FF>?~$=I~/j%`J`.R63}@xaI 8~w!ړ>??$VB϶v253 1c4b)F#_/e/U>7Yøqc"n2JLhLpϤZ{Pcxx-?87eo&yE#O?U+@R(]f14Mۼl`|)F#_SBBdoR@@L(Z1YR|)F#}s`0$ a. s] 82 ^-\M 3)Z a. sH @@\0$ a. sH @@\0$ a. sH @@\0$ a. sH @@\0T濺+}P4}ߗ>V4%q^SJ|>>??P3\egtJ)9uO4I W(pAs_q̓h XzYqƒ~ _93#;_~ВSLxgs66Cg ],)?0n'_|ucwh}[{Nd{KanG1P@@:-Ο,I ^3FP 27)? y p~*$t p8f`s'h,0s3Yy)p8Mha@6ɝY#Dߍ +9i``#RpeqIj.z1z`!}7ykvk>+`0DPhxkA߼Md5i&۴4B=xRl +bObabSRs(I< c`8/- gwofgDſ`[E/2&GZ-92F:y 5sqޛ= hIr׼渒ڣX B5C,zm;csbo3Z)mxKOrf#+333#4ޘ/Qo xy4ۙf?~ٕqh~ߦ~>1>Nq}ED3?'bҜoCٱgQ;?|_ذ|竚&)9arߢ[xʺ\n͈9?|C)N ^ݣ zC4>?@>`iS=Ɵ0L"J;/Kia3WcޅC]UVˀGߥNև7NyaUd//i*>uZ*xWV;+<|\y{ ڲ>?d{.%5dEgFT޵%u~A`!W?_um~i.-%xkAߛdӬiӴࡴTGmxmzhڔ-db rӃBU'v_fk|O[bG lpS~o*kNcQH'4ɢ.:93?b%sT|0O/'`3W곺ޅ{?S8Bg腼g%%?̳i=.E`!t ݓX0`pxJAg]AP- N1 hi $`Z=`Z++>Hlb⒉3 ̛W!3T{E}tj0tSw ?uHiNA78NmkaMB# Lάړ.|dкRyϮ%YC.LUTӁЋ0V{k_rCr`!"GES0Jʤ2hc@(e>xKK1'Yk>Ԣ,ZQDOTPxPPPAOEA]AD{EDLҍN .OfI`8fZ|*qdm˞8k=Ե1khMF'0D)bɃ~ `S&Lf\ڶR\0g%wn0~aw›gQLbx(:ǵy@s.a"Tэ<8;BTE}6_VDNh^|DJ#|t<Ӡqa`rOJ!s&ES<_ج=6x{=(<FI-@~j_vQ{P-|os7J_*VƝ}Ktt_cgg:5ojw,cjn努Ve_/{+=K1LpI<,59R¤`!vBPQ'7QLP`xR=KAMrKB?ȑBl & V6*D8%BF,$WlLmaeʟ"VZsg Xn7ogv! e3K$D (JsZmf>'-LFOr:E<8ZtqM8Zk\I1PFKqu6 ͅs/iMkhD2kc845F->oP8<ܾs^9̵3)5 W qɕh}gu~#ןZ+rV$yF5ӜTSg7Y(H狨V#j?#[kԋU{`(8=Jb`!}ny3Etk#;t9\XKxkAMHflD_QTVh)*ٶЊ݋Ixaޔz x|{PЖo&;oA|0h0~r݀(k1Q8cqeKedgM3dZ-)ȂEQ!Ci:I8W6p`|;^0ywFE^x򦿐ԟ2kJ"v-!*NtLA%_7,;\׹2]'Lxh;Fc/廻)MS~ЩXX-ofMZr?7ZrV!Y?!'Nh^jW4?LȷTnTMhVwvS! w`!w-K-F: *MAx;KAgyj11EM PȩI'"  E ϝۍ&,ygonx^!kq3F-" K3'e^Wp&AH]X0DLa{E|5jNPhdn倣z37w{T~] b~?kMQ3fsZ?D;f6]D.<)s*s8-GxU[۵%_]_W'eN8ZғɤВJ->iJBэ< y\C'%!R;u+l a] Mi]܌ _]aogHnφ~3&{磻I#sjMtKq[) YӞ b̐a`!cV̫Lf/xK@]RHS*t( *V7W;!b k@p..$R㽻UPl9r. (H([Q;-Jy̓T(q%=lWKA]UYӶ*?ic,@?]sP?Ǣv]} J%e >*\2B\\r6 13N~qմvE%;ws9gءhH*8 cdscRx˹󢛀Orm)U%D[ybd'sz!՚Ty.TOuFeyVŧT#CM[||0MMC M1ԸeO`!Dw kVF-V 3 PFxW_hSW>&&in&M1ikDm0Y:(2,\Lv%cfTAADDd(0އ'I|C캇19Inwu)!{n)M0'|5Ư(w(N&ŝ1FA 3.jPMΓղ=2AȀ4cBdgk2!U3#K*\0b3ϳ+r;v;_EnbtO.vK.~㢋ur㼋 ykOP?C[QT/dPA[P-fRh &1v} _ggR1ԛB)D05!֐_y[,)j4)UGcqYVxF&.w8o+9\DTou\Qq!jo(ؘO8j)ʻ#r+Om*<*Rʯ;m OX! wB}+ ;z+ _a> /3({Ff1a?Lb~$W s,z8F]?"W9 cW!GG*Nd};ǢC큓âWunѝ1\GآQtp"QZȫK=Q@Ŵ7Qaq?C]bućW|eߏ#~#nNʜ?c'g/mibno>?S=~K#y[%#Nk|SERMex'Sb :Qoď7Ϣ ē\Ϣ᠜oQSM|}]i}!*G|Z՝Iqlʄ*ɏxU蝬zFO?C..QG,.2&i?tpn"r -ڊw^;.h@? (ckK˕7y|"_Fn`! Cc%%K`% 9xM/CḀJ۪W}n% KMHzUT ;XXלvAi|Q\_gفE}2^7a6S}(H>⡺iP})b?4,[zGEy9y߫t'od$]qͤ{}`!n)D|ayzŐ)` @VxRJAct`!'e8D} n|{ `-WFhxK[Ag&3ږJ@l.ӧsEx$jǧc&j.{ Q0HT5WpYW};!_,?OySO'_GD|0~(|wа_>~ɨL+^LlT١u,՝=s{+vf8I6>)U9ݒL;gSqy6s=*N8O :_Η{Soj8Qk<&t^Syt>0[=mMVw=s];7ﲙS8IO}\xZݜ.߲t 뙺3KϹ\|OAGœsGܥ/uނKiuM|UyMOC~b?6gM lڶdu~T`!KFk㯒X[ `1PxkA{uS%wK  `pha&AE^# *XZڤV?@b:ofgge (<9Q %IVNr0UE0 ܕr2L3jJpuesms Z7i?SjǓY<0ꂣTDV/Qw3͍Lʮ]h|Gm_.VtAgUQ}ůI{(LS9RyU3{i }53nTyTګCߝG,vty?tqG|'ѫ ZaVF}s| tEQߊ>ĦPyw/l:3v2x}RTw8=}PM-tڛN(?[x`!D3U^ɑrR|2 @@ dPVx_HAgfםus=μJM/:"?uZ(ԑADP/]dCCQDP "eEEEQ`еsO}:Yw !BB2#1E85$LZ ;>D\9*V\df19f^W"U9)6t?E(@LM>}d*q z"ry\G&_ u=_-q.&8H#~mԟNCCn!83A=h_dT^٫Gs>X)/CzAfM>w[mޛ!9*y@J;q_񙠵\opa V .ɴ=b]p2`! I bΤ q(+xO@]u?2(!`E!@ l c @ DLL`H( rﮭ t}^{ww o0xШoQBcٞx>"n\%[$)=:$ '.>&ϵ~ ? VJ[ٵ*%s}P 3 wcƉ ?Oo3ٌKW)>dӼ~.SE3Üq}Jqq??OIIe/dCf13\$=g5欉wDԉ?'},|/;Zޑ3f|G G=|)c'-0 <'{"W窕@˖Rm=ChGSφF_S~G{~\8SPso^kf%N۶|Ocեi:[Y & {?3R!`!,Yt͒ k@`\xT=H#A~fM Œ"܏ *5jqZyQ\P\FRb+2`ca!b̛q'.þ7}2H`AȫE$#anbtv ui\NDO}QLJab8]_dPW:1Ug_:D.Kqe,dWqB,Gf+찻tkg#Y٬uКu$t7_T=F#8KNzkϕ2*%NjL%q$.b18] y >FٸHY&%xλW9W .Ґ8HȌ~Y bCMJ;ו0lh&},yRV{uKn(w=eqW]~KWv ۙWW~f>޷ﯘ trt&o_XR9e:6s> Bg`zU9_`!Tp)D0."V\,`_EP"xeSKkQ>̴M&HI MltBD]Yp &- H+K)]IVҕ?@Js\HqOB( {g`.|>\J#EZ(FF*(,;ܗņۛW\5NڛH)dfLo40 M{e{3is|;NZH z8n/'[k6":nⳜ\\S/%~57G}-AAR"`T1Y]dvuЩfo>y)"c(=uMprݟs`L_:6q|І;R }'2͌Se4oN!?=RǞ#<痖O9Y"G>);'gKID"۱]ݶ c*>LW#D_`!wq_plf p`> 8`dxMLA?`iA+,UI+PEJM#$M4@F$1Jxedc!Ļ:ofw&6yol> S"$6 !ɒ)4Qj)GCm] 4+>z_G:18XZ: |8J:M|>6zYH 0ȼؓ>6 Jjϕ+e2y8:4S\!_r ͳߜAvώf8ws漗ETmc _N (yOK؂s dsPh2?c?Z+M>P&Q#Aa/ZI+[Ӑ+.Ս-hRWJSZ?'&~d`!_\e۪~Ţ `Ho*`'-xV[hU>sf6{s6ٴuM6OEi L f R>( R RA1A\6P#J$B9g?},9 ! BTD-J>XQn˘Ϙ!pEOݙVw8huir#|9ZDNFJQX豩fXGyAD_{zkz՚!'-TWPX.Ԙߊ4fc+,8vu(1bTtUszԒ;\<vxE4USvPztqOp|۽e- ۛZ  O?/n۝lLϾΉhݜ!E]07#Cqۻ[vΧ C@F!_/2>1rŐ|wR흌O; z*g8|AfHb%Lܺw͙O =cMfЇNCgdu@!Nf{|BD(З쿡dsn>PzT!5'`!1#&h{"O2`@ (V'PVxMhAߛlvIvIҚPjXћ`I ($vAă҃ػ^X= +~\13Co/DkDk7#{zmP;_:6xby\zD4YHZ;?,J=rU?+AӒgt5ѩQYzV3u_@+N{\_.k#?)Qɯ:\m 5(.5}8{*2ȋ=ՑQ*Z)OFIp"O^%foCxLzBZК8Őlo${ ogѓ`!~ԤЊsJL}XDI p/xVKHTQ>ΨwJi 󅎏,\ 3*AN#(aԦE`P p%- Ej-&*Ms1F8~:xTP Gu HbJ|33t^lj% QuVt5^'ɡɱ$c3 t|¸c$ՃҊDQU VAV8hD_Qu@-:GC ƶ&VZ0(: qWJ7qk *]zO;_90$V=k4h69Di)ՖQ.^IZp=^B\L\LEؒ/$`K%C} x:5tkdR Des';v4FgG:bCjxq/UA9u# ,9uoU@H_VZ&m@ZF/u3G5YΣ[|<}E)巶BUHo _Vo-,~#M癁+'ޥ-F/pqF7y?2=̛P5yU~ ?a0B3^Z6ok(gnxNBY% :y\M2ruzxeBjc/cu%/ O#;,ŰaدL FWڝ:F (ID2@>e \ `!QRYڱ򧂲~@X xJAg&39j@S ;%9䌊,RZ$A-T(xQPt | "DKkٻ%w"_(_5rIIBNBd R) Ca2C06.Yk r p$wt#gwƒ1O6Z 6/jsOR,MH #]6Ӫoj+:#ql\'~S֪3\b= UU5;bA5 U}F-0oUnWMK]\"~οJRp~ue: SvGg>R*5!t}O9Zs/Sk'`vIb/(q~;XD8?Xܩee>kc{p4፱VsS!Zg Kٻw`!wj`B=ɬҵ4b@$'HD ExMkAǟyf_&1iSOKR[RhSœH H@9x z\0ū@Cjya&%{gv@ en$DDi*"Ӥ""3H;Y^{XF^ Cʓ dSNc=<8]o8@FdaB|4F,6eFNƾ;jY^+;DŮ|ѝj b%uHo%'[z8d X*%[뼩 5٧|- Ad~7柝A(O:yt\Y'e_.>'=?EZ 9fYoIMdz5狚#wz;jy:Om~S 0E |A/ĩGV0[TT`!x ;2mbd2xDFx @C x}L J)/m =Qvb4&OlDW9I%R 'հ G`!T*_ba` %(xMhA~fk(Ei"E!xPQS,^VR,~@/B B=t"E=I( djE4Lۃξ!b0㬍).DZ&RV9)q7F]fv ˱b~ճg1,16~_r l(8󇯗KSMĺ{l>w[(^P4Q&Wm? _dP'L;tht?45d| @ 3Qx3N/]"_YɢaC[}޺ H3YB \_-M`|A޶ i=/T[#{U#A)=d >4:A=o63}Lwg'U%O+O/[^pf Q?~sզ9CnPUi/ҷ4!_x |WſΞ.9'}KMO)~Dn5ՓO^d3EvA}D~O?"]U=+Iv .&gB7zL#ztQ7,MB}>GYQ]ғ;\ugr%λSӥr_!}d`!/ұTv8<`W%' $7t" x}UMkSAw/Iӗ4M? &|U.lWT*MhA".r!AY+) .JAdEDDDR0,L2g3wwn&x0Ԙdz6C4+FY)9RbiB^.&aF:r lƵ~JD7lv;M}5>,6ﶚKK7vZ6t&^LK6rj1Y(:k8vFNae(k!KRYpk%BTc*F,s3/šw4!bqл9W gڜ^f*#>(!j OQBH q$$c&>H^' yԃ߭cTa և+I AF|Bz A Q=TǠ փ@ZUx '>U>ZJ%NRw!9ޅ=conٶ=g;ݔƇ4~o^jz_ ؏;^7CyTmi[.[do@lNnZsndlylA{]nx`!p6( :.!ɋ@ _>xkA߼lW[(ؘVRAkzR (tSa+X͓JO"x Mpݙ5a|}7o ` (Ū=ȘaicrpXy]=N>A* mz#hࢗ;k{(*d!*GQo[Z:}MQd[=".&JV7_3U7N8dX}oYrk'֦׼PINA'^oH&bI#NC/g^oHI_z?BbRSNf鿞83N~"◨mW޻}>zppV1A]Y,'4Iz =C:^|sT)_&zU󅾿?;=%7hlۆ}}} 'B+=ꈘ?R@|ZD_u]ܐ:;b)ttM*_b-^x=X]EOϐD`!:ÝT`q P%xڝS/A~v[۵M8 (#$6t/qp㈸9898H? Jy3D{ya0(>Ċ``!cbXqnQhFXWb$FMc^(<xKT@gv 0%fan iǤjMfy!9÷sºib%2dab)6^g>s ̫#_J%9O>e˺wx|Isl75j"%Lol%KgW/g,嘵)\8UɾOA Kytlƃn}ĔE=TFӠ'9aU RB 0;e35(qag$R~+anBD62ĮyPYB6t¾ &k >"=2a6;`!C:N@Kk(e xS=KAGTbS3hNL* &$ VVb!"66)-ڈ?BS Q 7YxcQO~y"Jʅ< A%&ܡ)(FYuN1ΤFp EŴiaWGLh Ͳ[/;rB4[4wV `Bx cj̜Iq&5'nf+#}ƽD"R^ DFsoڅfym{ Z2O =ČW0i`5oM ߶½~W'APM?AχN] XrZꎘCC[cBp0'y7Z`!e}Rv> ?YC,@_E8 xT=hSQ>KRGӊIIMCIšJl%b1I$:CtC 'qp,A}:s|&L1 c8p8HH#b5n_ 4!C6lhINR3V,T̋KsH#g(B!U GIWF'mC6u=+}Y[)Ȉa_<Iv'v2EDm\=b\g'US L;ļ7o Z^}ѨI1p [lwϲ9cyvh %|>_*>}ҋ*W%p3vOBR1eIQ(*3]|6ϧUHںw1t (U !b.Z ӫ7}ыΨz\ԑ ١g Vmq81KyQ/>b:]1wSu,`'Fr懎b\lM޳㴎U`c.3_U~aNpg[FZ͵j p!ִ`!60V*eh@SR` %(xMh@dg.-E补BD/z{X/bkɶbjQPAуDVуxCQH? ]I2ηuqvyoIL :!J#&>So䊄phZJdE!p^eSh K M"Oќe ɻKƃsp@aHNgxb;N}z; jzP; +P[Xt5PBm\sF'Cu:Ԙ'4QbK]cݣ-jG{ŝ/űߧb&zA~e"AyE6cP Eyz2-כ!כ #oEy*pUyڽN?c`<4j~SSM^CwS ɯ|Qٷ$;ᘜoLLF({ؿ,oåp(纜o`~:;uQ̯\Q5Fwޣa?>aѹh=h=mҰJp6~ (ˀ,;`gp3^^%:{t.7\k=R{e _20|+YR0?Fq48D9Q*ڞ)`!&G=XAm|f돊 x xTAK[A}lbP( (P=BԈ1B$ b/=BR(=>("w{@Y9_Ky3߸\ڇ6SPc &J," RI*0C|EKեUs> Kwι;{9F4B)ԃ'X aT%s &Rǽ 8$gVVV9TAl6~{=4>?=ph T NwbqzVN٪H^Q.D4_ bDt3UPITE*lъZea+e2ڦu* ,G3G4>ъv ۲]ȹ m'hh;I=v-ڮL d>՞7 B/҃M1-L#|_gx7{R_*)Lf7K,bW c#~ϖ;r?Ԉ,bU3 ;qUY*ϨwӂQ܏6Fx2o "yjz+ң1>k.*X_ WıNe2mY:άcd  `$u&kX5ȸ5pi?d 0}di:+k05_oUG>U_.h{h\ (3xYO3/3xzǖ5􍜬WLE!cŀ3~]i~E5e j 胚zɔ5O0WU*РCVxGb<fQWx4EE|e5𕙢\_"_Y+}eEr̢11F#W,7tXqw]!PQY$nf o7ɺ(u ?K_0 w  gn,7tq)?g҂Sr|]ec20ڄep^U_Ws}?08C.+'u1 %[iىJXj',ߒ'bKmfܨ||XAk Uq#7W& _i}߇1!,OJVQqG`!ו47Oo߮0`hn 8rxRJA˙\(G  b;hN^g|; ['Vܝ,gD7| >̃\y-6!)MS{(H컳ZM"2|5;p"9Eni(x(jORrY* EiuK#ߍ=/ Ry`'< ꝨÌΌ0%Zu$fK\5׹,({D{RE0Kbe+jt?=?F軬xIi5[_0=??mϟuD<ތy\Ь̈ /mLd*ˣְ6T4u#o&p&}l0̓^ qL NpLD&)fCޙJ h75RL&u}#`!h}#FIZ@X- x1hSA{.%*ZD`!,N"AL4 8HҡSdpTnXABq}`Bw{ IBh+D$=r0WFJmȗvKjt=1MrdM$PF_%dWpNy(d%W Γ,J9IJ6=$OP: qQ^AK~|x؟_(/fEcPDy9|Ay9e񺠧Sqg7sp'{ʜϨ#Pmv->L?`! sFOBJ pVnDz-S` {f xoǻ{` v6&~wQr؇aH1kG%(+-qY), !R"U1F)~Xwէ[Uկf0èa G }XʴQem(Y-^ϏA/^khI>OKȓe5* /{W_}Dl*jf4-ڐ4Ц`F<^ݲ֖pw4n1Ӯ%TbّbJ8>SO4Ûl$ h#o/wVT7Ә`Q0{F*kV#֧J7nWfJZXl.v~.6dC\0뛫0{zODCC0􎥵Q|T|\"NJyP3oL5a[g~٫?W; ͍:QO+79 >i~ȟٿƎ2wM^vdJǡ&/ 3=cP&Ah %}$-;qZP-]+*uw?{5cl,ֹ;Jx菀7g{>cNơDDNh;^A'X8{ DD&r};ȉ[Π7G?n{Eo;鵵}J0DRaq^P/^n]CoA tNb =׋NӢk< k#ܫC|́[fGFtsF,Fxq(b̜PxE|}VM@}Wy-eCR96vLw2eoʥ<æ! (a6|/M2J@x9yYx\,ll{y\Q Ν$ԕ;ɔC)=l-3ѳFJ찣k=$E$RbԆVYYac{I Q 8N;UU<u0qгIMoe25ߓp$; FjSƟaʝ_莈Lif0?3{H7Y-[{}uli-(9z|X6֛0>ƋyS5sUȻN3 OvE y;D&yZ;RҜ|Nݹ~De5(XpIx. ƭ}P+I㔯4̯m\K1}!iAC$ }DFJLL5?,׋w!>+^i=KK;ǰp ,BYdm~UYY 9\X-l.vUUؓ}\4'ͭ&O37t2 ʻє 8נ>%ZTց+rY_$(J\)sT &ejeEN>ԡ\* lJ]X(1>0)w̽k fp_{qC( X"!V#שNFS`'jPZMHLTF :PM,ѢjR*1ϒٳj@%Qi-x!"]A5d첮r;g5 S_IsV٧7K>?Z5 +R>N)ꔖ:E:jIQKZjPuQK]$UE]RW uCQ7 BQ-uP@K= #E=R]-Kz^ja"0u5)Rf5f 5y-5OZK-Emi-Bm+j[Km꺢k넺Z6+꾖OEhB=US-P\K='+ERU[:SME5TP\-jFQ3ZjPmETPZRTԦ$E]RW>R E} =ERPQCB=Q-PLK=#KER/ _)RuXK&(GK9:Z$(ꌖ:C(CQec "uYQeB=Vc-PoHm(,I:sE}>'3E=RG%%),IfYRXw)wr?9`!WS,qB"D(60UoxڥOAT(F H#QOp$H\k iO&DO&77D4F yov'EͶ3;yy&a/H>Aܷ8cx^'kŽfBZ (wP9&ӥ +*4\G!d> Z1a=h:Tf:sg!y.jx#UQ{XXj$cz|=k;(g} 7ύ2g82$3t_ O0@z)^\/m2Iz'%y$HD7qG;Nzww~k1+h#W޶EzYf% "ڲ#N3&e[~a'm:OXqO/AuJ |Xy2Wxl CT#PyOg{·tש3k9ٚ#,~Ɵj;!>6ÁMgr3#Eyϩ}M!9 mywE.?\Q=qW;.p-f54'kݻ`!.=_;٦n@S^AL xڥS;KAݼ.GrFB 3 &E$WBXAKX )R(sw|A;nﻹ%cq !!Gжm鍒'nX{(-{x򘗆bZ3bF݅4.Nlt7vqc1kTT+sUsX,sZyWƝ4Xl)\Ӗ? xu:yue^\ciPX3ColFˣAG$; _;AaUx]lYB'O7+5B$5 [hsDyK www.careerbank.comyK 6http://www.careerbank.com/Dd hb V c $AG? ?3"`?2u0\DZ"g> `!u0\DZ"@ xڕK@߽?r UACfPq Ec$ѡ8nw?hǽ0N-14-(dL+aJUd3zo,`,,Brf!`ߒ +&h lTAvQyAT),ajcB}eyΤ=]z~=oռ!41̵M |4H-9;"g}ovA6)?:/T0IQ|Ü3i0sT+~v^U'^-9U;St+v)>*?^(~m,hm{* )I7#|ٯ7\O9hIcgao evDd Tb W c $AH? ?3"`?2> A}qFnH\@ `!> A}qFnH\ `\{xڕRK@~,CtDwApVTVlAgI(TGAPQh|.^]{0*1,6v2$I$*I7e3C-<$xQqG8yRo(޵4fJE[zk2Sem;s "9 $}GLzq[a7 QÑ^[b3n?yӟq4̹c7tML% Os6KRh(OPa>K CO堨Ϻ0 pZ({VWUfnм4Psj>zfR~b`Mb$<sJ}YE/}QWZ7##|eC|\q$6azmB{7C~jFO}s 3 ; 2ߔz l7@%Xm7'2pGwak'|і7Dd Xb X c $AI? ?3"`?2gu6vNU 4^]\ `!Ugu6vNU 4^># k7d#xڝkA߼&۴j(nZ%Y"is(6B@l.zQ=^< a=Q*df3f>ޛyHXQ`#} -Q&\ZeгåOD PZo8\8dNjaݬ|I9 U=B=4Y3z9MuÞ< m^֞z{jCt.}tp||Hvh޼DT,6TFK0[[ GpA7 '0[=RI)$5bPd+Sg!ua CtDd   !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~_ /   !"#$%'&(*)+,-0.1W32465789;:<=>?@BACDEFHGJIKMLNOPRQSUTVXY[Z\]`abcdefghijklmnopqrstuvwxyz{|}~Root Entry~ FyTljjɃ Data |WordDocument}ObjectPool;ÍɃjɃ_1150714346FÍɃ@#ɃOle CompObjfObjInfo "%&'*-./014789<?@CFGHILOPSVWXY\_`abejmpux{~ FMicrosoft Equation 3.0 DS Equation Equation.39qEH  CV=()100% FMicrosoft Equation 3.0 DS EqEquation Native a_11507144052 FKɃnBɃOle CompObj fuation Equation.39qLx,] CV=sy()100% FMicrosoft Equation 3.0 DS Equation Equation.39qObjInfo Equation Native  h_1150717399FJɃ`kɃOle  CompObj fObjInfoEquation Native _1150796015'F tɃ#Ƀ,  A =335.4 B =91.7 C =175.8RTR A =350335.4=1.04RTR B =16091.7=1.74RTR C =410175.8=2.33Ole CompObjfObjInfoEquation Native  FMicrosoft Equation 3.0 DS Equation Equation.39qHt  P(x)=P(X=x)=n!x!(n"x)!p x (1"p) n"x x=0,1,& ,n_1150796346FĮɃ@GɃOle  CompObj!fObjInfo# FMicrosoft Equation 3.0 DS Equation Equation.39qt  =E(X)=np 2 =Var(X)=np(1"p)= np(1"p) Equation Native $_1150798997"F`Ƀ 5ɃOle (CompObj )f FMicrosoft Equation 3.0 DS Equation Equation.39qJx P(X=8|X~Bin(10,.90))=10!8!(10"8)!0.90 8 (1"0.90) 10ObjInfo!+Equation Native ,f_1150799382$F 5ɃɃOle 2"8 =45(.4305)(.0100)=.1937 FMicrosoft Equation 3.0 DS Equation Equation.39qP P(Xe"8)=P(X=8)+P(X=9)+P(CompObj#%3fObjInfo&5Equation Native 6_11507995411)F@Ƀ̏ɃX=10)=.1937+.3874+.3487=.9298 FMicrosoft Equation 3.0 DS Equation Equation.39q(D =10(.90)=9.0= 10(.9Ole :CompObj(*;fObjInfo+=Equation Native >0)(.10)  =.9487 FMicrosoft Equation 3.0 DS Equation Equation.39q P(x)=P(X=x)=e "  x x!e=2.71828.._1150802259.FӏɃ`ɃOle ACompObj-/BfObjInfo0DEquation Native E_1150802425,63F  ɃbRɃOle JCompObj24Kf.0!=1x=0,1,2,... FMicrosoft Equation 3.0 DS Equation Equation.39q =E(X)= 2 =Var(X)=ObjInfo5MEquation Native N_11508034038FZɃ^ɃOle Q=   FMicrosoft Equation 3.0 DS Equation Equation.39qPHt  P(Xe"1|X~Poi(5))=1"P(X=0)=1"e "5 5 0 CompObj79RfObjInfo:TEquation Native U_1151233937l=F@ɃϐɃ1!=1".0067=.9933 FMicrosoft Equation 3.0 DS Equation Equation.39q8[fObjInfo?]Equation Native ^6) 2 2 2 ""<x<"=3.14159...e=2.71828... FMicrosoft Equation 3.0 DS Equation Equation.39qmIyI X_1082389052BFؐɃɃOle cCompObjACdfObjInfoDfEquation Native g0_1082389152@JGF@ɃUɃOle hCompObjFHif FMicrosoft Equation 3.0 DS Equation Equation.39q@mIyI X 1 "X 2 FMicrosoft Equation 3.0 DS EqObjInfoIkEquation Native l\_1082390641LF^Ƀ`KɃOle nCompObjKMofObjInfoNqEquation Native r0_1082390773QF옑Ƀ Ƀuation Equation.39qmIyI X FMicrosoft Equation 3.0 DS Equation Equation.39q4mIyI X 1Ole sCompObjPRtfObjInfoSvEquation Native wP () FMicrosoft Equation 3.0 DS Equation Equation.39q4|IHI X 2 ()_1082390832^VF Ƀ3ɃOle yCompObjUWzfObjInfoX|Equation Native }P_1082390865T[F&ɃWbɃOle CompObjZ\f FMicrosoft Equation 3.0 DS Equation Equation.39q4mIyI X 1 () FMicrosoft Equation 3.0 DS Equation Equation.39qObjInfo]Equation Native P_1082390864`F iɃɃOle CompObj_afObjInfobEquation Native P_1151237483eF@Ƀ 7Ƀ4|IHI X 2 () FMicrosoft Equation 3.0 DS Equation Equation.39q8xIڬ|ИO` 7&0*<5CD1 4F)$:ap{ FVK;%wjҽ,[滔`1 v &}oN6ɮ\q{=mK͞#Z-?6}BFqʖ7uf,sq߱%⧜ˊzZa+Eɋ9ܟUP6 iE)γwlxCrc4RQ"ykWI<_$=φ)^kSD933X"n<(IO>O=s]wqxgb=_|d?WaӪDd @N  s *A? ?25ٯO['_f `! ٯO['2@/X@I xڕ/CQϽ⩯Mj&KHL"`1 4"&tgb1a4H,ݛ,s}8?rqƜR$8 :k>9D@DZh` ~Q)P|㇩N -{/P`έg3{Adf;%FqEyp _b4ኝ*gœu1)2VҰSDa)&Ơ=kLa+oC\W O0߭P/}d[N-![ת?YA#qdeW7!?&.<K=mr]G4i|CyÅ|kw+>iAkhn%Ϳ0rpː(ș-*%=șeGgs{,p&ez}@͗7L]oYDd \N  s *A? ?2&0:{D=S196i `!0:{D=S19`Љ-hxuMK@g'֥ZB?0D)~D񮇶BAЂ ؃Ń'ug7YHф%L޼3lDgM(2E ͦlrZnd" wA?4I \Ų23BibY.T/TOk)^8(GlZ+eX(vOxg/{9x*GI /GXVVnkVŲ^bUi0m>t?es/#3;U/T s)a.IyWb*8$ۚ; ˎ4GڂCzw13'Wܭ].D=l=3C^?t_iXuAm Sڇb۰9߷S}N-:K@:MUC_0ġ|T.D& ™w=L@'Zm!HzDd  <  C A"A0E&k>Aq @=0E&k>A듄)q;Ӓ!xZklTE>-EVW)EBRR6T1D@)67@F$kH 1$6h?6" c?;wݝn 79;3͜9wVjPXCEO\r/$Ry |JD !Y_ 4uA Qv)5 PՄX&ɗk5|&`57TC)Y $?00@K~>T*Kqt:>E0'&/1n75T ү /!]Z<:t<; =Cm^ОG<.u!F1l֡5N/U&x#?huvýcXͿ7XuHw>VeN}ZXv͹ZBø&8ku8ZͲ u[eY+eibcoOolf]7].u=VVSgea%X .-ee<^2W;Ob^lkhJXq}}tt{{tvCݐo0 y!o1uΐqY_]Y`ei]w1c?9С:e^CeoW_g[ُl}i`>6͚M4ú vO<*>|lg=ۀjqG74 [N>>h>qDŽG5uԟ>>)^P/N/{9e{}9=x X<NρsNwǫ|j]-u^:sN}cP%] # "3 )8vԗ>уcT`(7"m.G?v|ݝlנ|羀tBdg˗>e;~w jj)vg؆pzNv-m5ՠ/1T1T'kiT93# 1+i\[#c9W.+0}]( \ BwTlX2j5ݧ<Dd Tb Z c $AK? ?3"`?2b$pd+Qp>by `!Zb$pd+Qp>^ * MAd(xcdd``^$d@9`,&FF(`TIEAKRcgbR VM@=P5< %!@5 @_L ĺEqX@\~;35V ZZtW(+\c`01R Td:0#8?ׁwp~aU(SWB\]@. 򷹡LtQ.=CP_`[w8A|M8+00B䱄3 W&09d9>cg&0h܉* #l\CFBcG0Cw*2&"\N&;o?#I *(-Y~& NTAl ~o0wcȣ#=A^L,+qᵎ ln6b bݛ 1 ZqK+p1K.+KRsA<.E.bdg!u~` Dd p|N  s *A? ? 2Iл~| `!Iл~`-0xڥSMK@jӨRу(HJ/T-+H.ų "GO /7nR/X7,#@@OCZb?uY!8d#1$8qT1`:U#` Fxz"|-2Y;5#uZS2_[ }aZu\y  4OY*sRy{T! q>ܪunYn8!67K }uTb><5A|.Q5ɼaz?43D_ >qmہ("VGwL4]{!Ta΅i'a+ZQ#Dd N  s *A? ? 2u_\S̤=ve@@ Q$] `!Uu_\S̤=ve@@ Q$`) `#@d#xcdd``.c 2 ĜL0##0KQ* Wä)d3H1)fY+XA$3PT obIFHeA*P PD.#l&o 0U( roB2sSRsnb f0Z46怊Tg SRa``fSX(BgwJp4:`)\3qdL_DA|M87 p!>TA<(Ԅgp| 8;_'5p~ |8߆ ɋ&0h\&=2`jpL@(\PJ{\q5&W< ?E *C UIU^T,f3W+wY b/zx17Pk#T˄@Dxτ%6| #*ߍlB>\;\021)Wx\ ]` ɞB |8CDd }<  C A "7y. @=7y..9?5Z%xZ}hU{ۜu-L6S*9LtҔKYѸ1Be1sN% _!YPd4#e~ϹϹ;;w: |9sW"Jw o:k[g*ʈʪ0&}w>|d2^EQSaK*hNUcrթ@ƪ8[8;XաzcUy(a>V-:V;V$17ۧZB&:#ZͼuIn;F_qQIs$2>XLLwuvvޱw؂?îc쯜5Aa/EMԁ|*'i,9W2;c[3Fe2 Pۆ>/?&e7Z_ngM?L')ow[R/Բd:ׂ\x}{e 9>4>_W\oͱrΎ}HĒKӺ1:֊rң෩d=H׺Ork@AUE.c;pHG; Nd'd(6"=QU#5B;tqs'Pe?b,D0.00`+ |bc7 6~~w e|7SYCeҼAr淿SP9=D%CK14W[_CCZLO} :X:5/Bg94] Ihv؈Y64/Tz ѿP(6J\0z0x8n.wNyzފ楟 3=N&t%r=.:5/FTxjG;lj.rԫyNCwG 8m = nP # ω0cyRĐTKojjʣh-~*#yAy`9A=K^CV>˪˲t1>^η~[[dy_ll{;qogs0N{;=g۳9"98a];S϶*Ev3{;нoƯo:=Kl o5GHR|]Fց6&Ie~鹖iEM..anIͻ$ɾ]'aÖg#SG~-=_[6Y<#{O'f_/xqyJQ5NaDd RB  S A?  2rQ mN `!rQ mN +B`\xUMhA~3Ilcji#lVג`bTT-"V"[HEbOY-JAs=I⡈ ff&X7 7~߾73QvL1ԍ(!:"h(Kzu.nXCI :HucyuC<9q-Ζ&32DDv'DMW.u. p~ -͠Nɘs+naIX+@#_$G9wo!| +ebrnXc5/^ך W'aYAt9ɏ7n4\{|U9fir'*BnNIhL]~(Xk*~jyj*Ԏc/W;Q S > 5?@-[LϷ?}^Ek?{> Wώn7=#_!rS^ʧEhugsY #xx>[+~\cE˯ ׇ4)*z{߈[Ds"O:_YP.Dd TB   S A ? 2L[q)0bo^И `!VL[q)0bo XJ$xQ1N@];88.|$EDE@HHT<^"/t q\ᬵwV;9B"NM.%H+ZӎVzLew)6%[.#^J&q*mBl=-M&,fpWj֖B) Evz3 3̳Y:i>2a]>cܯ9܃W "_^Wx ͊[3,8L⟫]$uE b <ˁ#ӴH.Dd @B   S A ? 29<35ZcB9< `! <35ZcB9 ׇ4)*z{߈[Ds"O:_YP.Dd |B   S A ? 2|tIj*~/V\X `!PtIj*~/V\`0xQJA}3w zD:R X*bRQN<#$ ~-Aי=4}3G $`f$B֬&DV!vYeVaW&3dk XSb=JhxLy?N}.&K|%aEMͰjeǽ<;^K.aN腺P:c{:ް9M8q&E&9f+rP*WJJ_4'gd37Ku=_"v/ vJDd |B   S A ? 2~8z6~ H,T,Zɞ `!R8z6~ H,T, `0 xQJA}3w zDZR _DĤ+#(x\~@ڴV T?`muvn%2{oB I>X[ !"DZ!je65.yu>e4$Z\&#!DGW.K|S샳òneWKXPX~ſ׎wtN36?|T߻L9HnQqPbdxJJw_4iBO1"?Hg|1z{ֿDn>&)0.P7KDd |B  S A ? 2|tIj*~/V\Xݠ `!PtIj*~/V\`0xQJA}3w zD:R X*bRQN<#$ ~-Aי=4}3G $`f$B֬&DV!vYeVaW&3dk XSb=JhxLy?N}.&K|%aEMͰjeǽ<;^K.aN腺P:c{:ް9M8q&E&9f+rP*WJJ_4'gd37Ku=_"v/ vJDd |B  S A ? 2~8z6~ H,T,Z `!R8z6~ H,T, `0 xQJA}3w zDZR _DĤ+#(x\~@ڴV T?`muvn%2{oB I>X[ !"DZ!je65.yu>e4$Z\&#!DGW.K|S샳òneWKXPX~ſ׎wtN36?|T߻L9HnQqPbdxJJw_4iBO1"?Hg|1z{ֿDn>&)0.P7KDd @B  S A ? 2Si{U0Zɞԕ / `!'i{U0Zɞԕ fH xcdd``$d@9`,&FF(`T A?dA뀘7$# A( XXbS% d1D YB2sSRsnb 1㬃.f~n[8Dd B  S A? 2bR|  `!bR|  xS=KA.g?`qXXMaR/kbaem!iImO * Xr͛7;[` f`ѐ\W2F<@"bWytw19QX̖VF&gP)0IT>'zyIOŠih=rM`6YZsCqHp<~^bVX+=1$O,+uNZa-o&LPw/~x W'Ÿ߽~ [q)~~/XOmK7Sx׆'3|诹)_}Ȑ->vH`u E^N,}-y (] b) dd-T!KDd B  S A? 2ErԼh `!ErԼh)WxkQ罷Iv7kE!"G"!xf)6)eɡmo!_/5^ꫳ"i]w|kֱk_a;~fuFUU<7dš47;7]}q7U{gp5i{GL[sGF:>8B6QԒu]F_CO`)ŔO*wr`Hy<>Y,f?Gi-N1'7v6Ӿ[cs{241|?S|N+,9%kEH+gG}A\?UW8+Ʌ@% ~+g"߽ ǀDd B  S A? 27[U>qxs `!7[U>qxsr@(.xMhAn6fm1X$[ =Hċ MZLAID!Gǭ/zxx( *$5%}Hh5-JHh:ɇ^rU1&# 6400Ƃ o  ="_,fgH-̺FV6._VI"DlBcz?\۠CCOqALjL.B]-u8Qy*:}QuP fQG?:.Xs:\ҍxbϙ"K⢎v~g7Ε%k"eg]ޱ|jxN489X=}rA<=rAQ rK6'8JHr~*U~ 1㬃.f~n[8Dd B  S A? 2L}|I_\_ThI `!L}|I_\_Th^4 x=K`.I_Ң*DEhi4 H?A]8+g]]M5!w.9(6G X,$5*kũj<̚ <,יW5 Rc*'+(cE?HVKg+=H˰)fUPwK}zU5ːK.5zw=j;&UDU6[t(thT$ۗS,k}܁VpyKb~{󊩉M¼txx5U:9s;$v[*쁗0tJ{d'm܋YNo _7G/Vi7ߦ7n{'\/$ϸIc0qjM1a݈F[,0{^5Q byDd B  S A? 2QGM#0+$W `!QGM#0+$W,xkAl&5U"^ d)JItB@OOZŃ@=x(=xRP t73d(A`'1~9T(!ъ0 jF!9 6j{| 3$9/S~-D>vNV5e' qG'2v` 62^ʷh"q85Ƚ4pMVsDQfȤ'm3:U]K~Kבʩm򪎺8s+Wq2Ԣ,S-qksg=v}~_{]]Q{.LDW K½78:/`92.^ۣ{#摿} ? iяZorN%;4FkrPxﯵ>N18JQk&u7b݃뿙q73;5M#Oi,xR݄?7a_In.0B38zrG|4PygS_L>@>Υ;cGn Ǭ%|X~,ѳ'|Nǐ%|\ Mo?7 "xѩ^n$#Ɠ(29QO߷t@*gu?<8wM'W9s,drLr^rݠ {>~O׏Į}ǒ* pϝfuwr2eܰǫ1y_]WƼ>ϵrQw K&\r~4MUWNU'g~ p7ػzBSm}|ag8 NʃJvLBVWUUoT}]Wtooc :Wo_6[U.adl9!%{i.:SJ9ˊ |M@F3o-Dd  B  S A? 2"y;HŽ'$UR$/ `!y;HŽ'$UR$ P8xKAg69s^43PQ*(TL"D+<T6YXZ J-D;ŋVzre6'&V+2#ah\ѹ1AD]zhĐ|"}I}.(GL_pkr+1~WجCWCY.^eDvÛ,<ޛ\!jW |2ŢmO^¿OKFe_߱uJy !{|sq>#H=-Pt,CLOŵlȁv(I1m>4@g">KaїODd 7 B  S A? 2;/e{i<uϏ `!/e{i<uϏ.xH8x;LAǿ],'KSxL H$Vb G4kqcAaB$>bF =AB Lܷٙ}ٹ%>j:jҢJųjUIf}0,'TPxY. 1ϲ<a}**!EaZ05S'|BHۨoXM+!ПaݩkشNfF+سђ='`K_d5='JJtdE'sё=7`wNʞI_T\O٧a]b9FBfRgaN m<оb^ݕ9Vu_TO>o>)hsh&̡z/Umx; ΋{u6l޴rSp4{O۽uϪ9$y:{vua`=z}1zZ7z4w D\ͮܨ?7͟zc+\=՜]8^߂1?kbDpi"61sѫ;yHq5ߧTQ\׊Zٹ0qp翍 _F%jCP݌/"~sFY:l~d+ǹǜ pb- $&B6ɀ.%*Gi^o{ZDd B  S A? 2٧D=J `` `!٧D=J ``f 4 dxR;KAܙD,N AP¤JDL@9RYX?@km,,R.?@Q@. 778%81nq<$D{0,ձFhXIM0 C]Zh n?tǣ21=YzЧXuڸDs78Cq1utTT#KZsjSU;1=-;[]E>ps`^n)vC/LYaζ=ꡨ6NwnV3̫PΖf0&"Kn1+!|u๐/*oa_ Ғ}̄}4/"k.Uu-zi×$m>䗙qx\x`nS\1WQ;.y .S_8\LJv'8q# 8Δ{qw&*#iDd B  S A?  2]fU2~[G `!]fU2~[G&  duxR=KAKr<4DB+?H)()mHRY%mTBzJğ 6V9; Cs7oͼHXMA/X)P u1o&{<XNSR(( {QAā :F]Śнg1Wvm峍^qW}*ALp{tCMSVY*H/o,5֏oH#dp_\{TnQt28@ӬExU98Q}]=}KC~zȓޖ(V lFwq9/5?Pȁ%h0Y|LT'qxπDd B  S A? !2H$x@ CN `!H$x@ Cf (dxcdd``$d@9`,&FF(`TI A?dm.k@=P5< %!@5 @_L ĺE1X@V ȝCTN`gbM-VK-WMN.10@deĥ< ըp_ WBoGL` /qЬπe2oLP{/3a 8+*/`b W&09pYAFIԶ=ށL,hhqsEFN䂦H.pG'.v0o8321)Wx\ ]` ɞB |8 ك:Dd @B  S A ? "2Si{U0Zɞԕ / `!'i{U0Zɞԕ fH xcdd``$d@9`,&FF(`T A?dA뀘7$# A( XXbS% d1D YB2sSRsnb 1㬃.f~n[8Dd B  S A? #2ze~b鿄$V `!Ne~b鿄$>(xT1kA~fr-B1&$z)$Yr!lᑀU B &H 6"r}~@ ! Ygٻw}߼|amp3Yوa&[|\AUEW N@ᮊvQ&Ta&j?M4aX*aYcϘΨ={?{Y}gʠܚȼvMJ{˒,\T"n[Vcϸ( ~/ bhŔVF[ t9d'B07zs%׊E 9FLس@sƓWt鍄t!Z$ _sǐƓ/< I:@ʛE|eqw1o;E=rN٧^?V;?WņW n8#xUhvx@u:_Y}E[';o?Q"nin#{@ѠbRk$daG^7wDd B   S A? $2^FvHaFاFk: `!2FvHaFاFk`"xT=l@~b'iL!(tv+ 3)u j U7:ueCN٘:tԭR,ݐ@BU?5Vb:}}l CPpJead%0cWB.r8TEmN*$G}mV^+jr0{ oti!.h`nJ27ĭ4sxVbnʑk>ҝT9>a-vr0Vv]u#83Yĥ4vΑ7:^{ٽ^u,y ]=?_=a h2m/"WMT?O /4qގW(Ĵ>PN,-BAEʾ>8\(nY31>в[],LP?$dwۡݸ%b9!,M?qS5X9N\G`&vU7p-\2h+ͦk{lFLa'g(/"Dd x B ! S A? %2y۫uX+|$\ `!Ty۫uX+|$NX"xTkA~fr̭r*X"r!vjD/ ؘ3(beTYYD { Zufp9!qaYO@^0Ϙ^s s,ˬuv\ qP㾶Ӡ!t L{u. 4)<&މfںĽ$_y|Tg. ߆.{qeqx97_Ԉ{_j>V5C{(>PswBs>N 2!i/G[Zg]zE`팧_0e*T3xSMFBͤs9^&bK&Ť->@ҿW?Ps*&ì[S0_nK^apIO5]y+jNoTuPlon~OVneo' jk{1d`Xg:˴@;Dd B " S A? &2rt[H fBEz `!yrt[H fBEzt`_,PGxKkQ=wɤF[ 8nܤ S\%€NSH@f]+ B7B.\("? qѭ "d!J}3i.9ys0+0 ˱e%XHdRJMIuI'pb I/j(p؆"~G?3D&\:U[^m՜w3Do2F.MekKU{0&uo-uO񃝭itpy U~%q]k+yLDOKr;h[ [S}#|kxQܧ\!.=K$SsfZ?>-!|L#BGQ$Q$P0@'a" iƻ 0' 娘$W[Nm|f$xj).uկu;՟8G*Ea]rQF*!J!gưv+!wRD,v|/7}{W,431(zlQzEyf= [wq 8e=;WV0@ _&\n6 k$RDs;:9lKEG뿇E7)URwAv BϗàYcߴ f{t&M؆Dd b 5 c $A.? ?3"`?(2U.7i `!U.7i % xڝSK@~w\XjDC8f]@@i )HRADqA٩" +B{޻!0>!|L#BGQ$Q$P0@'a" iƻ 0' 娘$W[Nm|f$xj).uկu;՟8G*Ea]rQF*!J!gưv+!wRD,v|/7}{W,431(zlQzEyf= [wq 8e=;WV0@ _&\n6 k$RDs;:9lKEG뿇E7)URwAv BϗàYcߴ f{t&M؆Dd b 6 c $A.? ?3"`?)2U.7i `!U.7i % xڝSK@~w\XjDC8f]@@i )HRADqA٩" +B{޻!0>!|L#BGQ$Q$P0@'a" iƻ 0' 娘$W[Nm|f$xj).uկu;՟8G*Ea]rQF*!J!gưv+!wRD,v|/7}{W,431(zlQzEyf= [wq 8e=;WV0@ _&\n6 k$RDs;:9lKCompObjfObjInfoEquation Native _1151317932F`%ɃpɃk`  t obs =X" 0 S n  FMicrosoft Equation 3.0 DS Equation Equation.39qt  Z obs =Ole CompObjfObjInfoEquation Native p s "p 0  p 0 (1"p 0 )n  FMicrosoft Equation 3.0 DS Equation Equation.39q4II t obs =x 1 "x 2_1082406437O:FjɃfɃOle "CompObj#fObjInfo%Equation Native &_1082404572F`ĘɃ ɃOle /CompObj0f  s p2 1n 1 +1n 2 ()  s p2 =(n 1 "1)s 12 +(n 2 "1)s 22 n 1 +n 2 "2=n 1 +n 2 "2 FMicrosoft Equation 3.0 DS Equation Equation.39qxmIyI  s 12 n 1 +s 22 n 2 FMicrosoft Equation 3.0 DS EqObjInfo2Equation Native 3_1082449368FɃ :ɃOle 6CompObj7fObjInfo9Equation Native :@_1082403812FAɃ@tɃuation Equation.39qt$mIyI X i FMicrosoft Equation 3.0 DS Equation Equation.39q$mIyI X HOle ;CompObj<fObjInfo>Equation Native ?@_1082403904YF0}ɃSɃOle @CompObjAfObjInfoC FMicrosoft Equation 3.0 DS Equation Equation.39q$̑IToI X L FMicrosoft Equation 3.0 DS Equation Equation.39qEquation Native D@_1151250825*FǙɃwɃOle ECompObjFfObjInfoHEquation Native I_1151251494F  Ƀr=ɃOle NREquation Native T_11512524860AF $nɃɃ FMicrosoft Equation 3.0 DS Equation Equation.39q}8mIyI C=k(k"1)2 FMicrosoft Equation 3.0 DS Equation Equation.39qOle CompObj@BfObjInfoCEquation Native R6pQPE X i "X j FMicrosoft Equation 3.0 DS Equation Equation.39q} IToI B ijOle CompObj|~fObjInfoEquation Native <_1082526381FɃ%ɃOle CompObj fObjInfo" FMicrosoft Equation 3.0 DS Equation Equation.39qII  FMicrosoft Equation 3.0 DS Equation Equation.39qEquation Native #$_1151318496xsF@ɃɃOle $CompObj%fObjInfo'Equation Native (&_1082526863{F@D.Ƀ@_ɃOle - h< (f o "f e ) 2 f ei =(3807"3484) 2 3484=1043293484=29.95 FMicrosoft Equation 3.0 DS Equation Equation.39qCompObj.fObjInfo0Equation Native 1_1082528045F@hɃcɃ߬mIyI (6738"7061) 2 7061=1043297061=14.78 FMicrosoft Equation 3.0 DS Equation Equation.39qOle 5CompObj6fObjInfo8Equation Native 9lPmIyI  .05,32 =7.81473 FMicrosoft Equation 3.0 DS Equation Equation.39qmIyI _1082527582FɃ (ɃOle ;CompObj<fObjInfo>Equation Native ?$_1082527522FOɃ@?@ABCDEFGHIJKLM2`PQRSaUVWXYZ[\]^_cbefghijklmnopqrstuvwxyz{|}~ FMicrosoft Equation 3.0 DS Equation Equation.39qbcT  obs2 e" .05,c"12 FMicrosoft Equation 3.0 DS Equation Equation.39qOle eCompObjffObjInfohEquation Native i4FxItI   U2  p S j  (1"p S j  )n j +p S j'  (1"p S j'  )n j' FMicrosoft Equation 3.0 DS Eq_1154174481FϦɃ@ ɃOle nCompObjofObjInfoquation Equation.39qF xItI  U2 FMicrosoft Equation 3.0 DS Equation Equation.39qF0ԨI I 1v2: Equation Native r<_1154175943F@ ɃfɃOle sCompObjtfObjInfovEquation Native wL_1081527477 F`ɃɃOle 7.815   .3610(1".3610)10545+.2564(1".2564)1018  =.0404.3610".2564=.1046>.04041v3: 7.815   .3610(1".3610)10545+.2301(1".2301)1117  =.0376.3610".2301=.1309>.03761v4: 7.815   .3610(1".3610)10545+.1586(1".1586)788  =.0387.3610".1586=.2024>.03872v3: 7.815   .2564(1".2564)1018+.2301(1".2301)1117  =.0520.2564".2301=.0263<.05202v4 7.815   .2564(1".2564)1018+.1586(1".1586)788  =.0528.2564".1586=.0978>.05283v4: 7.815   .2301(1".2301)1117+.1586(1".1586)788  =.0506.2301".1586=.0715>.0506l:B J$    ''  '  '  Arial- 2  LOS ANGELES.A77<A7.77 2 $56..2 pNEW YORK<7SEG뿇E7)URwAv BϗàYcߴ f{t&M؆Dd Ob 7 c $A/? ?3"`?*2,-)lgzXz `!,-)lgzXz0,  xڝSJAΠ!F xK{1I!$ ^"$ ׄV""Dl| ,< v`!D̹'$ , }3{@!bQnF ш`ɼM}%ZGl Q[!cc c5Jbyy.*|${hIecحN&̺Jiנ%J{ITjZAJ{RicpƔ>mqSJ|T:.;V?n{{[ɇ%:g 5/ƆVU^0'|Z$Ƹ9k|l؍Y\q<oQ~yO>|#lN/;!w+M¿*1h޶ڒF q85ֻ)@D=W1mp uqD s !DR=a]Cx|@Ӯ4ӕDd $B 9 S A1? ,2:t ٨VH[1 `!t ٨VH[P.`pGHhn xOHTA3o޼RrWMJ4uI%A)tPq]k;ux$iE^ .TRϛf[~yA M{Øbj-I-Iu"x'Q=q zw< pc8jXD؏DF|(5x'Է !Ȁdm׷ՙ3^?[wn%~D ;곞FttZs"lnFm?3Ue+6b'N?}QĿصt*88EߍA7WX90(yZ9s%(&hcܭ83[-ԤGH#$+JTuE2zѭGtو  pSx7q=fRF&wt2gM0lk*^/:KO;@QǤ;q]ONYh^u}to2n󿲷|4TpEr/Mޤ?xw!u1]ĢC٩y\:b2i[VdӹN*k71?lηZh G!{u TRn_Qrx^GE',~D>cZїof!rTt4-eἚ"~d\;wOi'cIy,z >>mN&,~B9,Dd TB [ S AL? /2V#LF{2K `!*#LF{ XJxcdd``>$D@9@, fbd02,(1dbfaR`Z$d3H1ibY8P#7T obIFHeA*- :@@ l.#t(0K*b7 01d++&10CLadF\z0@LhG L`|t1' . M``CI)$5*LE:IDd |TB \ S AM? 02U~3=W&;mUP$17 `!)~3=W&;mUP$` 0XJxQNA}3A0!V :~P(-N :>K5a=dnN{vPr_u!]dEyi& &mj)c+1W.+WbmpEX8e&=4 ~9(v-:`&hJi<#N^mp!\ډ~hm._<ӎPQvL{>핈͓8 ,@/U:'6Dd $B # S A? 12dʗ@˜"9ϘC(|" `!tdʗ@˜"9ϘC(H@#0= Bx?hQ{krD-A"*-68CIj@$:)8Hqp'ZPKGԎPE=. ~߿GpVՐzJ%ec'qN Vqpz2BX WW䘘.\k3 BF}םQ&B+v߳2ow\n XۥbqV7.S[;F{BDM)t׍ L۬&mP"&.P/vviP©>׼ ay}8er 9[bʹ\Ϳ {\$?k$_7e |2FNeOsߢӱKz]݈&}$sXudjla^.9]=>5}TX!}nV2}يp·ܬ"53Y[i.Wj _di^&N>ri9k]QJimp֜k87wI-t7!T/A[ $GaEo]ȝ%():*RȻ;Dd ||B $ S A? 22cS:`]C?X `!7S:`]C``00xcdd``$d@9`,&FF(`T),56~) @ k+17T obIFHeA*CPD.#l(0K*b7 `01d++&U' x f22x3b^?c``Q)84C``ÏI)$5bPd+o> 1㬃b0@}CiDd ||B % S A? 32cS:`]C?Q `!7S:`]C``00xcdd``$d@9`,&FF(`T),56~) @ k+17T obIFHeA*CPD.#l(0K*b7 `01d++&U' x f22x3b^?c``Q)84C``ÏI)$5bPd+o> 1㬃b0@}CiDd 8B & S A ? 42nl.J `!nl.  v xSKP4iPDADQGA(`;qh [8NtqW"Vl|ZjU(5^K@yCtq`W[FHzGQĽ94*cv^OQoJ1X2;vF$aP{kK vjHƛJ{CG"'ɝƴ`+'\5`fS֝CkJTPnAUuBe/ lKd)E3sO綮zl&uH3qN@`{KiT' ?[}ٯAo3{]4p'4n]QFozmRzW )M?kww50mApqtx2Wǐ6x2{YM@S/Dd 8B ' S A!? 52AEu`7R  `!Eu`7R   &v xcdd``Nd 2 ĜL0##0KQ* WtWRcgbR v*6ĒʂT@D.#l&NK₈%0CXDFL ! ~ Ay@wrՉ:= ްjsVBA|M8?רا<,Xecb|{F?P 27)?(p+?!2k2B?#7&j=UGg3x-'8( a.#EÖ2! .~ݟW : az^YU2 |Vw4wbg]Y,8 Ł#J4sKp6a%!LLJ% "CX1xYgGQDDd hB ( S A"? 62q-`o^' `!q-`o^' @(|Pxcdd``ed``baV d,FYzP1n:&N! KA?H1Z@ ㆪaM,,He`HI? 01d++&b .q,6)BXbQ!le|M|H@€ܹW/˙Ü93(}A>t`_E1gEQ(j{n+a j'x9fq.ꎝþK)lNN[hv^].34Mq?y/Y4yK9psH xeoiҩ.#™Vz;1 `!60c d: v xTKA~ofvW8 $p@TL?ZX/OCTC;m4B,6)LDt_~|D<%#I  6k y?ݴldH |(=1.[CSJ_ Kt=sSVWeTl&}ڤ}Zyq;RyЕbF 5V(2|qjNhbdN2@Uyȹ[!?Yq-`eGw9!:x%hp|'r3o<阮,)\6mSgB]ZL NP [WZ_^i3KT]>=YvxAxҸvC~}z:ODu:1}ľPûP0cҘ>1:UTYнAu!!|Q"aSMZT?>۰Dd B + S A%? 92.dsv `!.dsv0@`PxS/A~vέs~%rPt r HYAD. BS\@Cv.y3}{a`M(n 4BbZDȘ/^֤h9i3QgU $d 1@)Fު P`VÔv&^DÖU\Nl! 8d'6)aI)W23+KM yQ九ӵi_IK=ӻ|g`J!C_tQn'/kĚB]KLz~.d^gWEzTGߣ()LvD㰬YĿ8#ny0B$(| 8v_lv&p}< qKt*O,Clwu7CCdzXg+ѣ!׿2JhDd TB < S A4? :28P N/7  `!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PeDd @B , S A&? ;2ntx8y=|J  `!Btx8y=|8M((+xOA߼]ZvXj[ x p@>L<@Lz6I>6b)&$xB8xؿ@ Fwn&μvm}yo0P|OX2I 0$:L/ i0s !ƐXA)|"ݟ<# a%|m)u u e/ Dɻ~me}y[,b25Ot|ITA[C=yG6`6 U5}S*/Fy|-C1gɼwF)ꘫԼMgqkkoOgw4=ij^!~o_|_;a.qzu~y[Fp1uQ_RlE\I{-N yJwc>ȦynD'49-N&v#C 1{uh4?ͥڕc8CTZ%@7C[^MxCRrZ\j\A8Dd lB - S A'? <2 >Jۗaa~ `!v >JۗaaJx DxQJP=&m3h "QЭB:~U Z(L\Mb⃛ss7 !US%TA[bNt8WcY -FCG7sMD{~{XJMr<ɑFhWJO\ެdS[ F|m1 ~vs d2dlmej(wEM&rڄģ( eM~6r`t}j=%"#<2CƷXb51Rk `!CƷXb51R@|{xcdd`` @c112BYL%bpuXh`` #RpeqIj.ŠV "z> 1C#3X?%Dd |B = S A5? ?2>.C$Z0k `!c>.C$Z0H@`01xQJP=3WA b6b~Y$fu#.Ͷb' ;lm^.993sf`*EigX-H,٠%Ej>`e/z:֜>dO"` ޹P!Th1iMAtIx6೉WNa_JUݥDd t |B 1 S A+? A2i2-W0n4Sy_.?nSg%Dy^%!L(ݬ:]`Y|>Rت(DdlB  S A? 2daXa BomUn`!fdaXa BomU24 4x=KAߙMr xjR $&X_"$ XXXXZ'XXZsgv=$ s7. ANZūXc"EkՋ۪-a$!b'k`l4? jd|ːҎmrWX>yiHgfD47(fBX^xk*"yΈᝒpW#su NQ*Ҷ瘧$ \e.+&%;C;7ѫDǓ$1aoK8DDd lB  S A? 2=зǛ- `!=зǛ-v PxQ1KPy UA! UY1U &Z,tptttrvppٟ,UhwA.{; B"Ub,|7Ylz5n:4,@0dtڽVr&xwHy^wi4u~(W^ iv=DkRbŝ(^O'9zWo|-uL;*UO/ѫ+O@|}>Tqϐ~<|7QńLQZ0=ܑht{a%X>05=\,Dd B  S A? 2~r=UR~^=%ZH"`!Rr=UR~^=%"8@2 xQJP3ɪO*laVb?!`\a$] oOX sZ o87s3ɽ.n؆[. (ba]69]Q*P=NXJLRYy:r\|+Ļo1;TT.o}'$MM!]+>^qO<Nsә%ne>ȯƷ"׳?ʛb%wk)a9@{XnMLhg Lx5Dd |B  S A? 2RaVNחur{\$`!sRaVNחur@`0Axcdd``Ned``baV d,FYzP1n:&! KA?H1Z00sC0&dT20ͤ `[YB2sSRspՉ: @@ڈ4fJ W 1y3p{@&Td@(\Pŵ!2/v.6s)p p8&8\0_^P`bN\X wq.%F&&\A D|bYGf~pCDd ph  s *A? ?3"`?2TiސwqnU0c&`![TiސwqnU0<)xڥkA߼n6wSQbc]EDp#"r‚wf%+[@-,,SXmAA$]NQp{3; <z9<2f,y+";e|E\][Ikz!`J~+rd$CMLK^2WzPg#䈘>C@1i-ob}I_.Yp@͛2BFΔ;^s xjȷP^c@+?&U, t#|tf; лx2IL>rnv wA"_:|2]_/7>u1WHݕflɃzX?Mr%, nfv]݂?9k0:~{ ׻*v*j~3}~ri-MN"_G#tl/Ŗ&4#tǟN_-O+/ Gǿ>6b3T9תBIu{1ijqNR" 3\#W>Dd h  s *A? ?3"`?2CI@8d?)`!CI@8d?: xڝRJP>6mjA!:8]AK+ -8"t oIp|A{Mb- {rhK&6XZS42"AHB-^(r*3 4f?Խ`i:A+)TY<Ќ@{<\=ҮJGZiCT~.9{#e K"kkV[nX0^zs?-NV_wc^ѾzFtT~3_~笠gN9ynccu|́~xAn6UH)ĵ r $~0LN=E^[b y,P˛d6ۀineK5ݱ]b/i{|)Bn='OB{0Ddc pN  s *A? ?2R{9zrsa.`!&{9zrsa(xڥKQ{{?[ +D'] H<0{"TX3T,$A ba!)h, $DpX<|g潙a!ǑEk!cbeٰ<.ug#h `%oK\ *271.vVj bR:!W齇 dݑ։kcjsV딴8Z{Ns3编:85T={cY!/ip(0N[KMNITxzݻrE߂.CS6bDg ;'e{˙ OAE$LuQf#UjVUwыbnϼϯpvhw} Tw7vu:Wvh7,U\l2y)b.\Z|35G'Un/}g:t-!)n*C,Z'iyp>`rK{}J\WfϮDd|N  s *A? ?2 @uУ\;O'`!@uУ\;O'@`+0xڝKAf&%)K"(E4X(ȌB!""/z>YK*P206֩Y͍f~Ϯw4E'73eަ՜IeM/)ƽlƽ Ck!oNW'^V{|+9Yi~&ޯC~V8eݿǨ{Cл]eI(2w5/_0ɣ֯9[ʶfzfrtU;. Og{qJe5t+L[_ZgK>7&DdlB  S A? 2(OI+ͺ) l/`!d(OI+ͺ) 2  2xQ=K@}3x`*-<0VZx.$cgK_M=B2AziۣźvZ>Dd |B  S A? 2v~TuwLEE{5`!v~TuwLEE{5t`p 0xJ@g6Ja, AlM"z^m`l!DЋ'/z",4aa~A<Kgdbbyb bf{Yann+|(T00GnWiv;n9Wcƭ),qůQ[ŭ߸4]R GIVY-;촒 nw]>[)hYN ghĉůet"[wQnUU|wudE3_6p[c`A7BMrR|&WHs3ڍ_G{#Rdbω%C1}ţd 'S-WoBRЃbm]YkZ&{Dd B  S A? 2 ^eWjRS,`! ^eWjRS0 'n=`zxVmh[U>w9IMeMd mg+n#Iv- ek)9F*n(((b\9eN1yQT؊@"h_U[Y={!ys5HsGdlaž`>c5vL ms`6ؕf#M$O6yI1ջ;EHwv7T&m@ GQյjȮ,GdOX)8a]q N8w&%\Fpu>O3F֠%AgVĞ1 N8y9ʝKȝߤ{1u|H}b}~'Fݡ}1FnЩ >+c#?dV{,'g:uAgQɤI8޳Ω>au'ٝJ NO\A9┥Γ}V#bW }^q>~sm[7$5dmpTO $]U {uX'%OztwmCE_k[w8;TztNae];?eqE{VT+WҸV1C$j%?_P_s*]_U?hDERx +|̠BU^ 滴 9*rΥm|b3hns9vs;q9R[8WL9^Jgqȹ0r0rn5po9s.(Eιg+E W|pGyt<9-3Wۧ3pЙ`)"r΃¹ێ+ЙӸt8V1/LLXLJG_h 6aCB?ya?Ä4حFP{2\f9 W&099 s=LWakDcuؐ2.aȣ"밤0<5gdˆ!?=<ILq&?i0!|.h.Dpt3@"]F&&\A $Ygz0z$hDdTe B  S A ?  28P N/7`!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PeeDd B  S A?  2ӴJ2W`!ӴJ2W `dqxR=OAH8 P- „?CggkIIkCggakXM!_IX(Dd+B  S A ?  2ImX }]Qn`!fImX }]QH Mh4xQ1K`}wiԤ Sqb[K\\t0]- :&| bL8rm5x5cL(4Ҥ ,O[3*6QH73v- ` >yBHd&w dͷJAݻRYᗚv v!J8'Ij[ .:V8%Y~wT* eq^ n)TM2uzO֟1$R'WR[3'-1oh}OP2hDdTB   S A ? 28P N/7`!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PehDdTB   S A ? 28P N/7v`!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PehDdTB   S A ? 28P N/7`!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PehDdTB   S A ? 28P N/7F`!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 Pe5DdN  s *A ? ?2cM;K $o`!gcM;K $$88(+5xڥTkA~vd7ѬiU+VHDAu9 q Qƾ?]|=N%. y,ߣY/0_S;6W|gR ϳ1<=[4w~/76;vF 2sw15w}ĺg6ĸ<c<]Jgysfk#ʙIZ/Wg(FP7XJ)!@j#tggg*'K><̳$0$)(%paWe;wxHVNxH^Pu\j $ˍϹ]Q)ƉH_zt}6K~"P؛qLmeR QLP9;-eƞt9=Ҽ2r_{:(G;#ʙ0Qq}c?جZb'Be[lu y}Hg#bSL9 oͭ^\rs#e}gsqw6a: IKG~==~^?]'ˬzcPRINT CompObjfObjInfoWorkbookT6A<7 2 p$34..2 CHICAGO<<<7AA 2 $34..2 > SAN FRANCISCO77<3<7<<7<A 2 >$42..2 DETROIT<74<A4 2 $41..2  WASHINGTON,DCS77<<A4A<<< 2 $46..2 sHOUSTON<A<74A< 2 s$50..2 ATLANTA74.7<47 2 $53..2 ABOSTON7A74A< 2 A$42..2  PHILADELPHIA7<.7<7.7<7 2 $26..2 DALLAS<7..77 2 $46..2 vSEATTLE77744.7 2 v$53..2  SAN DIEGO77<<7AA 2 $37..$2 DMINNEAPOLIS/ST PAULE<<777A.77477<. 2 D$38..2 MIAMIE7E 2 $42..2 ST LOUIS74.A<7 2 $44..2 yDENVER<7<77< 2 y$45..2 PHOENIX7<A7<8 2 $31..2 GSAN JOSE77<*A77 2 G$42..2  BALTIMORE77.4EA<7 2 $31..2 PORTLAND7A<4.7<< 2 $34..2 |ORLANDOA<.7<<A 2 |$42..2 FORT LAUDERDALE3A<4.7<<7<<7.7 2 $29..2 J  CINCINNATI<<<<<74 2 J $32..2  INDIANAPOLIS<<7<77A.7 2 $37..2   CLEVELAND<.777.7<< 2  $20..2   KANSAS CITY77<777<46 2  $24..2  LOUISVILLE.A<77..7 2 $37..2 M TAMPA 47E77 2 M $35..2 COLUMBUS<A.<E7<7 2 $29..2   SAN ANTONIO77<7<4A<A 2  $24..2 AUSTIN7<74< 2 $45..2  NASHVILLE<77<7..7 2 $42..2 P  LAS VEGAS.7777A77 2 P $21..2  JACKSONVILLE*7<77A<7..7 2 $30..2  PITTSBURGH74477<<A< 2 $14..2 MEMPHISE7E7<7 2 $22..2  CHARLOTTE<<7<.A447 2 $32..2 S NEW ORLEANS<7SA<.77<7 2 S$18..' FMicrosoft Excel WorksheetBiff8Excel.Sheet.89q  A\p Larry Winner Ba=f&=--!(<X@"1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *  `b Sheet1'' LOS ANGELESNEW YORKCHICAGO SAN FRANCISCODETROIT WASHINGTON,DCHOUSTONATLANTABOSTON PHILADELPHIADALLASSEATTLE SAN DIEGOMINNEAPOLIS/ST PAULMIAMIST LOUISDENVERPHOENIXSAN JOSE BALTIMOREPORTLANDORLANDOFORT LAUDERDALE CINCINNATI INDIANAPOLIS CLEVELAND KANSAS CITY LOUISVILLETAMPA COLUMBUS SAN ANTONIOAUSTIN NASHVILLE LAS VEGAS JACKSONVILLE PITTSBURGHMEMPHIS CHARLOTTE NEW ORLEANS"` m !+!b"b#$% jbbb b b0bbXblAbtblAA?\vbIr0bbbbbT0bbT0[b>lS0Dbt00^n00b 00Ԣ0bbb0blbtb0bbPercent0]db|0|0dbax0\XT0\e0\ZT00ZT0_T0=l0Te0T0T|?b 0|)bb@NJT0e0YT0L0YT0LNJT0bbk0 $bȆ04bXWbXbC0 bbb bc0 G0XWbd b  A  dMbP?_*+%MHP LaserJet 4000 PSw odXXro@ F4RdCustom page 1XCCCustom page 2XCCCustom page 3XCC"dXX??U} 'T0bbbb b b  b bvbTTbnb0bb ~ L@ ~ A@ ~ A@ ~ E@ ~ D@ ~ G@ ~ I@ ~ J@ ~ E@  ~ :@  ~ G@  ~ J@  ~ B@  ~ C@ ~ E@ ~ F@ ~ F@ ~ ?@ ~ E@ ~ ?@ ~ A@ ~ E@ ~ =@ ~ @@ ~ B@ ~ 4@ ~ 8@ ~ B@ ~ A@ ~ =@ ~ 8@ ~ F@Dl T0!b"b#$%&  ~ E@ !!~ !5@ ""~ ">@ ##~ #,@ $$~ $6@ %%~ %@@ &&~ &2@Px>@ SummaryInformation(DocumentSummaryInformation8_1081528104f FGɃzɃOle  Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@8  ՜.+,D՜.+,  PXh px Compaqh  Sheet1  Worksheets 6> _PID_GUIDAN{DEE8263D-5AD8-11D6-A632-E1F056FE7867} FMicrosoft Excel ChartBiff8Excel.Sheet.89q Oh+'08CompObjbObjInfoWorkbookd4SummaryInformation(  IBa= f =h48X1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *   p  p "x@  Chart2Sheet4Sheet5$Sheet1E2Sheet2.3Sheet3ZR3  @@  -* LOS ANGELESNEW YORKCHICAGO SAN FRANCISCODETROIT WASHINGTON,DCHOUSTONATLANTABOSTON PHILADELPHIADALLASSEATTLE SAN DIEGOMINNEAPOLIS/ST PAULMIAMIST LOUISDENVERPHOENIXSAN JOSE BALTIMOREPORTLANDORLANDOFORT LAUDERDALE CINCINNATI INDIANAPOLIS CLEVELAND KANSAS CITY LOUISVILLETAMPA COLUMBUS SAN ANTONIOAUSTIN NASHVILLE LAS VEGAS JACKSONVILLE PITTSBURGHMEMPHIS CHARLOTTE NEW ORLEANSBinMore Frequency* ?m+^ RFbMMN 0Ir0RFbbRFM MM8OFS0Lbt00MMM^n00b 00Ԣ0bbb0b8OFbtb0b(ZFbPercent0]yF bΝ0\b0ZT0_T0=l0Te0T0T|?b 0|)bb@NJT0e0YT0L0b@b|0IbI~0BT0bIbI0bu0(1T00bbblb|0bIZb&b0bZI 0ZbE 0a00ba*  I"@STDLADE??S3` o ` o  ` o  ` o 3d23 M NM4 3Q  FrequencyQ ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O& Q4FA 3Oxd 3 b43*4% M3O& Q  Bin'4% zCMZ3OG& Q  Frequency'4523  O43"  3O % M3OQ4444A 3O3 b! M43*43" 44% ,RHIM03OL& Q  Histogram'44e 14 21 28 35 42 49Moree?@@&@&@@@e>   I  dMbP?_*+%MHP LaserJet 4000 PSw odXXro@ F4RdCustom page 1XCCCustom page 2XCCCustom page 3XCC"dXX??UT0bbeb Bin Frequency,@?5@@<@@A@&@E@&@H@@ More~ @i&>@  I  dMbP?_*+%"??UT0bbb  Bin Frequency,@?5@@<@@A@&@E@&@H@@ More~ @i&(  p  6NM@? <]`I  I"??3` o ` o ` o ` o  ` o  п @3d23 M NM4 3Q  FrequencyQ ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O&Q4FA 5 3OkY5 3 b43*4%  M3O& Q  Bin'4% u%MZ3OG& Q  Frequency'4523  O43"  `3O % M3OQ4444A 5 3O3 b! M43*43" 44%  OIM03OU&Q  Histogram'44eee >@   I  dMbP?_*+%MHP LaserJet 4000 PSw odXXro@ F4RdCustom page 1XCCCustom page 2XCCCustom page 3XCC"dXX??U} 'T0bbbbb I  Ib bI IbIIIbEbbeE6T0TIbT0 LOS ANGELES~ L@NEW YORK~ A@CHICAGO~ A@ SAN FRANCISCO~ E@DETROIT~ D@ WASHINGTON,DC~ G@HOUSTON~ I@ATLANTA~ J@BOSTON~ E@  PHILADELPHIA~ :@ DALLAS~ G@ SEATTLE~ J@  SAN DIEGO~ B@ MINNEAPOLIS/ST PAUL~ C@MIAMI~ E@ST LOUIS~ F@DENVER~ F@PHOENIX~ ?@SAN JOSE~ E@ BALTIMORE~ ?@PORTLAND~ A@ORLANDO~ E@FORT LAUDERDALE~ =@ CINCINNATI~ @@ INDIANAPOLIS~ B@ CLEVELAND~ 4@ KANSAS CITY~ 8@ LOUISVILLE~ B@TAMPA ~ A@COLUMBUS~ =@ SAN ANTONIO~ 8@AUSTIN~ F@Dl&#"("(""!'!"$. #!"#$#"*%'$&%!#& T0!b"b#b$%&  NASHVILLE~ E@! LAS VEGAS~ !5@" JACKSONVILLE~ ">@# PITTSBURGH~ #,@$MEMPHIS~ $6@% CHARLOTTE~ %@@& NEW ORLEANS~ &2@x$$'%"$>@  I  dMbP?_*+%"??U>@  I  dMbP?_*+%"??U>@ @Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@e ՜.+,D՜.+,X PXh px Compaqh  Sheet4Sheet5Sheet1Sheet2Sheet3Chart2  Worksheets  DocumentSummaryInformation8_1081531700 F탨ɃɃOle PRINTCharts 6> _PID_GUIDAN{79B2FAE0-4338-11D5-A630-CA70A6EDB726}0*     ''  '   '    Arial-"System-'- g  2 Rating<.332 Count<333-'-   Arial-  2 pk1. 2 p108... 2 k2. 2 519... 2 >k3. 2 >744... 2 k4. 2 47.. 2 k5. 2 5.' FMicrosoft Excel WorksheetBiff8Excel.Sheet.89q  A\p Larry Winner Ba=f =-- <X@"CompObjfObjInfoWorkbookBSummaryInformation(2      !"#$%&'()*+,-./01345789:;<?BCDFGHIJKLORSTVWXYZ[\_bcdfghijklorstvwxyz{|1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *    ` Sheet1RatingCount 0T0bb Fbb0b1.0ρ,b?΁0lPbm/JJ'rN ^n00b 00Ԣ0bbb0bFbtb0bFbPercent0]db|0|0dbax0\XT0\e0\ZT00ZT0_T0=l0Te0T0T|?b 0|)bb@NJT0e0YT0L0YT0LNJT0bbk0 $bȆ04bKd?bKbC0@Fbbb܎bc0܎G0Kd?  A  dMbP?_*+%MHP LaserJet 4000 PSw odXXro@ F4RdCustom page 1XCCCustom page 2XCCCustom page 3XCC"dXX??UT0bb  ?[@@8@@@@@G@@@d>@  Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@ܼ ՜.+,D՜.+,  PXh pxDocumentSummaryInformation86_1081532242 FɃ{ɃOle =CompObj>b Compaqh  Sheet1  Worksheets 6> _PID_GUIDAN{79A0541E-5AE1-11D6-A632-E1F056FE7867} FMicrosoft Excel ChartBiff8Excel.Sheet.89qObjInfo@WorkbookSummaryInformation(ADocumentSummaryInformation8E Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@ ՜.+,D՜.+,D PXh px Compaqh   ABa=f=hc 8X1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *     Chart1Sheet1%Sheet2Sheet3ZR3  @@  RatingCount  FbMMN 0Ir0FbbFM MMFS0Lbt00MMM^n00b 00Ԣ0bbb0bFbtb0b$FbPercent0]yF bΝ0\b0ZT0_T0=l0Te0T0T|?b 0|)bb@NJT0e0YT0L0b@b|0IbI~0BT0bIbI0bu0(1T00bbblb|0bIZb&b0bZI 0Z  A"??3` ( ` (  п3d23 M NM4 3QQ ;QQ3_4E4D $% M 3O&Q4$% M 3O& Q4FAI 3Oyl f3 b#M43*#M! M4523  O43" j I3Oj % M3OQ44444ee[@8@@@G@@e>   A  dMbP?_*+%"0??4UT0bb00RatingCount?[@@8@@@@@G@@@ d%( dI p  6NMM?]`xI  A"x??3` ( ` (  3d23 M NM4 3QQ ;QQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA9  3Ot u3 b#M43*#M! M4523  O43"  93O % M3OQ44444eee >@  A  dMbP?_*+%"??U>@  A  dMbP?_*+%"??U>@ Sheet1Sheet2Sheet3Chart1  WorksheetsCharts 6> _PID_GUIDAN{79A05420-5AE1-11D6-A632-E1F056FE7867} FMicrosoft Excel ChartBiff8Excel.Sheet.89q_1081532438 FɃɃOle MCompObjNbObjInfoP Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@ ՜.+,D՜.+,D PXh px Compaqh  WorkbookSummaryInformation(QDocumentSummaryInformation8U_1081587217 F sɃɃ  ABa=f=hc> 8X1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *    G Chart2 Sheet1Sheet2Sheet3ZR3  @@  RatingCount,  FbMMN 0Ir0FbbFM MMFS0Lbt00MMM^n00b 00Ԣ0bbb0bFbtb0b$FbPercent0]yF bΝ0\b0ZT0_T0=l0Te0T0T|?b 0|)bb@NJT0e0YT0L0b@b|0IbI~0BT0bIbI0bu0(1T00bbblb|0bIZb&b0bZI 0Z  A"??3` ( ` (  @3d23 M NM4 3QQ ;QQ3_4E4D $% M 3O& Q4$% M 3O& Q4FA_1 3OF 1 3" )@F 3O)@% M3OQ44444ee[@8@@@G@@e>   A  dMbP?_*+%"0??4UT0bbRatingCount?[@@8@@@@@G@@@ d%8 ( dI p  6NMM?]`xI  A"x??3` ( ` (  3d23 M NM4 3QQ ;QQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA9  3Ot u3 b#M43*#M! M4523  O43"  93O % M3OQ44444eee xp  6NMM?]`ty  A"t??3` ( ` (   3d23 M NM4 3QQ ;QQ3_4E4D $% M 3O&Q4$% M 3O& Q4FAD 3OF D 3" 8\:3O8\% M3OQ44444eee >@  A  dMbP?_*+%"??U>@  A  dMbP?_*+%"??U>@ Sheet1Sheet2Sheet3Chart2  WorksheetsCharts 6> _PID_GUIDAN{79A05420-5AE1-11D6-A632-E1F056FE7867} FMicrosoft Excel WorksheetBiff8Excel.Sheet.89qOle ]PRINT0CompObj^fObjInfo`Pd: E !    ''  Arial---Arial-Arial----------------"System-'-  -'-  - "- $E E  "- "---'--- I 8l7  7 * *   ---'---  8l "- E E ---'---   "- E -E ~E 7 ~7 * ~*  ~ ~~~~~~~E E [ E [ E [ E [ 2E 2[ E [ F E F [ E [ Y E Y [ E [ mE m[ E ---'---  !!---'--- ] ---'--- Z --  "- $n n---'-- - Z  $z---'-- - Z +v $v+v@a+v---'-- - Z  $---'-- - Z , $,Au,---'-- - Z o $ooZo---'-- - Z W $WlWBW---'-- - Z  $|---'-- - Z 4 $4I44---'-- - Z t  $ _ t  t _---'-- - Z ^  $ I ^ s ^ I---'-- - Z @  $ + @ U @ +---'-- - Z :  $ % : O : %---'-- - Z KJ  $J 6_ KJ `5 KJ 6---'-- - Z   $     ---'-- - Z t  $t o t _ t o---'-- - Z   $     ---'-- - Z i  $ T i ~ i T---'-- - Z   $     ---'-- - Z ~%  $% i: ~%  ~% i---'-- - Z   $     ---'-- - Z Y  $Y n Y D Y ---'-- - Z +  $  + @ + ---'-- - Z t  $t  t _ t ---'-- - Z k) $)V>k)k)V---'-- - Z +  $+ @ +  + ---'-- - Z [  $ F [ po [ F---'-- - Z   $   n  ---'-- - Z   $     ---'-- - Z %a  $a v %a :L %a ---'-- - Z R  $ = R g R =---'-- - Z !  $! 6 !  ! ---'-- - Z >  $ ) > Sl > )---'-- - Z ~  $ i1 ~  ~ i---'-- - Z ]B  $B HW ]B r- ]B H---'-- - Z $  $$ 9 $  $ ---'-- - Z  $---'-- - Z D $/DYD/---'-- - Z  $ ---'-- - Z +  $ + @ +  ---'-- - Z 7  $" 7 L k7 " ---'-- - Z C  $. C X ~C . ---'-- - Z  t $t  t5 _ t ---'-- - Z h  $S h } h S ---'-- - Z ? & $&* ;? &T ? &* ---'-- - Z   $   k  ---'-- - Z  A $A V A& , A ---'-- - Z   $     ---'-- - Z ---'-- - ] ---'-- -  ------'-- -  f 2 |#Pruduction Costs9!444/44=4//----'-- -  ---'-- -  ----'-- -    2  70' 2 10'' 2 20'' 2 30'' 2 40'' 2 50'' 2 60'' 2 70'' 2 80'' 2 90'' 2 100'''---'-- -  ---'-- -    2 0' 2 5' 2 10'' 2 15'' 2 20'' 2  25'' 2 30'' 2 2 35'' 2 40'' 2 F45'' 2 50''---'-- -  ------'-- - f  !2 Quantity Produced7+'+&/+++''+----'-- -  -----'-- - Jd Arial- 2 t Total Cost-----'-- -  - "-- 9l---'--- 7m---'--- 7m "-- "- $2 Series1/''$'---'-- - 7m---'-- -  ---'-- -  --'   '  ' Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@ᢔ ՜.+,D՜.+,, PXh px Compaqh  Workbookf+SummaryInformation(aDocumentSummaryInformation8e_1081592703 F Ƀ` Ƀ  A\p Larry Winner Ba=f/=h.#<X@"1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *  `* Chart1 dean 0  0 T0!b"b#ǂb$% {bb(b)*0bbbXblAbdǂtblAA? rbIr0{bb{bT0bbT0Pǂb ǂ,{S0Dbt00^n00b 00Ԣ0bbb0b,{btb0bX{bPercent0]db|0|0db{bΝ0\b0ZT0_T0=l0Te0T0T|?b 0|)bb@NJT0e0YT0L0YT0LNJT0bbk0 $bȆ04b@"333333<@#Gz8@$@4@%ףp= 1@&33333,@'Q8*@(#@){Gz#@*Gz"@+ ףp= @,33333 @-@.@/RQ@e)\(W@p= 3V@\(U@333333V@QU@HzwV@R!V@ףp= V@GzNT@ GzT@ (\"U@ ףp=jU@ HzwU@ QNU@U@Hz'W@p= V@HzU@GzNT@fffffT@(\T@{G:T@{G:S@fffffS@GzR@fffffQ@)\hP@Q O@GzN@)\HS@GzR@HzQ@ = ףpP@!HzP@"Q L@# I@$33333E@%zGC@&ffffff?@'33333s=@(Q=@) 3@*\(\4@+Gz1@,{G:3@-ףp= -@.zG&@/zGa)@e> /z  A  dMbP?_*+%MHP LaserJet 4000 PSw odXXro@ F4RdCustom page 1XCCCustom page 2XCCCustom page 3XCC"dXX??UColumn AX0T0bbǂbbb   b ǂǂ bT0T0bETeȂ 0 0}{bb}b}?`G@@ @z@X@ @Z@@ @@X@ @t@@ @R@@ @3@J@  @}@@ "@@@  $@ @Z@  &@@@  (@@@  *@@@  ,@@@ .@@U@ 0@&@@ 1@@@ 2@@n@ 3@@@ 4@\@G@ 5@@@ 6@ C@@ 7@@ @ 8@&@@ 9@@!@ :@l@@ ;@N@@ <@L@A@ =@@@ >@l@"@ ?@@@{@ @@ʪ@}@ Dl******************************* T0!b"b#ǂb$%&'b(b)*+b,ǂǂ-b.T0/T0 @@4@@ !A@2@%@ !"A@@@ "#B@4@ I@ #$B@@4@ @ $%C@@@ %&C@l@@ &'D@|@@ '(D@#@@ ()E@p@ĝ@ )*E@0@П@ *+F@x@@ +,F@@ @ ,-G@@P@ -.G@@@ ./H@w@ԓ@ /$,***************>@ deanChart1  WorksheetsCharts 6> _PID_GUIDAN{EE8F475D-5B65-11D6-A632-F329463F6467} FMicrosoft Excel ChartBiff8Excel.Sheet.89qOle mCompObjnbObjInfopWorkbookU Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@M ՜.+,D՜.+,D PXh px Compaqh   ABa=f=h,_8X1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *     Chart1Sheet1Sheet2lSheet3I RaceWhiteHispanicAsianBlackSmokeYesNo  4Ihb( b0b 0bP}bu0b0 B 0  %bd2  5602u50 ? 5  'k 5 1k 5E 9C> 56-0V Ir0%bb%g 9e> 5 6-05  %S0Lbt00&\ 5^n00b 00Ԣ0bbb0b %btb0b$bPercent0]5W8 5 ( 8 bΝ0\b0ZT0_T0=l0Te0T0T|?b 0|)bb@NJT0e0YT0L0YT0LNJT0bbk0 $bȆ0 ?d  A  dMbP?_*+%"??U252>@  A  dMbP?_*+%"??U>@  A  dMbP?_*+%"??U>@ SummaryInformation(qDocumentSummaryInformation8u_1081593033M F Ƀ HɃOle } Sheet1Sheet2Sheet3Chart1  WorksheetsCharts 6> _PID_GUIDAN{68C8D64B-5B72-11D6-A632-F329463F6467} FMicrosoft Excel ChartBiff8Excel.Sheet.89qCompObj~bObjInfoWorkbookI#SummaryInformation( Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@M ՜.+,D՜.+,P  PXh px Compaqh     %$ !"#s+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~  ABa= f=h,_8X1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *    q Chart1Chart2Sheet1w!Sheet2`"Sheet3I RaceWhiteHispanicAsianBlackSmokeYesNo  [I  %bMMN q0Ir0%bb%M M %S0Lbt00#^n00b 00Ԣ0bbb0b %btb0b$bPercent0]8 bΝ0\b0ZT0_T0=l0Te0T0T|?b 0|)bb@NJT0e0YT0L0YT0LNJT0bbk0 $bȆ0 ?d  A"@c?? 3` 4# ` 4# ` 4# ` 4# 0 0 3?dd 3Q:  YesQ ;Q ;Q3_4E4 3Q: NoQ ;Q ;Q3_4E4D$% M 3O& Q4$% M 3O& Q4FA*q 3O,r 3 b#M!  O43*#M! M! M MN43 #M4% +M3O& Q  Race'4% sM3O4& Q  # of students'4%   M3OA& Q  Smoking Status'43" :dd 3O% M3OQ4444% SM03Oz& Q 0Smoking Status by Race'44eWhiteWhiteHispanicHispanicAsianAsianBlackBlacke@R@Pp@@p@@@_@@e> ?d  A  dMbP?_*+%"??U252>@  A  dMbP?_*+%"??U>@  A  dMbP?_*+%"??U>@ DocumentSummaryInformation8_1081597955 FoɃ`gɃOle CompObjb Sheet1Sheet2Sheet3Chart1Chart2  WorksheetsCharts 6> _PID_GUIDAN{68C8D64B-5B72-11D6-A632-F329463F6467} FMicrosoft Excel ChartBiff8Excel.Sheet.89qObjInfoWorkbookh(SummaryInformation(DocumentSummaryInformation8 Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@ ՜.+,D՜.+,0 PXh px Compaqh   ABa=f2=h,_8X1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *     Chart1 airyieldP .YearYieldUnitrevCPIYield82dyielddpcidyield82  I ^%bMMN q0Ir0V%bbV%M M`%S0Lbt00#^n00b 00Ԣ0bbb0b`%btb0b$bPercent0]< bΝ0\b0ZT0_T0=l0Te0T0T|?b 0|)bb@NJT0e0YT0L0YT0LNJT0bbk0 $bȆ0 1Q?d  A  dMbP?_*+%"`ǂ??,DU3@@p@6 @̡@@y@@@T@@6 @@@0y@@|d@6 @t@@u@?@?p6 @|@@t@t@^@[@$@6@@@0t@Ĩ@ ^@`}6@@@t@@k@`@ 6@ @X@@t@@@e@`s6@X@@t@@@@u r@6@Ԙ@(@`s@@`s r@ 6@@@q@B@N@z@|6Ğ@\@@q@C@@@@`6Ȟ@|@@r@h@@p@ w6̞@@@`r@E@|@z@>@6О@@@0t@@D@t@q6Ԟ@@Ѓ@t@G@z@`@f6؞@L@Ȇ@@@H@&@v@6ܞ@D@@y@̵@y@p@`}6@@@{@M@@ @Q6@@P@p}@@Ё@@Q6@,@@@h@@!6@@8@ @`@@@Ё6@$@4@H@@@+@Б@D2 lR::::::::::::::::::::::::::::: @  airyieldChart1  WorksheetsCharts 6> _PID_GUIDAN{68C8D64C-5B72-11D6-A632-F329463F6467}  FMicrosoft Word Picture MSWordDocWord.Picture.89q_1081610531F  F Ƀ`ҎɃ1Table*CompObj  hObjInfo      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~     r !#$%&'()+,-./013456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqtuvwxyz{|}~ [(@(NormalCJmH <A@<Default Paragraph Font&.6efghijk&.69 |(  ,2$=m:):*n\]@J (  $:@ /93 MH  # /93H  # /93BB   BB   BB   BB   BB    aBB    aBB     BB    BB    +,BB   :;BB   IJBB   XYBB   ghBB   vwBB   BB   BB   BB   BB   `BB   oBB   BB   `~BB   BB   ~BB   BB   ~BB   BB    BB !   BB "   BB #  BB $  ~BB %  +,BB &  ~+,BB '  :;BB (  `:;BB )  IoJBB *  IJBB +  X`YBB ,  XYBB -  ghBB .  vwBB /  ~vwBB 0  BB 1  oBB 2  BB 3  `~BB 4  oBB 5  QBB 6  2BB 7  ABB 8  BB 9  2PBB :  BB ;  P_BB <  BB =  P_BB >  BB ?  _nBB @   BB A  _ nBB B  BB C  PnBB D  +,BB E  P+n,BB F  :;BB G  2:n;BB H  IAJBB I  _InJBB J  X2YBB K  _XnYBB L  _g`hBB M  vwBB N  Pv_wBB O  BB P  A_BB Q  BB R  2PBB S  ABB T  #BB U  BB V  BB W  BB X  BB Y  BB Z  BB [  BB \  BB ]  BB ^   BB _   BB `  BB a  BB b  +,BB c  +,BB d  :;BB e  :;BB f  IJBB g  IJBB h  }XYBB i  XYBB j  }g@hBB k  }v@wBB l  BB m  BB n  BB o  BB p  0BB q  0BB r  0?BB s  BB t  0?BB u  BB v  0?BB w  BB x  0?BB y  BB z  0?BB {  BB |  0 ?BB }   BB ~  0?BB   BB   0+,BB   0:;BB   0I?JBB   0X?YBB   0g?hBB   0v?wBB   0?BB   0?BB   0?BB   0?BB   >zBB    BB    /BB    BB    BB   BB   +,BB   +,BB   :;BB   :;BB   IJBB   XYBB   ghBB   vwBB    BB   BB   /BB   BB    BB   >zBB   BB   .BB   BB   .BB    BB    .BB   BB   +,BB   :;BB   IJBB   XYBB   ghBB   vwBB   BB   BB   BB   BB   iBB   KBB   <ZBB   BB   -KBB   BB   <BB   BB   -BB   BB   BB    BB   BB   +,BB   :;BB   IJBB   X-YBB   XYBB   g-hBB   ghBB   v<wBB   vwBB   -KBB   BB   <iBB   BB   KBB   i:@ $  LBB   YBB   ;BB   , JBB    BB   ,;BB   BB   +,BB   J:;BB   ,IJBB   IJBB   ,X;YBB   XYBB   g,hBB   ghBB   v,wBB   vwBB   ,BB   BB   ;BB   BB   ,BB   BB   ;wBB   BB    BB   :BB    BB   +BB    :BB    BB   BB   BB   +,BB   +,BB   : ;BB   :;BB   I JBB   IJBB   X YBB   XYBB   g hBB   ghBB   v wBB   vwBB   BB   BB   +BB   BB    BB   +BB    BB   :BB    BB    BB    BB    BB    BB     BB   BB   BB   BB    BB     BB    BB    +,BB    :;BB   IJBB   XYBB   ghBB   vwBB   BB   BB   BB   BB   H W BB   H W BB   H W BB   H W BB   *  BB   *  BB   H W BB   H W BB   H +W ,BB   H :W ;BB    H IW JBB !  H XW YBB "  H gW hBB #  H vW wBB $  H W BB %  H W BB &  H  BB '  W  BB (    !BB )   !BB *    BB +   ! )!BB ,    BB -  !)!BB .   !+)!,BB /   :)!;BB 0   I JBB 1  !I)!JBB 2   X YBB 3  !X)!YBB 4   g hBB 5  !g)!hBB 6   v wBB 7  !v)!wBB 8    BB 9   !)!BB :    BB ;   )!BB <    BB =  !G!BB >    BB ?  )!G!BB @  7"F"BB A  7"F"BB B  7"F"BB C  7"F"BB D  7"F"BB E  7"F"BB F  7"F"BB G  7" F"BB H  7"F"BB I  7"+F",BB J  7":F";BB K  7"IF"JBB L  7"XF"YBB M  7"gF"hBB N  7"vF"wBB O  7"F"BB P  7"F"BB Q  7"F"BB R  7"F"BB S  ""BB T  " #BB U  ""BB V  "#BB W  " "BB X   # '#BB Y  ""BB Z  #'#BB [  "+",BB \  #+'#,BB ]  ":";BB ^  #:'#;BB _  "I"JBB `  #I'#JBB a  "X"YBB b  #X'#YBB c  "g"hBB d  #g'#hBB e  "v"wBB f  #v'#wBB g  ""BB h  #'#BB i  ""BB j  #'#BB k  ""BB l  #'#BB m  ""BB n  #'#BB o  ##BB p  ##BB q  r# #BB r  # $BB s  r##BB t  #$BB u  c#+#,BB v  $+$,BB w  c#:r#;BB x  c#Ir#JBB y  c#Xr#YBB z  c#gr#hBB {  c#v#wBB |  $v$wBB }  r##BB ~  #$BB   r##BB   #$BB   ##BB   ##BB   b$$BB   D$$BB   5$ b$BB   $ $BB   5$D$BB   $$BB   &$+D$,BB   $+$,BB   &$:5$;BB   $:$;BB   &$I5$JBB   $I$JBB   &$X5$YBB   $X$YBB   &$g5$hBB   $g$hBB   &$vD$w:@ 5$, ]KBB   $v$wBB   5$D$BB   $$BB   5$b$BB   $$BB   D$$BB   b$$BB   %%BB   C%%BB   %&BB   %%BB   4%%BB   %&BB   % 4%BB   % %BB   & $&BB   %%%BB   %%BB   &$&BB   %+%,BB   %+%,BB   &+$&,BB   %:%;BB   %:%;BB   &:$&;BB   %I%JBB   %I%JBB   &I$&JBB   %X%YBB   %X%YBB   &X$&YBB   %g%hBB   %g%hBB   &g$&hBB   %v%wBB   %v%wBB   &v$&wBB   %%BB   %%BB   &$&BB   %%BB   %%BB   &$&BB   %%BB   %%BB   &$&BB   %%BB   %%BB   &$&BB   &&BB   ~&&BB   o& &BB   & 'BB   o&~&BB   &'BB   `&+o&,BB   '+',BB   `&:o&;BB   ':';BB   `&I'JBB   `&X'YBB   `&go&hBB   `&vo&wBB   o&~&BB   ''BB   o&&BB   &'BB   ~&&BB   &&BB   (A(BB   (A(BB   ((BB   ((BB   ((BB   'A(BB   'A(BB   ( (BB   ((BB   (+(,BB   (:(;BB   (I(JBB   (X(YBB   (g(hBB   (v(wBB   ((BB   ((BB   ((BB   ((BB   ((BB   n((BB   _( (BB   ( )BB   _(n(BB   ()BB   P(+n(,BB   (+),BB   P(:_(;BB   ):);BB   P(I_(JBB   )I)JBB   P(X_(YBB   )X)YBB   P(g_(hBB   )g)hBB   P(vn(wBB   (v)wBB   _(n(BB   ()BB   _((BB   ()BB   n((BB   ((BB   1)@)BB   m))BB   1)@)BB   ^))BB    1) @)BB    O) )BB    1)^)BB    1)+O),BB    1):@);BB   1)I@)JBB   1)X@)YBB   1)g@)hBB   1)v@)wBB   1)@)BB   1)@)BB   1)@)BB   1)@)BB   {*>+BB   {*>+BB   {**BB   {**BB   {**BB   {**BB   {**BB   {* *BB   {* +BB   {*+ +,BB    {*:*;BB !  {*I*JBB "  {*X*YBB #  {*g*hBB $  {*v*wBB %  {**BB &  {**BB '  {**BB (  {**BB )  M+\+BB *  M+\+BB +  M+\+BB ,  M+\+BB -  M+\+BB .  M+\+BB /  M+\+BB 0  M+ \+BB 1  M+\+BB 2  M++\+,BB 3  M+:\+;BB 4  M+I\+JBB 5  M+X\+YBB 6  M+g\+hBB 7  M+v\+wBB 8  M+\+BB 9  M+\+BB :  M+\+BB ;  M+\+BB <  +,BB =  +=,BB >  + +BB ?  , L,BB @  ++BB A  =,L,BB B  +++,BB C  =,+[,,BB D  +:+;BB E  L,:[,;BB F  +I+JBB G  L,I[,JBB H  +X+YBB I  L,X[,YBB J  +g+hBB K  L,g[,hBB L  +v+wBB M  =,v[,wBB N  ++BB O  =,L,BB P  ++BB Q  ,L,BB R  +=,BB S  +,BB T  y,,BB U  ,,BB V  y,,BB W  ,,BB X  y, ,BB Y  , ,BB Z  y,,BB [  y,+,,BB \  y,:,;:@ y,3 &JBB ^  y,I,JBB _  y,X,YBB `  y,g,hBB a  y,v,wBB b  y,,BB c  y,,BB d  y,,BB e  y,,BB f  --BB g  --BB h  --BB i  --BB j  - -BB k  --BB l  -+-,BB m  -:-;BB n  -I-JBB o  -X-YBB p  -g-hBB q  -v-wBB r  --BB s  --BB t  --BB u  --BB v  -.BB w  -.BB x  -.BB y  -.BB z  -.BB {  --BB |  -.BB }  i--BB ~  -.BB   i- -BB   - .BB   Z-i-BB   -.BB   K-+i-,BB   -+.,BB   K-:Z-;BB   -:.;BB   K-IZ-JBB   -I.JBB   K-XZ-YBB   -X.YBB   K-gZ-hBB   -g.hBB   K-vi-wBB   -v.wBB   Z-i-BB   -.BB   Z--BB   -.BB   i--BB   -.BB   --BB   -.BB   Y..BB   ;..BB   ,. J.BB   . .BB   ,.;.BB   ..BB   .+.,BB   J.:.;BB   ,.I.JBB   .I.JBB   ,.X;.YBB   .X.YBB   .g,.hBB   .g.hBB   .v,.wBB   .v.wBB   .,.BB   ..BB   .;.BB   ..BB   ,..BB   ..BB   ;.w.BB   ..BB   0u0BB   /0BB   / 0BB   00BB   //BB   00BB   //BB   00BB   //BB   00BB   //BB   / /BB   //BB   /+/,BB   /:/;BB   /I/JBB   /X/YBB   0X0YBB   /g/hBB   0g0hBB   /v/wBB   0v0wBB   //BB   00BB   /0BB   u00BB   /0BB   0u0BB    1V1BB   0t1BB   0  1BB   V1 1BB   00BB   t11BB   0+0,BB   t1+1,BB   0:0;BB   1:1;BB   0I0JBB   1I1JBB   0X0YBB   1X1YBB   0g0hBB   1g1hBB   0v0wBB   t1v1wBB   00BB   t11BB   0 1BB   V11BB   0t1BB    1V1BB   11BB   F2U2BB   11BB   F2U2BB   1 1BB   F2 U2BB   11BB   F2U2BB   1+1,BB   F2+U2,BB   1:1;BB   F2:U2;BB   1I1JBB   F2IU2JBB   1X1YBB   F2XU2YBB   1g1hBB   F2gU2hBB   1v1wBB   F2vU2wBB   11BB   72U2BB   11BB   (2U2BB   1(2BB   F2U2BB   12BB   F2U2BB   22BB   23BB   22BB   2'3BB   2 2BB   3 63BB   22BB   '363BB   2+2,BB   '3+63,BB    2:2;BB    '3:63;BB    2I2JBB    '3I63JBB    2X2YBB   '3X63YBB   2g2hBB   '3g63hBB   2v2wBB   '3v63wBB   22BB   '363BB   22BB   '363BB   22BB   '363BB   22BB   '363BB   33BB   33BB   33BB   33BB   c33BB    c33BB !  3 3BB "  33BB #  3+3,BB $  3:3;BB %  3I3J E@ 05E3 IBB '  3X3YBB (  3g3hBB )  3v3wBB *  33BB +  33BB ,  33BB -  33BB .  33BB /  33BB 0  33BB 1  33BB 2  3 3BB 3  33BB 4  3+3,BB 5  3:3;BB 6  3I3JBB 7  3X3YBB 8  3g3hBB 9  3v3wBB :  33BB ;  33BB <  33BB =  33BB >  b44BB ?  D44BB @  54 S4BB A  4 4BB B  54D4BB C  44BB D  &4+54,BB E  4+4,BB F  &4:54;BB G  4:4;BB H  &4I4JBB I  &4X4YBB J  &4g54hBB K  &4v54wBB L  54D4BB M  44BB N  54S4BB O  44BB P  D44BB Q  b44BB R  %5p5BB S  55BB T  4 5BB U  p5 5BB V  45BB W  55BB X  4+5,BB Y  5:C5;BB Z  5I5JBB [  C5X5YBB \  5g5hBB ]  4v5wBB ^  5v5wBB _  45BB `  55BB a  45BB b  55BB c  55BB d  %5p5~ e 60t12 ~ f 68!t1$2 ~ g 6't1+2 ~ h 6.t122 ~ i 6 72$E3 ~ j 6'72+E3 ~ k 6.722E3  l BXCDEF 5%XX @t1W (2 m BICDEF 5%II @%t1P'(2HB n # %t1R%1HB oB # %t1%1HB p # R%t1o&2HB qB # %t1'2HB r # o&1P'2HB sB # &1P'2 t BXCDEF 5%XX @,t1Y.(2HB u # ,1Y.1HB vB # ,1Y.1HB w # ,1Y.1HB xB # ,1Y.1HB y # ,1Y.1HB z # ,t1,2HB { # =,t1>,2HB | # j,t1k,2HB } # ,t1,2HB ~ # ,t1,2HB  # ,t1,2HB  # -t1-2HB  # K-t1L-2HB  # x-t1y-2HB  # -t1-2HB  # -t1-2HB  # -t1.2HB  # ,.t1-.2  BXCDEF 5%XX @72W 2HB  # U2W V2HB B # 2W 2HB  # 2W 2HB  # ,72-2HB  # 722HB  # 722HB  # :72;2HB  # 722HB  # 722HB  # H 72I 2  BICDEF 5%II @%72P'2HB  # %72%2HB  # %72%2HB  # %72%2HB  # Q&72R&2HB  # &72&2HB  # #'72$'2N  3 ,72w.2HB  # {*# +#HB B # l*#z+#HB  # 0*#=,#HB B # 0*$[, $HB  # 0*&$y,'$HB B # 0*D$y,E$HB  # 0*b$,c$HB B # 0*$,$HB  # *$,$HB B # +$,$HB B # +$+$HB  # ,$.,$HB  # [,$\,$HB  # N*#O*#HB  # N*#O*$HB  # {*#|*$HB  # *#*#HB  # *#*$HB  # *#*$HB  # +#+$HB  # /+#0+$HB  # \+#]+$HB  # +#+$HB  # +#+$HB  # +$+$HB  # +#+$HB  # ,#,$HB  # =,#>,$HB  # j,$k,$  tBXC;DE F5%CC;;-h-h<<--<<K < Z:Z:i+iIiIxXxIXX,:,:;+;+,,; ; ,,;;,,ii<KK<<--<--<-<KKZZiii-@0*#,$HB  # *"+7"HB B # *"+"HB  # *F"+"HB B # *"+'#HB  # z+"j,'#HB B # ,"y,#  \BCDEF5% xx;--ZZZiixx,;xhxhwAD@*"y,'#HB  # X(")"HB B # XF"G"HB  # Xd"e"HB B # i""HB  # i""HB B # ""HB  # ""HB B # J""HB B # ";"HB B # ""HB B # Z""HB  # ""HB  # ";"HB  # h"w"HB  # w"x"HB  # ""HB  # ""HB B # w""HB B # "J"HB  # ;#J#HB  # h#w#HB  # ##HB  # ##HB B # X6#7#HB B # +6#I7#HB  # 6#7#HB B # 6#7#HB  # T#:U#HB  # IT#vU#HB  # T#U#HB B # :r#s#HB B #  r#s#HB  # X#g#HB  # g##HB  # x"y"HB  # s""HB  # s""HB  # d""HB  # ""HB  # ,"- #HB  # ,d"-"HB  # Yd"Z"HB  # Y #Z#HB  # Y#Z'#HB  # " #HB  # d""HB  # d""HB  # ""$U@ Z "4- HHB  # "#HB  # '#6#HB  # 6#T#HB  # '#6#HB  # "'#HB  # ""HB  # d""HB  #  d""HB  #  "'#HB  #  E#T#HB  #  c#r#HB  # :#;#HB  # :T#;r#HB  # :T#;U#HB  # :'#;6#HB  # :d";#HB  # g"hc#HB  # gc#h#HB  # c##HB  # T#c#HB  # "T#   BgCDEF5%XX::wwhhYYJJ;;JJYYhhhhhYYJ;;w,h,Y;;JJwwh,,,;;J;J JJY J:J+J+;X;+;+,;+;;JJ;;,,wwhwJYJ;;,;J;J;whwwwhYhhYYY,,xZii-ZZKKgXXcd@Z"#HB  # 6#7#HB  # 6#7#  BKCDE,F45% KK<-@h'#E#   |BCDEF5% @E#T#HB   #  +#!+#HB  B # k+'#y,#HB   # +#y,#HB  B # =,#,#HB  # y,$,&$  BwCDEF5%((whwhhJYY,,ZZKKxxxxiiZKK<<hwwQT@+#,5$HB  # 0`&1a&HB B # 0~&1&HB  # 0&2&HB B # 0&72&HB  # 0&U2&HB B #  1&d2&HB  # )1'2'HB B # 12'U23'HB  # )1P'72Q'HB B # 81o'72p'HB  # 81'72'HB B # 81'F2'HB  # 81'U2'HB B # 81'd2'HB  # 81(s2(HB B # 81#(2$(HB   # 81A(2B(HB !B # G1_(2`(HB " # G1}(2~(HB #B # G1(2(HB $ # 0Q&0&HB % # 0Q&0&HB & # )1'*1P'HB ' # )1Q&*1'HB ( # V1Q&W1(HB ) # 1Q&1(HB * # 1Q&1(HB + # 1o&1(HB , #  2& 2(HB - # 72&82(HB . # d2&e2#'HB / # d2'e2(HB 0 # 2#(2(HB 1 # 2_(2( 2 B:CYDEF5%----xxiiix<Ki:; ;JYKxiiZiixxixiiK<<--[\@0Q&2(HB 3 # e1=,4>,HB 4B # e1y,4z,HB 5 # e1,4,HB 6B # t1,q4,HB 7 # 1=,1-HB 8 # 1=,1-HB 9 # 72.,82-HB : # 2.,2-HB ; # 2.,2-HB < # E3.,F3-HB = # 3.,3-HB > # 3.,3-HB ? # S4.,T4- @ B*CDE@FH5%*-K XxI**-*<!$@e1.,4-HB A # "F"HB BB #  " "HB C #  "G!"HB D # 8!U"9!V"HB EB # * d"8!"HB F #  " "HB G #  " "HB H #  " "HB I #  " "HB J #  " " K BCDEF5%,,w-KwKKZwZZZiYiYxwxhxwxwYYJYJYYJJ;;,,wwY\@ "t!"HB L # +*.*HB MB # ,*.*HB NB # .,*,*HB OB # ,*.,*HB PB # +*+*HB Q # k+*.,*HB R # ,*.*HB SB # ,*.*HB T # ,*,*HB UB # +*=,*HB V # ++++HB W # ++,+HB X # ,+L,+HB Y # ,+.+HB ZB # ,/+.0+HB [ # +*+*HB \ # +*++HB ] # +*++HB ^ # ,*,+HB _ # ,*,*HB ` # =,*>,*HB a # =,*>,+HB b # =, +>,/+HB c # j,*k,*HB d # ,*,*HB e # ,*,*HB f # ,*,/+HB g # ,*,*HB h # ,*,*HB i # ,*,/+HB j # -*-/+HB k # K-*L-/+HB l # x-*y-/+HB m # -*-/+HB n # -*-/+HB o # -*./+HB p # ,.*-./+HB q # Y.*Z./+HB r # .*./+ s B9CDEF 5%FF;X*9;JJ;JJx;iiKJKY<w<-w<w--h<Y<h-Y<<-<<<KK<<<<---<<-<<KZiiZx<xZ<x<x;;@k+*./+HB t # =,*L,*HB u # L,*M,*HB v # =,*>,* w |BCDEF5% @=,*[,*HB x # , +,!+ y |BCDEF5% @, +,/+HB z # ++++ { BCDEF 5% @+++ + | BCDEF 5% @, +.,/+HB } # *%+%HB ~B # )%z+%HB  # *&+&HB B # *$&+%&HB  # )B&+C&HB B # )`&,a&HB  # *~&+&HB B #  +&+&HB  # )%)%HB  # )3&)`&HB  # !*%"*&HB  # N*%O*&HB  # {*%|*&HB  # *%*&HB  # *%*&HB  # +%+&HB  # /+%0+&HB  # \+%]+&HB  # +%+&HB  # +$&+&HB  # +3&+~&HB  # ,Q&,`&  lBXCDEF5%BBx-xKiKZ<K-<<-----Zi,;YY--<<KKZi i xx+:IXII++  YYK<-<<KK<<-@)%,&HB  # y,#-#HB B # y,6#-7#HB  # y,T#-U#HB B # y,r#-s#HB  # y,#;.#HB B # y,#;.#HB  # ,#J.#HB B # ,#K-#HB  # ,$- $HB  # , #,&$HB  # , #,$HB  # ,",$HB  # -"-#HB  # K-"L-#HB  # x-"y-#HB  # -"-#HB  # -"-#HB  # -r#.#HB  # ,.#-.#x  <BC,DEpFx5%hhiwxxxK,,-9<@y,"J.&$   BCDEF5%**I<IJwhJ,,wxwxhihiJZJZ;<<xxK<II<VX@`-+t1-HB  # )("*)"HB B # )F"*G"HB  # !*d"*e"HB B # 0*"*"HB  # *"*"HB B # )"*"HB  # )"*"HB B # )"*"HB  # )#*#HB B # )6#*7#HB  # )T#*U#HB B # )r#{*s#HB  # )#*#HB B # )#{*#HB  # )#l*#V@ 3 q4/  GHB  # )")#HB  # )")#HB  # )")F"HB  # )")"HB  # !* ""*s"HB  # !*""*#HB  # N*#O*#HB  # N* "O*#HB  # {* "|*#HB  # *#*#HB  # * "*T#HB  # * "*E#  BYCDEF$5%GGZKiKZ<K<KZZJJYY,J,;;;JYYYhhwZK<---ww;xxxxiiZxZxKiKZKZZ@) "*#HB  # t1--b4.-HB  # b4--q4.-HB B # S4i-q4j-HB B # t1i-&4j-HB  # t1-3-HB B # t1-3-HB  # 1.3.HB B # 1Y.3Z.HB  # 1.3.HB B # E3.T3.HB B # 1.'3.HB  # 1 /1/HB  # 1 /1/HB  #  2 /2/HB  # (2 /72/HB  # U2 /d2/HB  # 1-1Y.HB  # 1-1.HB  # 72-82 /HB  # 2-2.HB  # 2.2.HB  # 2-2.HB  # E3-F3.HB  # E3.F3.HB  # 3J.3.HB  # 3;.3<.HB  # 3-3.HB  # 3-3-HB  # S4i-T4x-HB  # S4-T4--P  BC DEF5%ww   xx xiZ ZiK< <,IxiiiiKKK--<KKiiiixxvvggXXI::II:I++J:J:wIwIhIw:w+ hhhYY;JJ;;,,     @t1-q4 /HB  # &4Y.'4Z.HB  # 3.3.HB  # 3w.3x.P  BZCxDE\Fd5%iiiK<K<-K-KZZKZZZKK<<<<K-K-Ziix/0@3.D4.HB  # D4-S4-HB  # S4-T4-  BC-DE(F05% -@54-S4-HB  # S4-T4-  BCDE F(5%@54-S4-  BCDEF5% @54x-D4-  |BCDEF5% @S4K-b4Z-  |BCDEF5% @D4K-E4Z-HB  # S4K-T4L-  BCDEF 5% @S4K-b4Z-  BCDEF 5% @3h.3.  |BCDEF5% @S4-b4-  BCDEF5% @S4<-b4K-  BCDEF5% @D4K-S4i-  |BCDEF5% @S4<-b4K-HB  # S4x-T4-  |BCDEF5% @S4x-T4-  |BCDEF5% @D4-S4-HB  # ,).)HB B # ,*.*HB  # ,!*."*HB B # ,?*.@*HB  # ,]*.^*HB  B # ,{*.|*HB   # ,),*HB  # ,),*HB   # -)-*HB  # K-)L-*HB   # x-)y-*HB  # -)-*HB   # -)-*HB  # -).*HB   # ,.)-.*HB  # Y.)Z.*HB   # .).*   BCDE F(5%@,).*HB  # A'D$B'S$HB  # '#'&$HB  B # P'#}($HB  # '#)$HB   # )5$)6$HB  B # '5$)$HB  # #($)%   BCJDE,F45%KKj<<<KKxxiiZ,<,<;-;-,,;;J,y[=iiZLZ=ZLZLK[<L<[-[jjj@2'#")4%HB   # -("Y.)"HB   # w.(".)"HB  B # <-d".e"HB   # ,"."HB  B # y,"."HB   # -#.#HB  # ,", #HB   # ,","HB  # K-U"L-"HB   # -("-"HB  # - ".'#HB   # Y.("Z.'#HB ! # .(".6# "  BC,DEF5%//g<v<gKviixgXgg,,hhK--,,wh-II-X<g<gK_`@y, " /6#HB #  # Q  HB $ B # `!!HB %  # B8!9!HB & B # 3V!nW!HB '  # ot!}u!HB ( B # !n!HB )  # !!HB * B # !!HB +  # !!HB , B #  " "HB -  # (")"HB . B # #F"G"HB /  # d"e"HB 0 B # "}"HB 1  # ""HB 2  # Q!Re!HB 3  # ~""HB 4 # ~ !HB 5  #  !HB 6  # ""HB 7  # ""HB 8 # ""HB 9 # s""HB : #  !HB ;  #  !HB <  # !F"HB =  # d""HB > # 2U"3"HB ? # 2 3U"HB @  # _ `"HB A # !s"HB B  # !!HB C #  )!HB D  #  !HB E  #  "d"HB F # ("7"HB G #   ! H  lBCDEF5%bb;;,;;YYhhwhw,JJYhYhJwJ;,KxxiwwhhYJJ,,iZK<x-x-iiZZ<<--ww-J-Y-J-Y<J<YKJK;Z;iJiJx;x;;@3 "HB I  # m""HB J  # _"`"HB K  # ""HB L # ""HB M  # ""HB N  # ""HB O  # @"A"HB P  # m"n"HB Q  # ""HB R  # ""HB S  # s"" T  BC-DE4F<5% -,Yx----@_s""HB U  # I/D$J/E$HB V  # v/D$/E$HB W B # .b$/c$HB X  # .$/$HB Y B # .$ 0$HB Z  # .$*0$HB [ B # .$/$HB \  # .$/$HB ] B # .%/%HB ^  # .4%I/5%HB _  # .S$.$HB ` # .S$.$HB a  #  /S$/4%HB b # :/S$;/4%HB c  # g/D$h/4%HB d # /D$/4%HB e  # /D$/4%HB f # /q$/4%HB g  # 0$0$p h  4BCDElFt5%;wwxiiiiZ<<--x,;;78@.5$*04%HB i  # )!r#!s#HB j B # !###HB k  #  #'##HB l  # ####HB m B #  !#"#HB n  # !#F"#HB o B # !$! $HB p B #  $t! $HB q  # f &$ '$HB r  #  &$8!'$HB s B # f D$ E$HB t  # u &$v 5$HB u  # u 5$v D$HB v #  5$ S$HB w #  $ &$HB x  #  $ &$HB y  #  5$ S$HB z #  $ &$HB { #  # #HB |  # )!r#*!&$HB } # V!r#W!$HB ~  # !r#!#HB   # !$!$HB  # !r#!$PX@ !e1j, J FHB   # !r#!$HB  #  "r# "#HB   # 7"r#8"#HB  # d"r#e"#HB   # "r#"#HB  # "r#"#HB   # "r#"s#HB   # "r#"#HB  # ####HB   # E##F##HB   # r##s##HB  # r##s##HB   # ####8   BuCDEPFX5%TT99fW-u-u<fKK<K9KH<<KvKgZIZIii xxYYJ;JJYJ;x<-<<KKxZZ<-ixxZZ-- @H r##b$HB   # "&$("'$HB  B # !D$!E$HB   #  b$ c$HB   # V!b$e!c$HB   #  b$ c$HB   #  q$ r$HB   # )!q$*!r$HB   # V!b$W!c$HB   # !S$!T$HB   # !D$!E$HB   # !D$!E$HB   #  "&$ "5$HB   # 7"$8"$   DBCxDEtF|5%iiZZiiixZZKK<-;-;YhwwJ;-<<KZxZZiix;<@ $U"$HB   # "#"#HB   # "#"#HB  # "#"#   BZC-DE0F85% <<ZKZ-@s"#"$HB  # !!7""HB  B # !!"7"HB  # F"!E#d"HB  B # "!#"   BvCDEF5%,,-KK<<----<KKvgg<X<XK:K+ZZi i   YYx-x-Z-K-ZY\@t!!#"HB   # ^)#m)#HB  B # )#)5$HB  B # )5$)6$HB  # ")#0*q$HB  B # )5$0*$HB   # !*$0*$   B;CDEF5%&&iZ-ZZ-KKKK<Z<Zii--<;<;xxixMP@(#0*$HB  # .*/*HB  B # *0!*f0?*HB  B # /]* 0{*HB  B # .*//+HB  # /!*0/+HB  B # 0?*G1*HB  B # /*u0/+HB  # 90*0/+HB  B # 0+0/+   DBCDEF5%==--<<KvKKXKgZXZgZXiXZIZIi:i+xx  +:II:IXXgXgvvi,i,<<--{|@.!*V1/+HB   # 0*0*   BC-DE(F05% -@0*0*   BCDE F(5%@0*0+   BCDEF5% @0*0*   |BCDEF5% @0*0*HB  # "* E#HB  B # " #HB  # ##HB  # T# #HB  B # r# $HB  B # 5$6$HB  # * # 5$   BCYDE4F<5%MM<J-J;,,-;Z;Z,i,iZKK<<-,-<<<KKZZZi,i,xhxhwwwhhwwwhYJ,;;JJ;Z;KJ<J<Y@"!S$HB  # '!' "HB  B # '!}(U"HB  # #(!)"HB  B # ( ")s"HB  # ")(")"HB   # )")"HB  B # )U"*"h   ,BCDEF5%::whJJxxxZZxKxZxKxZiKiZiKiKZ<Z<----XII::KIKXZIZgZgivivxxvvggux@'!0*"HB   # ,1).2)HB  B # ,O).P)HB   # ,m).n)HB  B # ,).)HB   # ,).)HB  B # ,).)HB   # ,"),)HB  # ,"),)HB   # -")-)HB  # K-")L-)HB   # x-)y-)HB  # -)-)HB   # -)-)HB  # -).)HB   # ,.)-.)HB  # Y.)Z.)HB   # .).)   BCDEF$5%-@,).)HB   #  //+/0+HB   # 0/+10+HB  B # .M+V1N+HB   # .k+V1l+HB  B # .+V1+HB   # .+V1+HB  B # .+V1+HB   # /+V1+HB  B # /,V1,HB   # /,V1 ,HB  B # /=,e1>,HB   # /[,e1\,HB  # ./+.+HB   # ./+.+HB  #  //+/+HB   # ://+;/+HB  # g//+h/+HB   # //+/+HB  # //+/+HB   # //+/j,HB  # 0/+0j,HB   # H0/+I0j,HB  # u0/+v0j,HB   # 0/+0j,HB  # 0/+0j,HB   # 0/+0j,HB  # )1/+*1j,HB   # V1+W1j,   BC;DE0F85% ,,gZ;,;,,@./+e1j,HB   # !*& +&HB  B # !*&+&HB   # ?*&+&HB  B # ?*&[,&HB   # 0*','HB  B # !*2'+3'HB   # !*2'"*3'HB   # N*&O*A'HB  # {*&|*A'HB   # *&*A'HB  # *&*A'HB   # +&+A'HB  # /+&0+A'HB   # \+&]+2'HB  # +&+2'HB   # +&+&HB   # +&+2'HB  # +&+#'HB   # ,&,#'HB  # =,&>,#'HB   # j,'k,#'   BvCDEF5%,,xxxiZZK<---<<KK+Z:ZIiXiXZgZgiviv,Y\@!*&,P'HB   # .1)*02)HB  B # .O)/P)HB   # .m)/n)HB  B # .)*0)HB   # .)H0)HB  B # .)0)HB   # .)0)HB  B # .*0*HB   # .!**0"*HB  B # .?**0@*HB   # .]*/^*HB  B # .{*/|*HB   # .).*HB  # .).*HB   #  /)/*HB ! # :/);/*HB "  # g/)h/*HB # # /)/*HB $  # /)/*HB % # /)/]*HB &  # 0)0@)HB '  # 0)0N*HB ( # H0)I0!*HB )  # u0)v0!*HB * # 0)0!*HB +  # 0)0)HB ,  # 0)0)HB -  # 0*0!*HB . # 0*0!* /  BvCDEF5%&&w--w<h<YKhKhiixx  ::IIgv;J,J,wwMP@.) 1*HB 0  # ?*(,(HB 1 B # ]*A(,B(HB 2  # *}(*~(HB 3  #  +}(!+~(HB 4  # /+}(,~(HB 5 B # >+(,(HB 6  # +(,(HB 7 # {*P(|*_(HB 8 # {*'|*P(HB 9  # *'*(HB :  # *)*)HB ;  # *)*")HB < # /+(0+")HB =  # /+}(0+~(HB >  # /+}(0+~(HB ?  # /+n(0+o(HB @ # /+'0+n(HB A  # +'+")HB B # +'+")HB C  # =,'>,")HB D # ,(,")8 E  BgCJDEPFX5%TTXXgJiJx;;,;,,,xxxxixi--xKxZZx-x-iiZ,@?*',")HB F  # +}(+~(HB G  # +}( +~( H  BCDEF5% @+n( +}( I  BCDEF$5%@)@))^)PZ@ < !r3[,  EHB K  # *)*)HB L  # *)*) M  |BCDEF5% @*)*)HB N # *(*) O  BCDEF5% @*(*)HB P  # *(*( Q  |BCDEF5% @*(*) R  BCDEF5% @*(*(HB S  # /+(0+( T  BCDEF5% @ +(/+(HB U # K !i)!HB V B # < !t!HB W # Z !!HB X B # ; ! !HB Y B # !!HB Z # t!! [  TBCDE|F5%--<<KKZZ,,x?@@< !X!HB \ # e1(f1)HB ]  # 1(1@)HB ^ # 72(82@)HB _  # 2(2@)HB ` #  3( 3@)( a  B:CDEHFP5%iiwK x+:%(@81(r3O)HB b  # I!8!!HB c B # +8!G!9!HB d  # V!e!W!HB e B # t!!u!HB f  # X!t!!HB g B # !!!HB h  # !!!HB i B # !!!HB j  #  " "HB k  #  G!t!HB l # :!;t!HB m  # g!h!HB n # !"HB o  # !"HB p # !"HB q  #  ! "HB r # H !I "HB s  # u !v "HB t #  ! "HB u  #  ! "HB v #  ! "HB w  # )!!*! "HB x # V!G!W! "HB y  # !!!!HB z  # !!!!HB { # !!!!  |  BCDEF5%11iI:XXgg-v-v<<ZZZxxxixiZZKK<<----<<KZiicd@!!"HB } # %!a%"HB ~ B # $!%"HB  # $!`&"HB  B # %s"&"HB  B # $"%6#p   4BChDEF5%;;,x,x;i;iJZJiJZYKhhYhhZZZZKKKK-<-<;<<KKZJZJxYxJxYYJ;;wx@$!&E#HB  # o'"'"HB  B # o'"'6#HB  # o'6#(#HB  B # ~'E#}(#HB  # 'c#(#HB  B # }(#)#HB  # )#O)#   BCDEF5%//xx-<ZZiiZZiiZZi,i,xJxJYhxixxxixxx-_`@o'"O)#HB   # .4%.5%HB  B # .R%;.S%HB   # .p%.q%HB  B # .%.%HB   # ,.%.%HB   # /%/%HB  B # .%/%HB   # .%:/%HB  B # ,&:/&HB   # ,$&:/%&HB  B # ,B& /C&HB   # ,`&.a&HB  B # ,~&.&HB   # ,&.&HB  B # ,&.&HB   # ,&.&HB  B # ,&.&HB   # ,'.'HB  B # ,2'.3'HB   # ,P'.Q'HB  # ,%,o&HB   # -%-o'HB  # K-%L-o'HB   # x-%y-`'HB  # -%-o'HB   # -%-o'HB  # -%.o'HB   # ,.C%-.`&HB  # Y.a%Z.`&HB   # .p%.`&HB  # .%.`&HB   # .%.`&HB  #  /%/B&HB   # :/%;/%HB   # :/%;/&HB   # :/$&;/%&X   BXC;DEF5%88,,;;,i,i;;;,;;JJ--<<KKiixxx::II:IXXIIXII::+ ,,,;,,,,;,,qt@,4%I/o'HB   # ,M+.N+HB  B # Z-+.+HB  B # ,+K-+HB   # ,+,+HB   # ,+,+HB   # ,+-+HB   # --+.+HB  B # x-,.,HB  B # Z-,i-,HB  B # K-,Z-,HB   # <-,=-,HB   # -=,J.>,HB   # ,z+,{+HB   # ,k+,l+HB  # ,M+,\+HB   # ,/+,+HB   # ,+,+HB   # ,+,+HB   # ,+,+HB  # K-+L-,HB  # K-/+L-+HB   # -/+-\+HB   # -k+-=,HB  # -/+.L,HB   # Y./+Z.,   |B C,DEF5%DDxi<ZKixZZKK<<-<,--<<KKZZixZZKK-  ,@,/+.[,HB   # [,+y,+HB  B # y,+,+HB  # =,\+>,k+HB   # ,+,+   dBCDEF5%!!<<K<KK<iZixxxZZxKxKZ<ZKZ<Z<K-K<K<<-<----<<<CD@.,\+,+HB   # j,,,,HB   # =,+>,+HB   # =,+>,+HB   # ,+,+HB   # ,,,,   DBC-DEtF|5%KKiZxxxx-Z-<-<-KK<KK-;<@.,+,,HB   # ++,+HB   # +k++l+HB  # +\++k+   B-C<DE<FD5%<-<<- @+\+,+HB   # y,+,+HB  # ,+,+   B-C-DE@FH5%-----!$@y,+,+HB   # +M++N+HB  # +>++M+HB   # +>++?+   BC<DE F(5%-<-<@+ ++\+HB   # --,<-,HB  # K-,L-,   BKC-DE0F85% <-<<KK-<<-@-+Z-,HB   # ,+ ,+   BC-DE(F05% -@,+.,+   BCDE$F,5% @,+,+   |BCDEF5% @x-.,-=,   |BCDEF5% @+k+,z+   BCDEF 5% @,k+,z+   |BCDEF5% @,z+.,+   |BCDEF5% @y,+,+HB  # ,+,+   |BCDEF5% @,+,+   BCDEF5% @+k++z+   |BCDEF5% @,z+,+   |BCDEF5% @i-,x-.,HB   # .,+=,+HB   # =,+>,+   |BCDEF5% @.,+=,+HB   # #!$!HB  B # # "$ "HB   # #("%)"HB  B # r#F"$G"HB   # #d"$e"HB  B # ""$"HB   # d""$"HB  B # F""$"HB  # d""e""HB   # """"HB  # """"HB   # "s"""HB  # #d"#"HB   # E#d"F#"HB  # r#U"s#"HB   # #"#"HB  # #!#"HB   # #!#"HB  # &$!'$"HB   # S$!T$"HB  # $!$"HB   # $!$"HB  # $!$d"HB   # %"%7"   BCDEF5%--,,x;x;i,i;i;KJKJ<Y<h----KKZZvv--K<[\@F"!%"HB  # )q$")$HB  B # _($)4%HB  # ($!*p%HB  B # )$*%HB  # )$*%HB  B # )$/+%HB  # !*%*%s@  !q40  DHB  # *4%/+a%   BCwDE(F05%JJ<ggY+Y+hhYYhhwwYwYYJY,h,YY,x,iZK,K<ZZKK----KZZxxxxiZZKK<<<-,,XXKK<<K@A(q$/+%   BCDEF5% @(%(%HB   # (%(%HB  # 8!U"9!d"HB  # 8!"!U"HB  B #  7"!"HB   #  " "HB  B #  " "HB   #  " "HB   #  " "HB  #  d"F"T#HB  B #  "F"r#HB ! # !"d"r#HB " B # "6#"r#HB # # "c#"r# $  LBXCDExF5%^^-w-wZZxx,;;JJY+Y:hXhhxwiwih-h-YYJJJ;;,,,<<KKZZiixxixixxxxxxxxiiZZZZKKK--@ ""#HB % # %"%"HB & # &!&s"HB ' B # &!o'"HB ( # & "'"HB ) B # 'U"'"HB * # '"#("` +  $BCDEF5%99;YY-KKZiixZiiiZiZKK<<--,,<;<;iJiJxYxJxYxJYJYYwhhYYJJ;;st@%!2("HB ,  # *1),2)HB - B # /+m),n)HB . B # +m) +n)HB / B # *m)*n)HB 0 B # *m)*n)HB 1 B # N*m)l*n)HB 2  # *),)HB 3 B # +),)HB 4  # +!*,"*HB 5 # {*m)|*)HB 6 # {*^)|*m)HB 7  # {*^)|*_)HB 8  # *")*m)HB 9  # *|)*)HB : # /+")0+)HB ;  # +")+)HB < # +")+0*HB =  # =,")>,0*HB > # ,"),0* ?  BvCDEF$5%GGvvxxK<xx-xii<i-i-ZZKKKZZZiiZZZKKKKKKK<KKZxZxK-K<<Z<ZKx<x-x<x--x--Z-Z<Z-i-Z-i-Z-Zxixx@0*"),0*HB @  # *m)!*n)HB A  # !*|)"*})HB B  # !*m)"*n)HB C  # !*m)"*n) D  B-CKDE0F85% -<----<-K@*^)0*) E  BCDE$F,5% @0*m)N*)HB F  # N*)]*) G  B<C-DE,F45% ---<---@0*)l*)HB H # {*O)|*^) I  BCDE$F,5% @l*O)*^) J  |BCDEF5% @*|)*) K  |BCDEF5% @*@)*O)HB L  # -$-$HB M  # -$-$HB N  # --$K-$HB O B # ,$-$HB P  # +$+$HB Q B #  +$+$HB R  # /+$,$HB S  # .,$[,$HB T  # [,$-$HB U B # /+$-$HB V  # /+%-%HB W B # /+4%.5%HB X  # /+R%.S%HB Y B # *p%.q%HB Z  # *%.%HB [ B # *%,.%HB \  # +%.%HB ] B # z+%.%HB ^  # *a%*%HB _ # +a%+%HB `  # /+$0+%HB a # \+$]+%HB b  # +$+%HB c # +$+%HB d  # +$+$HB e  # +$+%HB f # ,$,%HB g  # =,$>,%HB h # j,$k,%HB i  # ,$,%HB j # ,$,%HB k  # ,$,%HB l # -$-%HB m  # K-$L-%HB n # x-$y-%HB o  # -$-%HB p # -$-%HB q  # -$.%HB r  # -%.%h s  ,BCYDEF5%::WiHiHxWxWuuffYYJJ;x;x,Z,K<,,x;;YY-J-Y;;<<--<<--<<--::- - ZfZfiWiWxux@*$,.%L t   BgCDEPFZ5%ZZ- --<XgxKK<<xiZ*,@` 2)q4*HB u # X/-Y/-HB v  # /-/-HB w # *0x-+0;.HB x  # 0x-0 /HB y # 0x-0/HB z  # e1x-f1/@ {  BvCDEF5%55wYYJw;h;;J;JhhJJ;;,,;xxiiKiKx<i<i-Z-K-XXgvhvXIII:+kl@/x-1+/HB |  #  3I/ 3J/HB }  # r3.s3.HB ~  # r3.s3 /HB   # r3 /s3/HB  # 3.3.   BYCDE<FD5%OOiZK<--<-KKZixxxxiiZKKKZZZZiZZK-<----,;;YJJ;,-KKZiixxx@2w.4v/HB   # .W0.X0HB  # .90.H0   BCZDEF5%,,KKK<-<<<--<-<<K<K-K<K-Z-KK-<KZ-x-xx-xixZiZKKi-x-Z<-<-KKK-<<KZY\@.*0 /0HB   # 0 00 0HB   # e1/f1/H   BC<DEXF`5%---iixZx--<<-----<-0@0/e1*0H   BKCDEXF`5%<K<---<<-KKK<-<<-0@,.f0w.0HB  # /90/H0P   BKC<DE\Fd5%---<-K-K<-<K<<<K<<---</0@v/ 0/H0   BC-DE,F45% -@E3.c3 /   B-CDE$F,5% --@.H0.f0HB  # X/0Y/*0    BZC<DEDFL5%-KK-Z<K<<--#$@:/ 0/H0HB  # .u0.0   BiC-DE,F45% KKZZi<<-@-u0,.0   B-CDE$F,5% ---@'3.T3.   B-CDE$F,5% --@.*0+/H0   B<C-DE4F<5% -<<---@/0X/H0HB   # 1/1/   B<CDE(F05% --<<@1/1/HB   # /*0/+0   B-CDE F(5%--@/*0/90HB   # X/ 0Y/ 0HB   # X/ 0Y/ 0   BCDEF 5% @X/ 0v/*0HB   # 2/2/   B-C-DE(F05% ---@2v/2/   BCDEF 5% @00090HB   # 72/82/   B<CDE$F,5% -<-@(2/d2/HB   # .f0.g0   BCDEF$5%@/ 0/*0   BCDEF 5% @g/90v/W0   BC-DEF 5%- @2.2+/   BCDEF5% @.90.H0   |BCDEF5% @////   BCDEF5% @2v/2/   |BCDEF5% @(2X/72g/   BCDEF5% @/900H0   BCDEF 5% @://I/0   |BCDEF5% @s2/2+/   BCDEF 5% @-u0-0   |BCDEF5% @'3 /63/   BCDEF 5% @2I/(2X/   BCDEF 5% @F2+/U2I/   BCDEF5% @/// 0HB   # .90.:0   BCDEF 5% @h.90.H0   |BCDEF5% @ 2I/2X/   |BCDEF5% @2:/(2I/   |BCDEF5% @(2/72+/   BCDEF5% @/// 0   |BCDEF5% @s2/2/   |BCDEF5% @2.2.   |BCDEF5% @2.2.   BCDEF 5% @.90.W0   |BCDEF5% @'3 /(3/   |BCDEF5% @U2 /d2/   |BCDEF5% @/ 0/0   |BCDEF5% @E3+/T3:/   |BCDEF5% @(2+/)2I/   |BCDEF5% @(2+/72:/   |BCDEF5% @ 2/2/   |BCDEF5% @/// 0   |BCDEF5% @.0.*0   |BCDEF5% @t1/1/   |BCDEF5% @1/1+/   |BCDEF5% @2/2:/   |BCDEF5% @ 3.3 /   |BCDEF5% @ 3. 3 /   |BCDEF5% @1+/1I/HB   # y,!-!HB   # -!.!HB   # .!.!HB  B # y,!.!HB   # ,("-)"HB   # Y.("w.)"HB   # .(".)"HB  B # ,d"<-e"HB   # y,","HB  # ,F","HB   # ,F",G"HB   # ,7",8"HB  # ,(",7"HB  # ,!, "HB  # ,!,!HB   # ,!,"HB  # K-!L-U"HB   # -!-("HB  # -!. "d[@ N !4=,  CHB   # Y.!Z.("HB  # .!.("   LBXCYDExF5%^^Y-xxZKK--KKZii---;-;<J-J<J-Y<h<h-Y<h-w-w<<K<<K<KK<<+KXKIxIXXIIhw,,@y,t!."HB   # l!!HB  B # l8!9!HB   # lV!W!HB  B # lt!u!HB   # ]!!HB  B # ]!!HB   # ]!!HB  B # ]!!HB   # ] " "HB  B # ](")"HB   # NF"G"HB   # F"G"HB  B # Nd" e"HB   # { !|s"HB  #  !d"HB   #  !s"HB  # d"s"HB  #  !d"HB   # / !0d"HB  # \ !]U"HB   #  !7"HB  # 7"F"HB  #  !7"HB   #  !!   BCwDEF5%%%-hYhYiJwJh;Y,w,JJ,;,;,,;,;;JJYJYJYYYhhxYihKY-h-wKL@N !"HB   # {s"|t"HB   # s"t"HB   # s"t"HB   # s"t"HB   # /s"0t"HB   # \s"]t"   BCDEF5% @Ns"\"HB   # d""HB  # s""   BxC-DE(F05% x<-xxx-@\d""HB  # /)0m)HB  B # 90)1)HB  # 0)1)HB  B # 0@)1)HB  # 0|) 2*HB  B # 1) 2!*HB   # e10*f11*HB  # 1!*2?*   B+C;DEF5%00K--++;;,whY,J;,iZKKx-x-iiKK<<--<-<KKKad@/)2N*   BCDEF5% @e1N*t1l*HB   # .`& /a&HB  B # .&.&HB   # .&0&HB  B # .')1'HB   # .P')1Q'HB  # Y.`&Z.o'HB   # .`&.o'HB  #  /&/o'HB   # g/&h/`'HB  # /&/`'HB   # 0&0`'HB  # u0&v0`'HB   # 0&0`'HB  # )1P'*1`'HB   # )1'*1'   BCDEF5%**ixx    xxx-ZJKJiwiZZiixUX@.`&81o'HB   # .o'81p'HB  B # .'81'HB   # .'81'HB  B # .'81'HB !  # .'81'HB " B # .(81(HB #  # .#( /$(HB $  # I/#(81$(HB % B # :/A(81B(HB &  # g/_(G1`(HB ' B # /}(G1~(HB (  # /(/(HB )  # /(G1(HB * B # /(81(HB +  # 0(81(HB , B # *0(81(HB -  # 90) 1)HB . # ,.o'-.'HB /  # Y.o'Z.'HB 0 # .o'.'HB 1  # .o'.'HB 2 # .o'.'HB 3  #  /o'/(HB 4  #  /#(/2(HB 5 # :/2(;/A(HB 6 # :/`';/(HB 7  # g/`'h/_(HB 8  # g/_(h/`(HB 9 # /`'/(HB :  # /`'/(HB ; # /`'/(HB <  # 0`'0(HB =  # 0(0(HB > # H0`'I0)HB ?  # u0`'v0)HB @ # 0`'0)HB A  # 0`'0)HB B # 0`'0)HB C  # )1`'*1)8 D  B*CDEF5%44wwhYYJ;;,h,hYJ;;,,;;ii<<<*JJ*  il@.`'G1) E   PBWCDEpFz5%<ZixxiiZK<-,9-WiWHY9X:<@`V1*4=, F   BCDEF"5%@`1 +1>+T G  C 1>+1M+HB H  # +2',3'HB I B # *P',Q'HB J  # *o',p'HB K B # )','HB L  # )'j,'HB M B # )'j,'HB N  # )')'HB O # )~')'HB P  # !*A'"*'HB Q # N*A'O*'HB R  # {*A'|*'HB S # *A'*'HB T  # *A'*'HB U # +A'+'HB V  # /+A'0+'HB W # \+2']+'HB X  # +2'+'HB Y # +2'+'HB Z  # +#'+'HB [ # ,#','HB \  # =,#'>,'HB ] # j,#'k,'` ^  $BCDEdFl5%--<<yKyK[Z[Z=i=i-Z-iJ++34@)#','HB _  # )(?*(HB `  # ?*A(@*B(HB a B # )A(0*B(HB b  # )}(?*~(HB c  # ?*}(N*~(HB d  # ]*}(^*~(HB e B # {*(*(HB f B # )(l*(HB g B # )()(HB h  # !*(]*(HB i  # )')(HB j  # )()(HB k  # )()(HB l  # !*("*(HB m # !*n("*(HB n # !*P("*_(HB o # !*2("*P(HB p # !*'"*2(HB q  # {*(|*(HB r  # {*(|*(HB s  # {*(|*(HB t  # {*(|*(H u  BC,DEF5%vvZiiZZZixxxx,,xxxiiZZiZZKK<K<KZZKK--<i<KKK<KK<<<<-K-<-<K<<-<--Zi@|)'*)HB v  # |)}(})~(HB w  # )()(HB x B # )()(p y  4BKCiDElFt5%<---KKZ<Z<iKi<iKi<iKi-iZK<K78@|)_()(HB z  # )(*( {  B-C<DE4F<5% ------<@)(*)HB |  # )1)*2)  }  B-CDEDFL5%--#$@)")!*@)HB ~  # )A()B(HB   # )A()B(   BC-DEF$5%-@)#()P(   BCDEF 5% @)()#(HB  # )()(   BCDEF$5%@)()(   |BCDEF5% @)P()n(   BC-DEF$5%-@)A()n(   BC-DEF5%- @*)*1)HB   # |)')'   BCDE F(5%@|)')'HB   # *(!*(   BCDEF 5% @*(!*)   BCDEF 5% @)()(   |BCDEF5% @0*)1*1)   |BCDEF5% @!*)0*")HB  # !*("*)   BCDEF5% @!*(0*)HB   # *(*(   |BCDEF5% @*(*)   |BCDEF5% @0*)1*")   BCDEF5% @]*(l*)HB   # ,o'.p'HB  B # ,'.'HB   # j,'.'HB  B # j,'.'HB   # ,'.'HB  B # ,(.(HB   # ,#(.$(HB   #  /#(I/$(HB  B # ,A(:/B(HB   # ,_(g/`(HB  B # ,}(/~(HB   # ,(/(HB   # /(/(HB  B # ,(/(HB   # ,(0(HB  B # ,(*0(HB   # ,)90)TU@ _ 3* n BHB  # j,'k,'HB   # ,~',(HB  # ,~',")HB   # ,~',")HB  # -o'-")HB   # K-o'L-")HB  # x-`'y-)HB   # -o'-)HB  # -o'-)HB   # -o'.)HB  # ,.'-.)HB   # Y.'Z.)HB  # .'.)HB   # .'.)HB  # .'.)HB   #  /(/#(HB   #  /2(/)HB  # :/A(;/)HB  # :/(;/2(HB   # g/_(h/`(HB   # g/_(h/)HB  # /(/)HB   # /(/)HB  # /(/)HB   # 0(0(HB   # 0(0)   TBCDEF5%?? ,9,9;u;JYYhww+<-,-xxK-K-ii-x-x,,<<:i:@j,`'90")HB  # ,#-q$HB  B # ,#.$HB  # K-#h.$HB  B # -5$.$HB  # -$.$HB  B # ;.$.%   BvC;DEF5%--KK<K<x-- - x+::IIgvgXw;w,,;;iiKKZ[\@y,#.%HB  # .'#.#HB   # .6#.$HB  # .".S$HB   # X/r#Y/D$   BCDEF5%&&,hhwwhhwhwh;,,xiiZKKZK<-ZZiiZ,J<,;MP@-"/S$HB   #  2O)r3P)HB  B #  2m)3n)HB   #  2)3)HB  B #  2)3)HB   #  2)3)HB  B #  2)3)HB   #  2*3*HB  #  2@) 2|)HB   # 72@)82!*HB  # d2@)e2!*HB   # 2@)2!*HB  # 2@)2*HB   # 2@)2*HB  # 3@)3*HB   # E3@)F3*HB  # r3@)s3*HB   # 3)3)HB  # 3)3)   BCDE8F@5%hww-Z @ 2@)3!*HB   # !l!HB  B # 8!l9!HB   # nV!lW!HB  B # }t!lu!HB   # n!]!HB  B # !]!HB   # !]!HB  B # !]!HB   #  " "HB   #  " "HB   #  "] "HB  B # ("])"HB   # (")"HB  B # 1("^)"HB   # 1F"OG"HB   # F"NG"HB  B # d"Ne"HB   # ""HB   # ""HB   # "1"HB   # )!!HB   # !!HB  # ! "HB   #  !!HB   # ""HB  # s""HB   # ""HB  #  "HB   # @ AU"HB   # @s"A"HB  # ms"n"HB  # m n"HB   #   "HB   #  "7"HB   # d"s"HB  # U"s"HB  #  F"HB   #  !U"HB   # d"s"HB  # ! !"s"HB   # N !Os"x   <B CDEpFx5%\\xiww;hYYYhYwYhJJJ;,,,;,,;;;,,,,;JYYJ,,xxiZK<K<<--xxiiZK-K<-<----K-KZZZix hwJwxx@_ l"   B-CDEF$5%--@!s"N"    `BCDExF5%x,,iZKK<--xiiZZK<KK--,,xx>@@`*),*    BCDEF"5%@`*)*)    B<CDE$F.5% <--<@`l*)*)T   C *)*)T   C *)*)HB   # :/$&;/%&HB  B #  /`&f0a&HB   # .&0&HB  #  /B&/&HB   # g/3&h/&HB  # /B&/&HB   # 0Q&0&HB  # u0o&v0&   BCDEF5%))iZZK-K---<KKiiZ--<-;-J--KKZixx,ixST@.$&0&HB  # +%,$&HB  B # ,%,B&HB  # +&,&HB  B # ,o&,&HB  # y,&,&HB  B # ,',A'HB  # ,`',~'    BhCDEF5%11hhYYJJiiZZK<--<<KKiixxiiZxKiKZ<K<K----hhZhhcd@+%,'HB ! # 's"("HB " B # ("")6#HB # # }(")E#HB $ B # (")c#HB % # )6#)#HB & B # m)#)#HB ' B # O)#^)# (  BCJDE$F,5%IIKZZii;Jhh--<<-<<-<,,;;JhJwJYJY;J;J,,xxZZKKxxxixiiZK-K--<-<KK-@'s")#HB )  # p%"'"HB *  # `'"o'"HB + B # R%"o'"HB ,  # %%#o'#HB - B # %6#o'7#HB .  # %%T#o'U#HB / B # p%r#o's#HB 0  # %#o'#HB 1 B # 3&#o'#HB 2  # o&#~'#HB 3 B # &#'#HB 4  # &$' $HB 5 B # &&$''$HB 6  # #'D$o'E$HB 7  # 4% #5%T#HB 8 # a%"b%r#HB 9  # %"%r#HB : # %"%#HB ;  # %"%#HB < # &"&#HB =  # B&"C&#HB > # o&"p&#HB ?  # &"&$HB @  # &$&$HB A # &"&&$HB B  # &"&5$HB C # #'"$'D$HB D  # P'"Q'D$HB E # ~'#'5$ F  BCDEF5%EEw+ wwhhYYYJJY;,hhYY;;,<--xiiiZ-Z-K<KK<K-Z-Zhhww,,JJYwhhwwwhww@%"'S$ G  tBCDEF5%##x--xxiZZK-KK-<<-Z-ixx<h<YK,K,ZiixxxxGH@{*#,#HB H  # /$90$HB I B # /$W0$HB J B # .$.$HB K  # -%.%HB L  # /%f0%HB M B # I/4%05%HB N B # .4%.5%HB O  # ;.R%0S%HB P B # .p%0q%HB Q  # .%0%HB R B # .%0%HB S  # /%/%HB T  # /%1%HB U B # :/%81%HB V  # :/&V1&HB W B # :/$&1%&HB X  # /B&*0C&HB Y  # H0B&W0C&HB Z  # W0B&1C&HB [ B # f0`&0a&HB \  # 0~&0&HB ] B # 0&0&HB ^  # 0&0&HB _  # -%.%HB `  # ,.%-.C%HB a # Y.%Z.a%HB b  # .$.p%HB c # .$.%HB d  # .$.%HB e #  /4%/%HB f  # :/4%;/%HB g  # :/%;/%HB h  # :/&;/$&HB i  # :/$&;/%&HB j # g/4%h/3&HB k  # /4%/B&HB l # /4%/B&HB m  # /4%/Q&<B o # A<B p # @<B q # ?<B r # ><B s # =<B t # <<B u # ;<B v # :<B w # 9  x BCDE@FH5%PP:v<  *vvvw:w+ wwhJhYhYYJYJJYJJJJ;Y;Y,J,;JJ;;KK<x<i-i-ZZKKi i ::@8<B y # 7<B z @ # 6<B { # 5<B | @ # 4<B } @ # 3<B ~ # 2<B  # 1<B  # 0<B  # /<B  @ # .<B  @ # -<B  # ,<B  # +<B  # *<B  # )<B  # (<B  # '<B  # &<B  # %<B  # $<B  # #<B  # "<B  # !<B  #  <B  # <B  # <B  # <B  # <B  # <B  #   BCDE F(5%HHhhJJxxZZKKx<i<K-K---  x x ixxxxxxwwwhYYJJ;;J;;JJYhh@<B  # <B  # <B  #   BCDE$F,5% @  |BCDEF5% @<B  # <B  @ # <B  # <B  @ # <B  # <B  # <B  #  <B  @ #  <B  @ #  <B  #  <B  @ #  L  B+CDE`Fh5%XXh+   , , ;; ; JJYYhwwhwKwKhihiwxwxhxwwhhYhYhhwwwwwwhhhhYYJJJJJ;JJ;;;,;;;,;K;<JJiwhxhh@<B  # <B  # <B  @ # <B  # <B  @ # <B  # <B  # $  B+CDEF5%33ZZxxxxiiKKZKKxZxiixixZZxZZKK<<w<h-w-h-w-w++Kxxgh@B S  ?  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLM QD| at ^ m t @ 4t P | 4t S@ Ct } t D Et b# t t $Qt t RRpt 4t RBt CDt Rat D$t 3t qt Ct $t x$Bt 34t 34t 3Bt  `t t hit ;<t t t t $t $t Q`t Z[Qt -.`t Qt  Bt B Ct  3t y z t L M t   t   t B Ct BiCt $%t $%t $x%t~ $ %t} j t| t{ [ tz L ty  tx   tw k l! tv > ?! tu  ! tt | ! ts ^ ! tr " { tq ] ^? tp 0 1 to  ! tn Gt 6 t { SYrt $Z+tJ | M:t YtBwWtt t&att]tt tt$otOOT@ p@G:Times New Roman5Symbol3& :Arial7Sasfont"hd&d&!20U Larry Winner Larry WinnerWordDocument  SummaryInformation("DocumentSummaryInformation8*_1150714089F`<Ƀ 6ɃG bjbjَ O7]  z?AAAAAA$h\Ze( e?????????jT?iE)? pci94 7.500 12.500 17.500 22.500 27.500 32.500 NOTV[]cekmsu{}B*OJQJhnH  jUmHOUV\]delmtu|}      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOTUV[\]cdeklmstu{|}dN N!" #0$!% Oh+'0`   ( 4@HPXss Larry WinneroarrNormali Larry Winnero2rrMicrosoft Word 8.0@F#@@y< ՜.+,D՜.+,0 hp  Compaql   Title 6> _PID_GUIDAN{EBCCBF00-5B9F-11D6-A632-B6D72B3E7867}2_u;oTި4BNҙ'AIc_X M_ebok:E^cɰmlD%9yLϦr뢰˭,`#c2/m?q,l+lt7:]uM k =&'.BsCr*U'X|DX󉍬Ӊ?"4WHE.g}f5Xͱ8s!R4NBjd5R>RS~O-مpS[>/Zu'ӓNuGwH5ZY!O%)$WʕC YX gیScc<_⌕cx#CR] 2Q(%80 X4gN}7+2F0cqu$I(kA.Đd?gzP.rYd|<~X.,ceӢᴟ+;l~>(l_&E]ag\`Qp}P6?F>(ϙ(XEt0^}p χuZuޒ [P76<7AwF Fag|kŢW"=)<;}wW;C+LE{/˕Rʄ:&ۦ~BDG:QC^- Q75uwFΨOQ7,j38JzbZ8hy'F[,2^N G'';u4|rWE'^<1/ĨPGU'FU 'FU ÉQU޴:QUݨnbUuD_[q+,V8 gó,83m=86™ U 'NC϶ wxHOz:OGn?_͖j ߄fb&1x?L3ֿkǣ0_:A~x/?ObW6Ս׋gy_nF47?hozO?GtzVյI|P~A~ֳ%Lb ג|{8A{8uvKޝg, cW)əB~ս}eO,IK({r$}ks8Dd0  # A2)f=)hF$D`!)f=)hF$$f4%VxXkWݙ&lDHJHRS1CXǦTVͣ>[ H!tRP,-pg]g93w\>8 a k|߇C3 bX|,1fG>|?zĥ&Z3/ eWV:j^÷Ῡcg)e\|5Ձ٢8F9*VYPeDRu?)^j_g g~o}#MūƞJX.0:1;I,҆wShS C.][\WTܶ{&d%]_`btBLD(C;yUi^ژl. ƫ8Λ+<¨Y547]1bY6zsrƗxZU|%gl`6ؚ V0X`y 0Y/Cؼ6 i],N226ī.j?d~Y{hW #kQWYyEei1"p7%i Ϭ\' _,RV *ҭ;K7 |-=AG:Rq a"߸(e OEp,'yU|'yK U%zU梁ѯR2)=Nb?k9a1]ޚVWn[VJ]gH$Ey?$]섳lqIZ"O2cBf RT99 %s3ճ.UR3rai?,4{ 2Gxٻ L=j{A=Szډ3E+vV{ё^4#E:fě:7wND鱗ƃjM%}Y ٞo4wkwq55J2Ǚg:_͞|ma\v_U>b||DD Dd <  C A? 2|dJx󫎁Q?,X~`!PdJx󫎁Q?,@BAxN@g'@č ́S%ތ<Uz@P$>6(c\ +`ި7dsbg=-[Vֳx;*@XUWY]g )"?v%{g?0P_E-uZBu /?٩ (5hU|䕁9#AJ^s@mД>\ُ۶[PF&'#vܸŅk7yV}-3Ώ,/ݞiK6jϨkM06] qnj%BlXX&Mb}YNǘn5a\C p"Xk'SW5 1Ogw6*gݜ3 q&!Ls$S>#ߑLLq:G?xz?K.dnٚnzFz}e+\͠^'Ҝ_\_Uf0с> h'qwb-Ʊ5Ο;n{s}܏㚶ki/83O RɦDd 0  # A2k,Lr8Mx`!k,Lr8MVc3`xW;AwvS@@G"b`)"(^"lp\t&Aٟ/0Z0<~Mϫfvoq`fzz 4vC`=ȣIgB4 u+aYF')&DIj+$=,}"ţ* ``(B=»oc1@H53ƾB(O;>(yP0& ,KUiDM6 9{+yN>+O&~knZqom t'\W<ݏu1I|.~5܏؛%;bF[q#c-- -eZZv-e6mܭ$orgJs?&<Òj5vfj XMsVq:'ҡ3Ƥ"٬cU\Q/qb.WJl\K~{`XYӮsS-\q~=3dww$.K|j\xrͮq)+5q ?Ƨ_hT^ٽ4DmmEIjZחϒmDd--<  C A? 2T% G6!L~0(`!(% G6!L~0  0^qqx lTE9}ۖ#,B04 vKy116J"P" ZtJPD"RJ~k`2&:ʎMq!`jmIr9n5-U 8v $fN|nMRȎuq!0^K5CX Q-*gرJ䬌P@*8v'93DuiQwhE7%GKG7CujG騇+j׵T1ot$c=DCT1@u Q -U f !xc!qjBCTqj>WQ:8JCT}8 93D=vRus'l! :@HG<͑~R貖x&#~3DFUZ*AR bjWGU ytc 18hT-ޒ1ճEKV=S QF c20DUDA-UfZٴ CT+VG*T:-" c q\T-|ڔ= 'j7CĦqQ31Llttn)"JcQ"0g^狧*6b ? :jG7S֏; i0!J)Jmk\Z visioZK˶!.-wi5`iC]Z c5kc!!Md|ȯ(YT/OeY:z|1ut|Y:*0-Y1^ ;1Yt_4SQ޼iEӋt ̙%ʼnl$K'ڎX33 ŝ!2SMh2TwgO775L07vaM6b}tnwaQGe6 yJ?Uղ"G{V!*b["3MiA~h_9W޽>Znos'.}Dd]5}$o0  # A2"ulD),Qe`!ulD),Q6<4]`ZxXMhG~YcɶbZT:v~ U}69Ԡ:J&KPr3lR街PR/){؟O=K"H 꼙}՟#˖y潙b_ /$Ѩƴj Rcd"h:ڋU-'5)6GLZUN ?j|Yeͥ7szkNav*b`c6_,Hg84z5% ck_۾6 -ǯ(z:yL˿?.ߊ.c|Xfp Z՚7PD1֢Mܺ5m[(Ld?(.DP| \7Q\Psߪ۾Uo37!E&n0r-JM>(+-6q7?|7JMQvA'锴ʝt[K=}:ww\ ^=!tм;6i8SKէ+H{=%&Lڔ=,Ze{@c˦|g oٟ3m{rRΚ=|yo81}tsY2m=z'W^šId-q~$Λ"\Uپea}ꛡދ3? K$&/|u\'<~#[%M5l5OTV?C-[Dd]5}$0  # A2c_ 4lk`!c_ 4<4]`ZyxYklTE;wfn) RBS[YHPP%j4Z(-h~%`(FӠQ& G $B$MHФΙ;sw޽h􇻹;s;̙3{fWA&Bj;E(& xebS ^Hy'G \ ge戱/ HfhkZ koO&% Z|D.u;V0ZlY&y;IPK1h f]]]Kr kǩatMZ[̌ Ko-b:۵ X,1a.^#4~r܉"y{E q:*|'^m${S/3Vjt/n@/5 tq\nvXu]{0@':Se VQ= t-2v Ardv es@dEG@/ ,:­2*Ӆftc>qp4{f,=*zqyztӋ}:cE/z.WYO~KFY[g}@{rI[g JTg G#.vf^[$CZnk@x1pП@\PDy_f?Xк"FSݠ%,>#9Z$wǶVq&d^9vv\n@sI7SZ7=AޝcA96sR׏4a+K/V;iV[YcEވ6瑹3T,i3\vm_T#c}d/vdoTwwId E%}\=OS'Ho*E./#yVҤv~DV;O@>@Jc>}LMTyGx&=L+\4wfq1&? rP-"m ej& ]@i K(ިdF8AQI_oD|VGkSd 7H=9;=AJyJ\Z95%d.yT(XARU) [u nѫzsfiVkFT^ª_*DIcU Fz-{;֮[e ښ ȝ[}Dk/ wm-9vQġnGX!#j\;D.FMI##"#{@ZGʿ-Hc~I?"I 4H~!d[pUs$ ƲC$Ӭpyd/XrnPm/x(a&W[(}W0\z}P*n sLk}[ZK[j\e O*o~ER0*^ &WNom*Ԃ01<b[+\u{ǒ+\)(藩ے+\5QHA1:" 츂Y~ZX\zK`nru _(ĵ\XLg/i<\-e3t3W4drum'(~C6KyK}3 ^K䪚_`l-gR~nHUTS촏itgtZܹݞLQ)O5,hSpG&>:ӃE%=g~CbCB3ϔF  `!K76S;k>`B. Z(<ϲxl}{smnX؝H *ѴL!QQDI!ô6Q|HQB MTs/ÕD1d13E9`dG:?  `eIM7"$+< H$6=7U/M["mQwc[6ָmU /Dp$i)?_m[6oS3Ǘۂ |i{|T;U}p75SUg(~~"ֿ }MWoDYqܙ#>_Qwo}O?1/ӧi5i?>?G7\t?꺟N|{g$9i-ķCIbP(wA2 ;H⯰=&KI<,.99ò1~$/.â<,i۳xbEGyXP3ykïâ<,iEW,!g r#3k^|_t}eٝ8cvˋkհ(rj nc͵]aYNC\tOʋٰ(rr o%# [r3EW57fXt}UJcSe fX^7iaq &e1>ayݤl2uss>_7i.=6dwUwUe9>/֠:꿫6P&˵P ;.Y[t6=BI6dwxfmUFn3&Y`ݙڢvn3&Y`ݙڢvn3&Y`ݙڢM\ 3ڢqfE6Y\oݙ?2 C,mWUF(gtq;EGqQƨANmhp$emcTF o ')k2Z1|1*Ӑ D'(Y(k2Z1 2@d esͣN^%(_%[q|pbݙ?1:ژoLyB@=f{5`}̿|V5>ZҐɃꭉe W(k[B^'UoMl:(?k[g},| qY/S#PdwI^_µʿ(r$&6Fm&d9Av͓1uŦy+ 9k]akjkl→ܺ9uX W n * 3XKɵ4Rj7XKa\( _KPJEC-}@yI\BMIvڴiS贓N[άA<3Ua2_YN :L F" \Sw @ٝY(/OBB!4ewfMc8@YgwAg䃔jo7,w'Qs ݰJYU)vîr8emݤl ɓ¾h7l ')kBJɧ(kBozg(k՛B(kBwsWQ*rptNsx+d4} 9xv'kϴg{ ?$葜I^`=34 dTn zd$lQI2hC IF&ɠGF M2&$14 dTn zd$lQI2hC IF&ɠGF M2&$14 dTn zd$lQI2hC IMA M6qIF#9C*uZUaB~ש zתZuשZu*_L˯*9-_%O*yp\,>כH^]ڜLs6'y}(_O@ٝY|713˯ 2828i(s1\/ 7I=r$ ݙz9\/k P%tPC ݙzS8xܙz`' v;&_L޸\/ MٝY|r2/zj{@w^2e lP0<sq^ۆ%ʲ;\o3ˏ'ʟѵ%ZۃT3:'7R>$!}tNǖD+dNEYؒhOQ>$@}Eג|ZRE䃔]ՂM2*'QM MʰnJY+~PNv Z;em'OR֪ś2TlAw SlILC/@'uX ,eYCmr R*-_q5\~ByZG4ȝJd7?SCP|}Ƶ,:g;Y/eЍ2xP)dy wIrtGz-w\)du wr uz 5trI u wruz vrIP r' 0-`7 P)dy w=r{v;{ yrIP r'7-Wo7P)du wrow{ }rIP r'K?-~7JP)dy wrcэ}k)du wrݤuӍekX7)du _꺹1n 7CucaW"WXKB T~2/? g8mPW+,0TKJ?K,-+t _`3kGXPW ,}GiPێz2Z/zUeT5C^`{:Jkkt Bt'k[WB]eݙ PA_=,3k Vix  ]NZɷӸV ,@Ws,] [vcX 4{ 2t}a:A5tؽ:֢6d9uA5NC =MKBob kh5JPCOSP ;[f=GGC6̇yfW8>GGC6Gyf=Wc8{>GGC6?͟Y3ۋUgv/x\kQ5*~r, *x-aC?AKSdWgXZנz;6F ==>?d<+? |Ҥ*_zc_daL$G,oCH\?c9bX?F,{dYLɞr?wרF ,wdyɲ^mMHj\|Yy `2,eyy^Aښ\5oݫ}&W$j\Yy`2,eyy^qښ\y +3j38Ky^砞7՞䫭)r?Vda݊eyy^<_mMHjO xq9!PUZvn&<ڴ؛0%փIpĔ$[;jK+-'lf4 Ņٚ刳"2&qO k>'{_@b /gsSn+s $oɛ@b /g&[ y96K@b٫{ t\CZΎq1V_:-};}A6 --֛ z#yL^&F=w>ipBzv/ )V^pvNH i${#wBrdw'lNH#ٛCl;!9V;!doNH\Ev' w4}sL?k0Y3Xo;Mw9.3mpal#i8L p8./m0al#a㲇 64Jlh64.L(maиlC#0d՝rV- *dսrV]~1TϪLV}3fU8'8l!ɹnfWՔ $' @.2/0Z>gcf|v3;Odr%A"v7з_}`v۝p.oE2y#~',`A2#rMG6NX~K2_wOLowµ{ A푭e s7b;=vK nAC e0zZip.uЖzd[)ul#;K'|,4:a]s9 k2C͡G6NXC]"[B}KG.#Y۷ 6o9EgqdYf+g98f3Y9L1Yd!ߎ {֐SoG{=k)#k=5ّ|A6yZ> g <|vd-gͳ|b>;3YC>O1Yd!ώ y֐SgGyWsE߉dl>نEv"HNIH=liuv$drԤfvB6=eL.5i -qd\ZwHNU ' F2{l"1aLN4η)aLN4-Y3EW0aLN4Y"af.1)9i ;9nEcf.1)5i xzբ {$d*P˪^ά5őٿ=@rJ&5i OI.86ĿKCrJ&4 =4p0)5i 1Ďe1ly3G4+nͦn6c;F9Ec?#~,&Am1Ȧ_/7Adr->~l&Vvz_ `ރ$&P[ 䵴-z {6rֻIae=HrLn@^M$[02$M&7b nqoz&Am1Wz8`/&@n1z[}`I~]&7i o[3X?Ln@~4S&*_]z4Svc4S)n EwʾUwʖE7n&yL~rhf.7DVF֒\%@0fvWDeQ $Wr+ d7ߍyitW('ߟ&yL@z4o}IҨsw$d{IZ~[#7% XGn"YkKGAoB%D_ٿK:r;Z[} gF4;er;urf-iw P d7sJktK'UnU2 9I/Dkt9ѓpF*\j5DF,v(/!yL~rٙ M_kt+ fzk7<}نaB]mh@0~ =O:"PL`C!]ꠁ<=lau{wer?ȃv^k` &ɧer?Cvg5:{Ei<ꐁ[/[o `o|Z&:d o[3X[$Ƀ ix ɧe CvZ^zk` &ɧer?C޽xwMߕz[m4XoH~W&wC?',`2d7F^;ghM ?c02d7F^{3ϲcILKbۃY#瀺@bYgfsM̿ݸ8FuvS"B4bCkd@0nJvSfSlh7S&@gyHn7h6 iker5r>̓r)Dn*I l7%r)D)6Jker55r>Mn l Z\ jvS"B4bC$V&WZc ݔn*I l7%r)D)6Jker55r>Mn l Z\ jvS"B4bC$V&WZc ݔn*I l7%r)D)6Jker55r>Mn l Z\ j|(=^j_$kנ46 #o%Y9 d+Adz5[lb_F}b.+wɗ#0tCltrkFk54CUrjFj5Cl7riFi5CrhFh5tC4vaFV~A|~7[ >~V.[q3`Ň6K[ nz.b3`i5Z v+R3L -WvVge օc wӖd[)l= wߐ9f~݇7w7{@>w!r~! on}~C~w3|C>< sߐ9|f>܇l7s7y@>s!r>! |o+♬!fUZϪ3nY`VjVGN͏gշrL6+ Yq5%#%?43\<32g|D67 J^?=O>K3`Vp`n&άWƏbfY:#kN2 =$g$9$N2 9optYI&# Nĝ4'9$Y#ٟmefCaC6q'@ݧlh6yX6a?m=p xêd+U<,6lC0 6$OZm ӆJd* lidrن aB!d$G NWTNr?|$y&IcpxI& )~G=q~'.'~G=$jP3$%G NRIrL99N$p N4u2 dwYbP~L*OaBO 6t3ey ȺUx\dB?d莏W+]n{'WI⚯Y?.^"O2+7Ͽ,շϛjaAb>}ſ⚃Wyͥ_qo]{ՇWW_s̽k{^or˿tO W#s̽k.]?Zs߯?[`#f5ɥ > _SsX?K^3/?~/;~V[Ļq />l ߚp^ gc =?>_/ΕXYɞϮkc%pfvkR+p ^J<#C­#Nk%nf!ɛe&PCyo/yi\PKO\]%{A3$Cf>K9< a $#*9 ֑E^z+s@1|+wA0=$"P 3 Pfy-ɛdre]zm}Bi@rk1dWrD2ĶXAA؂Ni"ẼyЛş9K#<8,Ȑ]}Wg26Po[?*V& f yL~g 37|T&af/jg  б;+}~4@:"Zh+IC%:!4aBfB$tD'RIb tфԁV'j$Tΐ FUw{Дt ך?{s9tLoMQ]ɞ7:A k<&DH_Tdo9M^B[)r>Ե6?5lr5 ii*>LC&|HCuEbcㅍ95b[#t?pBk`WӠM~'' Ոb?,rGv W} _o1m>.FaE~EpG`Im8yHy3'ua6A*2}c9Q&?5lr5ȕd ˭H,rk[͐.YEeAg@~Mo;vS/ m"V&GS6y ӎ~/; D?E,J> s~"r',Wy>| )뒿N&/u[M"dfdj@]גlbK&橾Bv[\Zms%uɏ*rΠgS>O#t`^xqM|m5U"zQ $JER5AWj_^dȯ%R-巺(I~A~ UJ;?vJF}R88>F'EeǤ z1tء8T+Ƥj*|T#4E Z" p$4w$`Jހ*!ƌlM,rjR5Ky_g8jL3@cW&LE*KMG6.?ͷKrE#ih tpe ([2w%PDGbJZR,"E?1'E+d үSD!eD2 <1Q.[0"'(Xr16'jEisHL_d L_`A=.zЀܥ/Dc@ިϱ TC"׈tؼX_`@>j@VG ȿ&'O,.y8KL֓Gn2Eng"3Z"ӈ|=#-WbhWժl# *2E<9mKL_\Jl[(dW+2oTLP]EM3 _&d<&,m2E: ry"&o؀L6*w]MNQ%Bm6҇VҪ&P (ӟ}2yX * XYZaZbE4ȭ"CKvt#| !Ry~Xl1MiE~YdCy@+~9}HɃZ5j 6ᑧ?o9 d筃$W"gt*K8lG6!}~}q JEE:^ ~~KB4y6K.my1;|_+}?k_|"bk3鍉o0)xC]̳9xKO=G!25Q<}"ZjҤ( lr*6ܾ}jPˆȼZj\hDGyOUlPWX Vy*ٜ!cMܾ̋3 ),U-3gA^-^2=dAKBQ.S|}3 D6!ߣ"{^&*غ*-{=mwQj6 U M>ȃ8y #,Ȑucm7L@5N2KV{dT8ϱ"mjEۡ |c&6Aű"`J3A,rJ41?dDV#a|# .9EN.K_ z%,iW"/9F䯁x+LE׃r1"6Z˙6o n&A\rG*Z?#xOtqM,hln&gЂ됾I[ߋjU?PnS{^6!(l`AnL#}e4iyM6F !|ӑm?qr %_bd6yVϚշQ/HcS@6c1=Qkiy'L>Rܾ=g#]_Gi=.m?):Ӗ$_w_Q춈nqq$I_g?Dc_O.2Ow[t]p#Ƀ^ B(>HD?%[p8?JГQo;r-tRL\]Cf+l_&߮9l PWK"A͏)r#8"K&'6ȧ_~P,bms1fvپy6sbٱۅN#~QKA*6.ȣy1|hcys._`1uAA1!k(Pl.2EM+ٜ!C6sݳ܎>w[-ҧq%O4Yorjy}摏:d@T6o{ZQƧ}ů ?o 3.wK %KແXp<@5ΆbR>aCPL-R  K`F+&dTZRӣhSNJebtAsAe:봃m-QJCH- KBw"W-͡1w6IEw1wsـ" 4 ?5lr5ȕd󠲹EqSd*a<1P[f)rl s/*2P#vwVN62'Unea͡jH"7D3 ?{dv_Wd*< -a}2*佼D&ZzY<"KWi%nCh#覞AF* m3,kժ:Ym~TL䇙 Mn/iyMv}&SInScع݋op_TclaP7t݆7tĠ {SLR &}oE]SN6![yM Z{e3#? Xk%rꜰ)B69 4oV[e.3NE^&zw+r\zi!oV ԲmCMvPf663ӟV@VZ'SBjqshPǻBR\-{11yY@2zqqE k>N7#J+B;6#UHOY>"_KFfҳ!>Ռ:Wd[p6n`OoVJ{%cR|U{ҮrE#mr!yN-?2W;#{zDVv)u0# fLq w'ّPХlI$Mҧ."w9MVۤM~ޏ~da9GRnU.NUIa{?=h-rߏ|'l=HfR۩0"?1 wڰCH_bր߆rm6r{t];3l l"Ӟ<P/L{69MZ5ˠZ׀L{6R =_an6ly%rurƁxЊ>y"Z^#]y\ȼUBجj i aJRج:$i0w~w_yJDLYV sUy*{VO {sGZŀND={#{Yw6;irѕ;7K"Ө ,0k kY ?KX )DX)2M_Jc"-͊|+ e6ߩȫP2*b @]hq&E?Uza_yGkB%mTCAU݊ q-zeDDu#]p; }#[R}[78_ށ9'">_'z+>WǷuz_J$`DGoDimp=7]Rv1îeo.6ERvT'^-Ub/K%pIH8L(%2ki)Bݓy7KO&'o;v䥧h' ɃɃOle CompObjfObjInfo FMicrosoft Equation 3.0 DS Equation Equation.39q X=X 1 +"+X n n=X ii=1n " nEquation Native _1150714184F TɃ@`|ɃOle CompObjf FMicrosoft Equation 3.0 DS Equation Equation.39qF  2 =(X 1 ") 2 +"+(X N ") 2 N=(X i ") 2i=1N " N=1NObjInfoEquation Native _1150714217 F`Ƀ`ɃOle X i2 "n 2i=1N " [] FMicrosoft Equation 3.0 DS Equation Equation.39q, S 2 =(X 1 "X) 2 +CompObj!fObjInfo"Equation Native  _1150714627%F Ƀ{Ƀ"+(X n "X) 2 n"1=(X i "X) 2i=1n " n"1=1n"1X i2 "(X i ) 2i=1n " n i=1n " []Ole CompObj$&fObjInfo'Equation Native  FMicrosoft Equation 3.0 DS Equation Equation.39q  COV(X,Y)=(X 1 " x )(Y 1 " y )+"+(X N " x )(Y N " y )N=(X i " x )(Y ii=1N " " y )N FMicrosoft Equation 3.0 DS Equation Equation.39qg cov(X,_1150714784#-*F{Ƀ`@/ɃOle CompObj)+fObjInfo, s    Equation Native _1150714861/F@ @ɃɃOle CompObj.0fY)=(X 1 "X)(Y 1 "Y)+"+(X n "X)(Y n "Y)n"1=(X i "X)(Y ii=1n " "Y)n"1=1n"1X i Y i "X i Y ii=1n " i=1n " n i=1n " [] FMicrosoft Equation 3.0 DS Equation Equation.39qH  b 1 =cov(X,Y)S x2[7R-h-KbKdZ"֠+ʏj]]1I+0p!!DV#lK~TL~}ȻtДLY/WG@brԽ9}DW`''dS‡tBYiS"_j2e//"S/L/1YVDrڼ>SkDc,J5&)g3˭υ1Y6cfe3ͯOX&<"?ɲu^&Sza}1e\%RYx1Ƈq\(5/mZgr V!ha`RkfZ1.>#Zw/y5)&٤|aʘraRiK/ʃSEaەq j0rkه8)cw&MhkXlYo{nfgR岥4 a k*he(k\8L8wƅS;Ycpt)٠GXaG< _sW;Ρת\4}ed85|nʊ| Sy-|&mAlBl NAXkڔɏtfa:+MH19d4iup|Vkk^tF~R3g'h99ŪakS#W}:{Jϐ{4u=O<+Lz^@=O`NP l6naBV;d;>:>ߑ:UA ۭiX㍃ƄdyCXpׇ5 Y=5;{'Pwr=,ۂ6Ztא:0bt]㸝2IZU9FjmMZhCWX:w By.|vqm꠻8e4y\  r ܞam2=Tz'ZRѻ6ݖt׭ͩQmR;m[>!K) jEpU%&#E }ݺr2/PD;R$#OWw̌$b2$6Y(b(b]DNFOy1oʿ>!?QuEmF*&W%S6l[*PT.4U#FmP.8J$55PƜj eNw 4ýMgsvvKXl+Yn-렻ns״=(p9ݎ&fc<'A)q'J1S"wИt/ "p*܎D1$>/ ?/"N qf#229V9+@d[UN{cedTڲ,#cZWHYFh$ey\H\ PcfI)ΕY=2糉 |ߡ'!'dC"9r,c:1@s½?}gQCW} 0mCV,Ib;[U8chc4ưE䱠uȻ4Y6Wu@0ZB'p< y\UJ,Z؝-KD>OuE =HA_.hb}<dz~LMY.6J389*??.\owbo~UR6A6gf@^43WCOÊhZR3"=Nw>G}mm.SPEqJ4hz,30 xq;uH`%ɦz(ᬃPVIqL=;YM8nYp8"- ѵZ{* +vґXZgtysbY K,1,hGv,] *-s ǤpLߴ[t;h'JEu^!yv!{k4I1y ,|ҮʂF_@ omu15 rWʨWU{5=1 r& &wmAv8KRr TڍaDͶ? sy$gv(g39&$SX4|&F=չUtI8Iy[gA@E<+yγX!. _K_-|^ϋՠVۼɯu_[o,5,9&Vˏw>mǚm3K~_!(K@$ϠX)] q|}(T t5O5^9$s"$N_'IY,T2|J'u9ϷF*Z #QF4.H҈dϧ${'N6|$/wvF%{>fps;fta5ܛQ7 n)llrGL/gYn?O'tV{f$O㘔@T@ кIhyH%I Nå$kZBҰg{<G,vfJJ *&gr _Xbks ;XD3/?%@aT*/|*S}?J;}OmLzwVUdEx'`8!w iaIՁ>Cjyr8ɬڣ1ܻزjS-~)qNG㒅dzrb33DkA]r7*JI5&*sɻAs'sozrQǏD#>3yɾyiħ)oh1I5ڨ&畺mϚ\PۢFh3z:5P-z (E|\*Y%|=";5/9*q)~_GE(r.Wx)2B_m o5%=&'o K!z:~$W}EZI%iȔ *+rf)*%E(_R)Ks%+.k 8:LV˓|׶ݫki^ee.΂\nd =.,2,FoZX3/^K8`%Zjǵqs>sv{%L(:žfSͰ")g 6v"3iҴ i'J넕`V#݀h9FVŠqVIF%5S*r\#-cK⾼q_K %wKᚐSyK&n#G'6*Ofl>oDv!"'(QBW(r,/gEksÈp7vA\*rLNKT)"lmb9WLuxo3E{brA|"!&S4I뒿w:y< l"/KA-5 E^#&S4U6?ț䍠n27X*&G0bclu)EK.FnnnQ9 #Nr;&%Oy;cdzoSODn5xAuEnkf%bD~K mN]E ?PSBOAXDX?l>cDT#V$2!%"džFZbFAL )"rasFE+yt-VM鴤>Q z/~M&P@7:C27|RL}?Qd:-IևAufWCŽُ(r|-uY鴤q\-9 M':o)}bLPg0"e.5AL^ S&_(r%F,"7/Ϳ=`brK{TH6?0?{[&%#?6*EEs Є#kQD;~lDauzD۵o{-ܼ"r"zԩ;tRz:y$='zF݄~0ur<~Lg-÷^Dhj$ɭQ)&Uꌆ;(S'wUF(r|A%^RyPCrGL_yjoh7O@j|=OuߪɳAm`?("E#|Ad!YQ"r?j#?)[%&o kk\'&ׂZ .@}INSģt95ţgk⚴ =HRS =jwȿ+;,H]\;/DV7z)x)[w2SI-§8$aW{"Wt+ .wK?RdWDŽWD>J[lȎZ}A_p̌Ad2&cE,AyYLn_+r|C6FOݠo`'|^L>Y툌y~Lk9OLTUKY=I!z[ l3E#E>dP:g"''b7$}X5㔉OBe^FF~86{pwp^FF.uiL,"_2? ~ҘFoSo!PBrȕF)Y=Gװ٠6ۏ)r\ _P:1ZC?,!&Am3R#s+aswknس[ {lP.Ǫu[A+x?U}!W!RUwCoai*g iBN+Я ˃rë55{,f4ZFoG+gJݬ%n/tgvP"[NJX[`/&rПNȩ 9m0yrŵ3ְ:ݝPvA=ڹylii/Cˍ|P}`W lן$9)۞l#I2ɞ\7W\ga4V\k`<2O>\e{.ˬoRO @5rPQe"ӹ/>d?M.QAKy:e`s"%yjwh=VGf>{v)Fn?xMD9M'U0xTgU wA+1(:k$Y䒴ee@HZmkkU|Z>h?k)HmDKs[m1}"/M>r{>=$KUo*b Pg9KA] #jԁ|"%rJm^Amkf,fwKUݧӛMPſ+2Y]9K!֟[pc9H#@~RDrWϩ&_ׂD1CD^]M=>Ŋ*"uC?ZMk0FG+Im \D.vZsz#2ES_s=D<7ƕm>hrڍt"7|ɳV+KǍ7k49 ML.]_*/^&"w9ft'/W[\?r"?)&WZa`ӊ\j͵KLjͯ(/}3Ma l~OO]vjSD6SN1ns@qWk)fNLH_52SPFB-:pUK'?IwZ羒H:,Gv*f(pϑ%Ŭ-ƍb~Z,haMe=-1W9sZɫ B͍i^'=Sƃq W/"=G{HU >8I:ȷ^=t]Y lGmΊDCl+YFdawFR ɓye؝qv+nfn7zSz֝n=voZ5H+XX'Qujxw["ǭh)k(뜕iMl(d@^4&mn(Bj.D=qFdJq4"Q횈~"L{"X/5evy;0V9;:ǮGm8ŗ+mvQt6JPf1d代~|IWjqdFV:LvGdeY%ͱYP;]ZOlbt=ؾ̾ڞZVW)ţچFZvF<5 &y- p"WqJeL䲠b#2vw ml g:#"{t 7чD`Tj#r=w mށ\gD~Y.asIe'f8>"N+ a~D>Dž6'}EWf|Ze'`~D?EMU qOMEd D~[ 5"SLH=WXڻḵgD6[rr Br @bό @-mU< 6O2||73 <_L 6K_P왗D4;{Y#&uUp65,&oft.}]GL ,mkM<ѷy=t.isqg* A^mD^Ȳ9Fwd9܇+<r৐cIfCk*rmu]N-k Dy\azf-5*;*nj\J 3*f6f*;7,aQ'KdJڙHqh-dpL4tVDъ.3Hxъ(:slP>*RS^\|?m|b2QOb2Q]"qU.MP~ȃ#4]n[@?BՊ*eN| r#~T*~kuȯ+v#7.9VyXE}v_:d~#E>'$oDXGֻdzԇQ,\qOBˈ]>p`*2Z$P'FY{N A=W6Z(ynyРPdZW*Џ'y-"6ObFh*bez(Ui^ϳqɢQ6L}u^7~sg"+(~~>TzP?fo2dӑB·rqO6+ԁŌ#~RR'0*YnLs9l,O?ռٗ=a55G8ʪ`3w^3wfyj}ޅ{[_qԋ6ΔӘ{`wjb(Gqt9kaH]Bwz{VI o'ZVՈILuwұ"|}/YUdžf[jFcEy*xʛ8ln3M,xmcU=)ocuiM 7n ҀG1V51\@T3p@{ҏD@{XVAMV@ţD%ZZQ15o0=zJ(G$MLѫ}-Tj5QJO~\19»nbP7TdjDži'3߅t݉71 &:x2:;ݟLij—,7XjG22QWr=W._ Aq+ S\Hp=2+]fc3܀{ﰔXD&-ffd4Husmρ6b`$o#dJes 496ǧl)~tdmd:u8Gtb:1u^Y'=4>un*)`@yBL\9G*) ΁'ӌ}6hgԛm)ɲ *7<דZIM턼wZ;㵄Ncr+7I5Nv{=jbE51y_{V jU-B7\k>ZQT 1j@,[jAS5mr!66?K:Qvbd^y'=#hwЪ(IGYvO|0 .# ~E^5iav?dI+ {M^{ډ|ڥ=9 +W66ӹ2jPn]&x6{ݠ6at:7p@L>8Ow+;@D% ɿS&ofj=A )ת ^h`H^+` -:JuFDݎU<;5 ͷI=7)w9uI^L^ Ruɫ$J5i,O(14$OV"{+Dz>XUEs;쥹1pn/c h=/Fɦ/鵦;euXyNJ6ͳtW+\sn,k2zXQVγ%>ЅY_J+{,RѭMvpB;`orTu Q$ᭈ-tZA7 _b^,j8֟uUOi):DNNVlMs/_1V5wp^J{О#2OPY;IeW߉<=_  9 jU ʳ&VZ섶JnKpE.:ŬEN)e# %?@fv2Xh4ӱ)ssd7)R45bB%bvH5sXvoFlؙț"ֈv=Vx[ğ!!sK/_g$w/gWM2sЛ}+f9L> FW=ς0ȷ(yyxp׌#21Jd8GF~2AޡDv'dZahG'^j#9G^D&xȩ& {2yvUao=]%~iwS#հo: Q&/ M eu%% X+X!.}a̳千eMgK8jcIs;ҝ iM# Ez>Z9p9}ـrb7kQN@'%8 g3fO8w"LobE[F֔Ti&$id;&#~l/8&)PJgXQ(5`Q~O 돨-L\aw~ʵ&kSt 6ZuN- ^>77 VFTnW: Amߵvh=3'p:bmS/ EU!Z?w0ywv$<7Dkxndy"g]^Ϭ5LkĴ1I<5i |i^ۘ8icZx9tjc:Y#IV.(ltO9vq9Úc8T8>n~ޕFw+5*:mnn!%r;NBk%y2IL5ˀ[ᑧ+:(JrP%i@'8+=2,DFH_+3mH5dB$W"WHs#: >>kp$35ةR"3F;Q9=4hI{It3 gheIP ݺ$y2r4Wm;sIdA_zPA%%m uL.Z*T2"$"ԾH' Σ;nCkD-ҠzBfyX|"ϟ- sO> ;*NY% SSaq3vPy'$Ƃ[-8ki=ğvl5Q%yD܊<'9OCEO7RMT(fOS,:Oߟ{KM7(32mXmw"&5=NEa~A39AH6(ma{Xh'3)F+Wwo$y(0/'Զ>gzl4 Vz'^tc@ {OUۍۏkYW#W>UM%$zjd+6`1}2Jd,QG؈^&_'ƀ%IުLȀL"1APm7t?Cݠ6_bd.)3q z;L\$)FIuu*bFd]UD#jV%]]hf|$Z5*w"򽒬kը0۵lF]F -ZwTkw}wj{G_F{LZ3Nk%9_< 4{rfV*׀ZiJ2[~{V)M"A\9[ r)P&ׁZg (2ɇA=%%KX29v ͿSP{ jՇr,y^|#X]Q۵J,9Ib45ݵ do*L%S*Q&ϋ9Vf2۵j6AI]4z(6݈#ji1~YoSŴSSFvgmV7 y쿐"jlw w5+B$+D5kĮe9OBfEݬ4Jø>YiN+kĦv ˛]F I0"'MFIӝ 5JW9h{Q*ޞL,LeG^K̎aGއ9VIM_*}mdAbmdVb#6RX$B#_,=g1qv>O;*v.v z.O{Mky1%rN|_4'ެMX; N.F&$o](T@vTEŽ䃓4\۔A=.v&)"sB na-`!WJDEXǓ IY0wr^Oi/:u2:-`y ðh):v jGδ]3ĥj#cd[Xsq=ZqR5 9Q!HڣvZ\%r}Hk"iDKIV)o$$;Sܞ!MdiHxnD 4*{`OA>$-KkCPybd|Rn1UaK%1銿*8wE8'G8?S$2DLdC39[0)m;lW"C,>wtN$Tp1#G/IU=JlR9i7cF䣒|BUL{CKh L. VG6A 7:VۘmpMlS|,JVw^h%NJcNX,/$)[.sbu*=rJpJ8V(p Utq̛w~,?B!N}AwۤS@d"ۉ:hpa]X:v.?=5-I|C{:}bGVj:_>>7b5󟞜{ɑ(YC_ENZ Rܛ$H[8i)6ڹ Q-'chkW#աaxcqpRJW'/]YVJx'5 xD.f ́(UgXm7I XQl7ث 䱚r7$TOA~t(aGY&b-.KdA *E-gWd455I"IW;cV|oՍvueχf/&7qR>lR>%VX)Xkcp +χyh@sy,@ӗp JG F}[_\S+2kfү_#ϥǼ~^ay?G`qyDc⠥jYwwgVʋ ԏ #dVjRo2=N?&Kѳ/Xտy]ܫ+7GklVpe?B>F0 C$D-DCU[otf.~Ҙ9J_#XcR8akd{^]ȁX姲P6f)lԇ[&S~>bBþ q(?? 4A ,ِ_1I1wPK^?mǠ'^ra -Rpmh =_˙'5c=h3Kz'Tk`3zg|Z$&yWm"rTF/-AT"ϋ5/azj%" 6*ӑw&߮'5׭wufFY{-+Fo7~įZ:h–J[{?7%DV®cnme@k"L¤8 ?t# O۪`">K/Z/׷k+U]gyuqebjm!r$zºjDVL3H3FdW~Dv!/h,O!)o[~B&I?(-[$i>43"$yo4iKi#}<~5QO## 8~,per5d5ZcI2dJ36`]>ڼ2 +dZyTBwXsCn/wE2+ B؆V:IXI$Qɭ&-I*i!vCP[!ay 1&;sb!DVCɛ$lfP7k$yb I~-X5 vSM'װJ7t,'c9*5jf}ɉBG@؍4(bJgtiV 2ջ`e{M0$W!m" OJrǽ+fְ9Ͽްn4"'|O6@:C4y7NcDKF>waZ$>CHe|n;ʹIdZIAw/ʪo3y8aP|,DNR|B'i yHRz&#O zs:J|^Q)ʴ25BxNً=_5K3kYk4;èf!~s 5PJ=H6'b^ջp?Rc*…F'*9E{Mމ f[|>&r#ƒ,rm0F-q|);EƗkA"z`gZdױ,7 S.Fo od7&2U\&WtUaZ#6+L{\[>EuteiM-iu73P6A"n~tRf3%KQʥ7ID4L~$oXdiBGX<9kFl?k􇒜& B694l~EK䝠Ԝ).g ydP1i AZUcQ͚!T! h҄`Gh-j뻅VcZCDž[4#wDž{-B|͘ ʹ/4X'QX1bMmd# $>nx)kRj 07]t{@kc^ {9zDH=2R*s=. bOٷErmvtn7:5׾%jǥRFD#)Q)vitj_[٫=\m c.2`< K%epBQfWm}'hf?K#f6}tVR-ot U CݥjՆOy$fkY}BIy3qgUV~5`p3إV>aȼZgUVNK~E&|m' ՠVǨa'2ȱpӰ7|Mn8%9 [j͞4'Jsm$gǂ:QvI&Ouߔ<6y.s5z%6y4l'I^&5Ku_esz &BP AW%&lre6RNcϓ: DLn@Z"ѥBJLJ[LEbB;0 %se3.D 9(/I}vz{M5B%>GM7nқ\qsL-a]or_f69jnLorM !~ 'di+ɧvP5iћ\mk/KW%, jg\&PL[2C69G>Q6[ՆȩX]w3 Eȼ|ܤ1|&zI.b(r ,Ƀ E^j-RI^$Y}껲Vy;˵n Gȃ-^ނ-kI@VoI>>wzN>'zI"eKAP9v݂#$ᓬzQ▋ 5j`FxF|'ڃmPfe ]:-JDH9) ssgx aU؉v]~/nM(B嬁 5VeX)TϡBxCPE} [] K? mr\y0iW4-bb>ТA7[}Fri(F@7qdfIwsΊ>KJdwsǝ*kn_M;[uO%e }'@^bVZա')(qguHG$r5^4[?Hglr7ݎg!].96[nޣtm+,c6Y4v>Ca3XqX#gqnij&i4l~IK"PkY= ZU6,:B<-?EKf:VMa'zbfk~Oy6ӞMcr Dž?\ԧBN~OuvN&M@'L'Itb,vjv^;[9bw ƅXB.!I(nMJ)N9@:% &P@Z,N9]$v#Lϲn9'InH2"g3 dG|\|;b1p" 2(6F@PsMx*7 "w/lt{2XQ)bV Q(g E(\"t9K(hNǠz#\2cLA9YWYe2J0Qɛi8 82Z=?gnWw6݃@`Sr6'7Ҽrc`Ҽ#`Bs.Yx*Tv#jV# mZT~VK`"w9OzȈ -0Q՝h2(>iz{@#z#,˷I";AOk#_i"4H|fn߲llE&0q,鐇ӂ0is}(2B5`N^&kGt7L3$-2>5%fQ_|$OPCr M!!䩠Nհ$^6PѰAI~*w} q:Ed=  lrOJ$A]a%M~5l~M+2Pa󛲯""סz+2^N=|C5t[ΤAʠ=MDZ)D#A#ĨNhDBRΝ!fhCpUhZh+@ΑYi A9__fe'w1mZ,ֲ o@o0m.*jI~E{ hk`lnP&gLVTJпSOBI6y'4L#wzgH)ɟ2L#xV|\)y3Hmur$f_Mu,`OR%wI!.SP'ɝA,Y"۬{'ƙ`Py^%dė IyDfw_L}~mf_6tpvL |N"S\b%"o >^9Q ʶeAF7eDnOX]tK4˔ɵa@=v><>ц%C6G4JX\|>|> /l#x^'V>wFF>wK>jF9أQB!-z:R}KmgJH=GHo`zحJ wAa"Yhn膤z3}tAm8T>EyC~x:O#A҈# DHx>JҙAbTՀQ~|sЉ1p,T gyŝVAk4A:zJhq!zōj$ɟZv,lV$GXgu;͸ƭB=&D:|\P,D|KdҎ.b' ( 7cBgͨys%T՝lU/9{l]RfVdIGXf]Nc hu楋V],5e9Ie[b,)dVQ1Y3e,lFd9/[} ߼^{?}{yAQ{h,0xl-PoYZhMfcnyYouKۏ?:VEM )5_@>,*ޤ"/NUұ0cKfRf.T֦w<:~WE^GoRI;8*TzS}TX;sPޫۋjT5[b;ʟ+A=c\US&o'J4?*!EUV5U\/}"znZ)wHk[E` H6+OW@<r4K QR,y IQҡ?BDuu k\*zKTI'E|{S 8;'u:kMyCoK<( :ygmn&?ĶgRm``oͭ|9y^aw(ۜ oM~CGE~7(͝䏇}gTiVh?|^PnU7`ٚ|@A;&ȖKEnhwӚu8w*-lwaU^Qk@\ɽVy8hݰ&Gh-(hPoM~%QUl߀ `w<9$lɠƚ!< ޑ cru*HC6_dȵj}1;m&7|@mphCn '.e=khQh'fSj(vn/m6vWW2ÉKkC?5>5y3[BP{{=#_gXOI?7'Bny/#Nh5~JQ5\^4D͢ah@<6z`zBwv2%*Л'^ ez:haQ AVV;-RhXԈ{skdQ-U">&vq0!G+ ,xW6ўg9 %.,+{)fQT;~)se|ePBЖP6r(k<Q拊4dY7T EseQ"JQr!KQVKJ^4ݚ@:&}ƞ-g3[V f2Gj5'JTQ<}5VC֑kdm4;/z#wWz]Q#v5/75t8?TrNEegK/q^MT>9&Um>˕3! "cN(TcBTt"s$ȑhbdWގzfȮ3?m~|C}/bK֐7ā|!&3u78n9JY̒qtr^MTވ),9ol߉!rAGlos>kT-O7ZmȜe&yG:? OĜefueW|]fI&s=wI|}L" y|4d)r7=>ɹKݢi3?oUgYƙ\ ʰ,s >fȳ}?mȜeyFN1S#1Ӝ|O-|r8||b3{k8 F?{UU>5W/:(1^U_~1[S Z؞iXW͐{b]QyϘ2+w2uA"=';!xGʌwJΉV2f+5E}=#㸈nwKw:q鿚{|xu6ou}_W2c.4Pw>揟_2ߐu1 f&ɺbf?~r MnNړ%MaRm؟}Æ|^%&²VMrtS\&_%d~6v긨iAOE1d~2[O+zNOȗ)ɵNdOd wwLNKf/i!JNS"bԝI1:mYf[}o0ɉO$|Cvk6dthۆ(]]6sFT* Tg.zsUC(&:c@=)|tX *@BG TJ|[9iVO V(e(4P߫jATIu8j%NtkΔSq T-:!%_r|v 4!kw*{ܯ9 '^ߨl:s?wӄ&'z3| np~f;41Sh˕OiǗoXh Y?u|63u9-4Gsam1=56!Mb;OtZ܇ l7ζŎ==!{;d0_#zvy.B9AY:cmE^V4"(JB*. S)IDd b = c $A1? ?3"`?27ƀT$Lr`! ƀT$LrAL xcdd``d 2 ĜL0##0KQ* W@HRcgbR ~RnĒʂT/&`b]F"L ,A,a K&v&"rofabM-VK-WMN.10@aoĵUMӯ/bGT+ّs"72K@('=L`]o}ȬgB~ "2ksCCq/(Bܤ:Zv.K\qx?l`G2Wc[8kE{X&@̅v_geC7,aBv(/3Ǐ:+Ϳ {|C\+J &οS%?EĹŁdnn%4rs88X v 0y{e#RpeqIj.ŠV "z> 1C3X?½<Dd b > c $A2? ?3"`?22.=_;٦n@S`!.=_;٦n@S^AL xڥS;KAݼ.GrFB 3 &E$WBXAKX )R(sw|A;nﻹ%cq !!Gжm鍒'nX{(-{x򘗆bZ3bF݅4.Nlt7vqc1kTT+sUsX,sZyWƝ4Xl)\Ӗ? xu:yue^\ciPX3ColFˣAG$; _;AaUx+` oC xڥMLAtP  *+pp&ihb55`(7&Mx41&&^W/5* M4&:b}y7|/hd#!ElfM] :+D5BB@>AI&@vQT-PTj&҉[J+{ͧ |"MO(nLA~( 22[{yN 50]ek1;M]4:X <zf6JSҡ3gj\@~k 0c)Dd <Ob @ c $A4? ?3"`?2sCc!J圄Y!wO`!GCc!J圄Y!wr`0 J0, xڥKLA}l XTy! $&XP!5pN&ƫ4.M4Gugfw!U1l@!Tz)DAh * l+ ˲s16>y@(PcyVw cظA>Je+:'謃фӔ}~.KIe rܔ5LYȔ)S5elbmܥE@۫. _.rvh3 ]Oz>J]5+D2,syfՊ/8 qp^sbp,6PW"g|qK>r~-sxzK(,d>l`ovo5壑g M5|2 oni%$U@\TJ}H'y=|z850v*i< vXLC$꿣q2S/ *!y;[s<2hk?eD+hWV*eբ}=D~C.E_P-1!ȟt vWz!9Q!gkD4*DUwyϠ[G// VQqV3(r9RDh[}s,bCw_o]wwNpV^sy vCj{d "3(vw3_=Dd D b A c $A5? ?3"`?2 OE ~q~H;,3T~`!OE ~q~H;,3T~<3PL xڥKhAǿټ6I!v[C$CA(Bmn!`Xi& 1^=yR| A=((gJ zR zy.3!HԅI7fײ CL~ qC )5SPe.("{bХb^!6gsm"[̗e/Ydd~Տ^: ٵNks9Oo1UBW%ֈ4Y(ik9/YgnOm}b7wLԛx#*M|`X%ڛjHvYR'©2X[Ĉo8wPdP/p^vEf*x`{e~2Ws-|tx~ oq 1m_V]W,ͥ mauaJ9݉$>Oò~nH֯{E}S}MuuH {V)akyk+/B0{3Eϭ8~3)g&deU'}A-,?KhzAǏиh7q~Tfc;I ~~sן$7X=ޡY_7:hOu3_ԏԪK%_"f}g4-ba}&r\8dbj\M_7lDd  b B c $A6? ?3"`?2 =')|$Q`!=')|$Q .@8HxڥKhAgg7MWVTۭEEJA<@QVɡx ^HlBiƝR_a773}# B29T4Q$%bH[4W y~nvGVUP8#~u98\ kה:Ԋe%cECq:ÕBNk@)k\ε3 }?lLi1I5bTõZ+f]o+\ƪ=/Z䌂fCQh3E~4mc""o7E*)az]uW-8W; l_;6㡵<g4D C.o0?ATřGdcɖ6wxhKy wy π>2DHXObjInfo1Equation Native d_1150714975(74F@歳ɃɃOle  =(X i "X i=1n " )(Y i "Y)(X i "X i=1n " ) 2 FMicrosoft Equation 3.0 DS Equation Equation.39qCompObj35fObjInfo6Equation Native o_1150715068<9Fs ɃPwɃS b 0 =Y"b 1 X FMicrosoft Equation 3.0 DS Equation Equation.39qJH{$ X=3.14+1.34+1.12+Ole CompObj8:fObjInfo;Equation Native f4.52+2.60+1.85+1.46+1.55+2.16+1.4110=21.1510=2.115 FMicrosoft Equation 3.0 DS Equation Equation.39q_1150715048>F~Ƀ@HɃOle CompObj=?fObjInfo@Equation Native _1150715140CFpɃ`\HɃOle CompObjBDf      !"#&)*+-./012369:;=>?@ABCFIJKLMNOPQRSTUX[\]_`abcdfilmnpqrstuvy|}~X$ S 2 =9.97805010"1=1.11S=1.05 FMicrosoft Equation 3.0 DS Equation Equation.39qObjInfoEEquation Native _1082130919H F@%YɃڽɃOle $  (Y"Y  " ) 2 =(9.10) 2 +("0.76) 2 +("1.35) 2 +("1.59) 2 +("2.19) 2 +("3.19) 2 =82.81+0.58+1.82+2.53+4.80+10.18=102.72(X"X  " ) 2 =(496.7) 2 +(98.3) 2 +(27.3) 2 +("199.0) 2 +("217.4) 2 +("206.0) 2 =246710.9+9662.9+745.3+39601.0+47262.8+42436.0=386418.9(X"X  " )(Y"Y)=(496.7)(9.10)+(98.3)("0.76)+(27.3)("1.35)+("199.0)("1.59)+("217.4)("2.19)+("206.0)("2.19)=4520.0+("74.7)+("36.9)+316.4+476.1+657.1=5858.0 FMicrosoft Excel ChartBiff8Excel.Sheet.89qCompObjGJ%bObjInfo'WorkbookIK,SummaryInformation(L(  ABa= f=h`8X1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *   p  p "x@ &x@ U Chart1YSheet4 Sheet1*Sheet2+Sheet3ZR3  @@  w SUMMARY OUTPUTRegression Statistics Multiple RR SquareAdjusted R SquareStandard Error ObservationsANOVA RegressionResidualTotal InterceptdfSSMSFSignificance F Coefficientst StatP-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% X Variable 1MexicoBrazil ArgentinaColombiaChile Venezuela A/ SFbMMN 0Ir0SFbbSFM MMRFS0Lbt00MMM^n00b 00Ԣ0bbb0bRFbtb0b(ZFbPercent0]yF b Ν0\b0ZT0_T0=l0Te0T0T|?b 0|)bb@NJT0e0YT0L0b@b|0IbI~0BT0bIbI0bu0(1T00bbblb|0bIZb&b0bZI 0ZbE 0ra00b  A"p Ftion??es3` ( ` (  ` (  ` ( .@3d23 M NM4 3QQ ;Q ;Q3_4E4 3QQQQ3_ O MM  MM<4JK4D $% M 3O& Q4$% M 3O& Q4FAy 3OY 3*#M43*#M4%  x=M3O%& Q  GNP'4% pMZ3O& Q $Defense Spending'4523  O43d"  3O % M3OQ443_ M MM  MM<444% O@IM03O& Q 2Defense Spending vs GNP'44e@\x@s@ W@fffffR@`U@eL,@)\(@ @Gz @zG@(\?e> (\  A  dMbP?_*+%MHP LaserJet 4000 PSw odXXro@ F4RdCustom page 1XCCCustom page 2XCCCustom page 3XCC"dXX??U  T0 b  b   eb    [ e  e [  e eSUMMARY OUTPUTRegression Statistics Multiple R k?R Square9t#?Adjusted R SquareHQ'?Standard Error0C? Observations~ @ ANOVA   df SS MS F Significance F   Regression~ ? $H3V@ $H3V@ T 9@ Wgxل}?  Residual~ @ F:+@ F: @  Total~ @ :vY@  CoefficientsStandard Errort StatP-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% InterceptPM?I?/E |C?pv?r>ۘ^Ƣ3@r>ۘ^Ƣ3@ X Variable 1V%$ ?d}h?=D8@Wgxل}?N'{?BD ?N'{?BD ?&@5<A?HE?,p}[H>@  A  dMbP?_*+%"??csU} T0bbBrazilA>@@Mexico@z@ Argentina"@ w@Colombia W@u@Chile@q@ Venezuela`U@@g@ud)),+((  p  6NMM?P]`I  A"??3` ( ` ( ` ( ` (  ??/3d23 M NM4 3QQ ;Q ;Q3_4E4 3QQQQ3_ O MM  MM<4JK4D $% }M 3O&Q4$% }M 3O&Q4FA 3ON 3*#M43*#M4%  w2M3O%& Q  GNP'4% oMZ3O& Q $Defense Spending'4523  O43d"  3O % }M3OQ443_ M MM  MM<444% M:=M03O&Q 2Defense Spending vs GNP'44eee >@  A  dMbP?_*+%"??nU>@  A  dMbP?_*+%"??nU>@  Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@ x ՜.+,D՜.+,P  PXh px Compaqh  Sheet4Sheet1Sheet2DocumentSummaryInformation8,_1082138961sO F{ŵɃ ɃOle 4CompObjNQ5bSheet3Chart1  WorksheetsCharts 6> _PID_GUIDAN{F58D2F3D-6057-11D6-A632-973C719EBF67} FMicrosoft Excel ChartBiff8Excel.Sheet.89qObjInfo7WorkbookPR>SummaryInformation(S8DocumentSummaryInformation8<             #  $          ! " 7 8 & ' ( ) * + , - . / 0 1 2 3 4 5 6 A [ : ; < = > ? @ Z C D E F G H I K L M N O P Q S T U V W X Y n o ] ^ _ ` a b c d e f g h i j k l m p q r t u v w x y { z } | ~   IBa=f=hT8X1mArial1mArial1mArial1mArial1mArial1mArial1mArial1mArial1mArial1mArial1mArial1mArial1mArial1mArial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *   p  p "x@ &x@  Chart1Sheet1 %deanZR3  @@  ?M MonthXYSUMMARY OUTPUTRegression Statistics Multiple RR SquareAdjusted R SquareStandard Error ObservationsANOVA RegressionResidualTotal InterceptdfSSMSFSignificance F Coefficientst StatP-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% au 2\bMMN 0Ir0*\bb*\M MMD\S0Lbt00MMM^n00b 00Ԣ0bbb0bD\btb0b[bPercent0]yL b Ν0\b0ZT0_T0=l0Te0T0T|?b 0|)bb@NJT0e0YT0L0b@b|0< ~b< ~~0BT0b< ~b< ~0bu0(1T00bbblb|0b< ~Zb&b0bZ< ~ 0ZbE 0a00b  I"parDdard??ti3` ( ` (  ` (  ` ( п 3d23 M NM4 003QQ ;0Q ;0Q3_4E4 3QQQQ3_ O MM  MM<4JK4D $% M 3O&Q4$% M 3O& Q4FA5*U3OU  3*#M43*#M4% + khM3OR& Q  Production'4% u+MZ3O%& Q  Cost'4523  O43d" k z 3Ok z% M3OQ443_ M MM  MM<444% MuM03O& Q 2Estimated Cost Function'440e`G@ףp= E@GzD@QE@(\E@p= D@\(D@{GE@p= ׃D@ QC@ 33333C@ C@ (\C@ fffffC@GzC@QKC@QEB@p= ׃B@LB@ ףp=B@= ףp=B@ C@zGB@QKC@RqD@GzB@QeC@p= cC@YB@̌A@@@Q%A@ zG!@@!Q>@"333333<@#Gz8@$@4@%ףp= 1@&33333,@'Q8*@(#@){Gz#@*Gz"@+ ףp= @,33333 @-@.@/RQ@e)\(W@p= 3V@\(U@333333V@QU@HzwV@R!V@ףp= V@GzNT@ GzT@ (\"U@ ףp=jU@ HzwU@ QNU@U@Hz'W@p= V@HzU@GzNT@fffffT@(\T@{G:T@{G:S@fffffS@GzR@fffffQ@)\hP@Q O@GzN@)\HS@GzR@HzQ@ = ףpP@!HzP@"Q L@# I@$33333E@%zGC@&ffffff?@'33333s=@(Q=@) 3@*\(\4@+Gz1@,{G:3@-ףp= -@.zG&@/zGa)@e> /z  I  dMbP?_*+%MHP LaserJet 4000 PSw odXXro@ F4RdCustom page 1XCCCustom page 2XCCCustom page 3XCC"dXX??U  T0 b  b b    b  b  T b  b     u. b SUMMARY OUTPUTRegression Statistics Multiple RwQ?R Square B?Adjusted R Square^8,9?Standard Error.Qm @ Observations~ H@ ANOVA   df SS MS F Significance F   Regression~ ? oae@ oae@ 0=@ Cz9  Residual~ G@ mA!@ 8pC@  Total~ G@ YcB@  CoefficientsStandard Errort StatP-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept\ @T@.' ?Sl?FXv@FXv@ Xk#7 @V>%?W+K<@凜z9Do?@7j/@Do?@7j/@& @5<A?HE?,p}[H>@  I  dMbP?_*+%MHP LaserJet 4000 PSw odXXro@ F4RdCustom page 1XCCCustom page 2XCCCustom page 3XCC"dXX??UColumn AX1T0bbbbb T b  b u.b0bbEbbeE)T0TbT0Month X Y?`G@@  @z@X@  @Z@@  @@X@  @t@@  @R@@  @3@J@   @}@@   "@@@   $@ @Z@   &@@@   (@@@   *@@@  ,@@@  .@@U@  0@&@@  1@@@  2@@n@  3@@@  4@\@G@  5@@@  6@ C@@  7@@ @  8@&@@  9@@!@  :@l@@  ;@N@@  <@L@A@  =@@@  >@l@"@  ?@@@{@  Dl8****************************** T0!b"b#b$%&'b(b)T*b+,b-./u.0b @@ʪ@}@  !@@4@@ ! "A@2@%@ " #A@@@ # $B@4@ I@ $ %B@@4@ @ % &C@@@ & 'C@l@@ ' (D@|@@ ( )D@#@@ ) *E@p@ĝ@ * +E@0@П@ + ,F@x@@ , -F@@ @ - .G@@P@ . /G@@@ / 0H@w@ԓ@ 0 &@****************(  p  6NMM?]`~  I"??3` ( ` ( ` ( ` (  *3d23 M NM4 003QQ ;0Q ;0Q3_4E4 3QQQQ3_ O MM  MM<4JK4D $% M 3O&Q4$% M 3O&Q4FA3O = 3*#M43*#M4% < MZM3OR& Q  Production'4% q!MZ3O%& Q  Cost'4523  O43d"  3O % M3OQ443_ M MM  MM<444% JfM03O& Q 2Estimated Cost Function'440eee >@  Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@ᢔ ՜.+,D՜.+,8 PXh px Compaqh  Sheet1deanChart1  WorksheetsCharts 6> _PID_GUIDAN{EE8F475D-5B65-11D6-A632-F329463F6467} FMicrosoft Equation 3.0 DS Equation Equation.39q_955976462YaVFɃ` ɃOle DCompObjUWEfObjInfoXGEquation Native Hh_955955733[ F -Ƀ`LɃOle VPRINTZ] ~L`hI8cI X  " =15.5200Y C " ="2.4882Y A " =2.4281X 2 " =124.6354Y C2 " =461.7296Y A2 " =195.4900XY C " =161.4408XY A " =84.7527 FMicrosoft Excel WorksheetBi856      q ''  '  p '  p Arial@- 2 Closing<.*..2 Price7*.2 ^Price7*.2 Price7*.2 %ChangeJ<.....2 @ %ChangeJ<.....2  %ChangeJ<.....2 Date <..2 S&P 500777...2 ^Anheuser-Busch7....*.7.**.2  Coca-Cola<.*.<..2 S&P 500777...2 @ Anheuser-Busch7....*.7.**.2 Coca-Cola<.*.<..2 5/20/97.....2 A829.75..... 2 z43..2 66.88.... 2 . 2 @ . 2 .2 `5/27/97.....2 `A847.03.....2 `42.88....2 `68.13.... 2 `2.08...2 ` -0.28... 2 ` 1.87...2 6/2/97....2 A848.28.....2 42.88.... 2 68.5... 2 0.15... 2  0.00... 2  0.54...2 :6/9/97....2 :A858.01..... 2 :541.5...2 :67.75.... 2 :1.15...2 : -3.22...2 : -1.09...2 6/16/97.....2 A893.27..... 2 z43..2 71.88.... 2 4.11... 2  3.61... 2  6.10...2 6/23/97.....2 o898.7....2 43.38....2 71.38.... 2 0.61... 2  0.88...2  -0.70...2 6/30/97.....2 o887.3....2 42.44.... 2 71..2 c-1.27...2  -2.17...2  -0.53...2 7/7/97....2 A916.92.....2 43.69....2 70.75.... 2 3.34... 2  2.95...2  -0.35...2 [7/14/97.....2 [A916.68.....2 [43.75....2 [69.81....2 [c-0.03... 2 [ 0.14...2 [ -1.33...2 7/21/97.....2 o915.3.... 2 545.5...2 69.25....2 c-0.15... 2  4.00...2  -0.80...2 57/28/97.....2 5A938.79.....2 543.56....2 570.13.... 2 52.57...2 5 -4.26... 2 5 1.27...2 8/4/97....2 A947.14.....2 43.19....2 68.63.... 2 0.89...2  -0.85...2  -2.14...2 8/11/97.....2 A933.54..... 2 543.5...2 62.69....2 c-1.44... 2  0.72...2  -8.66...2 |8/18/97.....2 |A900.81.....2 |42.06....2 |58.75....2 |c-3.51...2 | -3.31...2 | -6.28...2 8/25/97.....2 A923.55.....2 43.38....2 60.69.... 2 2.52... 2  3.14... 2  3.30...2 V9/1/97....2 VA899.47.....2 V42.63....2 V57.31....2 Vc-2.61...2 V -1.73...2 V -5.57...2 9/8/97....2 A929.05.....2 44.31....2 59.88.... 2 3.29... 2  3.94... 2  4.48...2 09/15/97.....2 0A923.91..... 2 0z44..2 057.06....2 0c-0.55...2 0 -0.70...2 0 -4.71...2 9/22/97.....2 A950.51.....2 45.81....2 59.19.... 2 2.88... 2  4.11... 2  3.73...2 9/29/97.....2 A945.22.....2 45.13....2 61.94....2 c-0.56...2 -1.48... 2 4.65...2 w 10/6/97.....2 w A965.03.....2 w 44.75....2 w 62.38.... 2 w 2.10...2 w -0.84... 2 w 0.71...2 W10/13/97......2 A966.98.....2 43.63....2 61.69.... 2 0.20...2 -2.50...2 -1.11...2 Q W10/20/97......2 Q A944.16.....2 Q 42.25.... 2 Q 58.5...2 Q c-2.36...2 Q -3.16...2 Q -5.17...2 W10/27/97......2 A941.64.....2 40.69.... 2 55.5...2 c-0.27...2 -3.69...2 -5.13...2 + 11/3/97.....2 + A914.62.....2 + 39.94....2 + 56.63....2 + c-2.87...2 + -1.84... 2 + 2.04...2 W11/10/97......2 A927.51.....2 40.81.... 2 57.. 2 1.41... 2  2.18... 2 0.65...2  W11/17/97......2  A928.35.....2  42.56....2  57.56.... 2  0.09... 2   4.29... 2  0.98...2 r W11/24/97......2 r A963.09.....2 r 43.63....2 r 63.75.... 2 r 3.74... 2 r  2.51...2 r 10.75....'CompObjWfObjInfo\_YWorkbook% "SummaryInformation(^`Zff8Excel.Sheet.89qOh+'08@\x College of BusinessCollege of BusinessMicrosoft Excel@'b@dDDDb@@@@,=(q|s?dDDD,GwqG?dDDD,bBdDDD`c@@@Q@,v-K dDDD,JU1U dDDD,u   dDDD @d@b@@Q@, F% @ dD DD, ޔ @ dD DD,  ֿ dD DD  e@Aa@E@E@, w$I͚ dD D D , ͔? dD D D , | B dD D D  f@X@F@PQ@,  [oEÿ dD D D , @ dD D D ,  Mo dD D D  f@q@@e@, ~H@ dD D D ,  dD D D , $xU? dD D D  g@@߰@Ϻ@, ħPv?dD D D , stO.dD D D , d}odD D D h@@E@}@,ҵ!tdDD D ,u-?dDD D ,֕2jO!dDD D i@@n@`M@,ѥN dDDD,U{ dDDD,r;#dDDD`j@1@@@,e,1@dDDD,Dc @dDDD,j @dDDD@k@@@c@, dDDD,dDDD, FdDDD l@@O@d@,=O @dDDD,A@dDDD,$@dDDDm@q@F@J@,=P@dDDD,2AcdDDD,KqdDDDm@4@@@,>T@dDDD,zE]t@dDDD,&IX @dDDDn@@@2@,3dDDD,XdDDD,MJv@dDDDo@q@`F@^@,u]/7@dDDD,i dDDD,2O?dDDDp@@ @@, ~N?dDDD,}dDDD, DӮdDDD`q@ @ E@@M@,7_5 dDDD,âM dDDD,HovdDDD@r@A@ʯ@K@,?(ѿdDDD,؉؉ dDDD,5H4HdDDD s@aT@4@@, bdDDD,i#;}dDDD,BI@dDDDt@@@L@,-}?dDDD,eum@dDDD,[?dDDDt@1@@|@,jE/?dDDD,#Y'@dDDD,(Gp?dDDDu@Q@ @O@,VI? @dDDD,7G@dDDD,ҽ %@dDDD@.DbbL>@ DocumentSummaryInformation8^_955987952c F tɃ`Ƀ1Table9 CompObjbeej University of Floridatsj  stk_beta  Worksheets 6> _PID_GUIDAN{F3E1D8C0-DA92-11D1-8237-0000034B20DE}  FMicrosoft Word Document MSWordDocWord.Document.89q [$@$NormalmH <A@<Default Paragraph Font  @誅@@GTimes New Roman5Symbol3& Arial"0h4%f4%f0 Larry Winner Larry WinnerWordDocumentB SummaryInformation(dfJ DocumentSummaryInformation8R _1082187082i F`)Ƀ@9ɃG bjbjَ  ] !$Z   2_y / =!"#$%Oh+'0`   ( 4@HPXss Larry WinneroarrNormali Larry Winnero1rrMicrosoft Word 8.0@@,Jy_y@,Jy_y՜.+,D՜.+,, hp|  UF   Title 6> _PID_GUIDAN{2348BB80-E518-11D1-9505-444553540000}Ole gCompObjhkhbObjInfojWorkbookjl\ `" FMicrosoft Excel ChartBiff8Excel.Sheet.89q Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@uR  ՜.+,D՜.+,  ABa=f=h8X1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)0.0                + ) , *      8 Chart1(Sheet1 Sheet2w!Sheet3ZR3  @@   TreatmentPlacebo25 mg50 mg100 mg # of subjects Mean Std Dev # improvingDose  i SFbMMN 0Ir0SFbbSFM MMRFS0Lbt00MMM^n00b 00Ԣ0bbb0bRFbtb0b(ZFbPercent0]yF bΝ0\b0ZT0_T0=l0Te0FL00FF0b]0 bb NJT0e0YT0L0b@b|0IbI~0BT0bIbI0bu0(1T00bbblb|0bIZb&b0bZI 0Zb$  A"`v Dovin??3` ( ` (  ` (  ` ( п3d23 M NM4 3QQi ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O& Q4FA{=3O?s  3*#M43*#M4% S M3O#& Q  Dose'4%  MZ3Oh& Q  Mean Response'4523  O43d"  l3O % M3OQ443_ M MM ] MM<444% .WC M03O& Q .Mean Response By Dose'44e9@I@Y@e@ @ @@e>   A  dMbP?_*+%MHP LaserJet 4000 PSw odXXro@ F4RdCustom page 1XCCCustom page 2XCCCustom page 3XCC"dXX??U} } I }  T0bb   Treatment # of subjects DoseMean  DoseStd Dev # improvingPlacebo*h@k@q@I@25 mg*X@9@t@9@@K@50 mg*@Z@I@ @I@@@T@100 mg*@Y@Y@@Y@@@U@Id,BFF( tIIļIlI p  6NMM?xi]`xI  A"x??3` ( ` ( ` (  ` (  3d23 M NM4 3QQi ;Q ;Q3_4E4D $% M 3O& Q4$% M 3O& Q4FAcI3O# O 3*#M43*#M4% s yM3O#&Q  Dose'4% J MZ3Oh&Q  Mean Response'4523  O43d"  X3O % M3OQ443_ M MM ] MM<444% URM03O&Q .Mean Response By Dose'44eee >@  A  dMbP?_*+%"??BU>@  A  dMbP?_*+%"??BU>@ SummaryInformation(mkDocumentSummaryInformation8o_1082217717pF`ڞɃɃOle wD PXh px Compaqh  Sheet1Sheet2Sheet3Chart1  WorksheetsCharts 6> _PID_GUIDAN{0567C81D-60DB-11D6-A632-B14EFB59B767}CompObjoqxfObjInforzEquation Native {_1082204295g}uF%Ƀ`=Ƀ FMicrosoft Equation 3.0 DS Equation Equation.39qHyII P(A|B)=P(AandB)P(B)P(B|A)=P(AandB)P(A)Ole CompObjtvfObjInfowEquation Native $ FMicrosoft Equation 3.0 DS Equation Equation.39qMmIyI  FMicrosoft Equation 3.0 DS Equation Equation.39q_1082204338zF FɃɃOle CompObjy{fObjInfo|MDIxI P(B 1 |A 1 )=P(A 1 andB 1 )P(A 1 )=.0264.5007=.0527& FMicrosoft Equation 3.0 DS EqEquation Native 0_1082204579xF@ɃݹɃOle CompObj~fuation Equation.39qMHyII P(B 1 |A 2 )=P(A 2 andB 1 )P(A 2 )=.0376.4993=.0753ObjInfoEquation Native 0_1082207232F dɃ`1ɃOle CompObjfObjInfoEquation Native _1150716214AF`JɃdɃ FMicrosoft Equation 3.0 DS Equation Equation.39qMmIyI P(B i |A)=P(AandB i )P(A)=P(A|B i )"P(B i )P(A|B 1 )"P(B 1 )+"+P(A|B k )"P(B k ) FMicrosoft Equation 3.0 DS Equation Equation.39qm E(X)==xP(x) allxOle CompObjfObjInfoEquation Native  " FMicrosoft Equation 3.0 DS Equation Equation.39qm4IhI V(X)=E(X") 2 = 2 =(x") 2 p(_1082272906FɃ ٺɃOle CompObjfObjInfo n    Equation Native P_1082267318FQɃ@OɃOle CompObjfx)=x 2 p(x)" 2allx " allx " FMicrosoft Equation 3.0 DS Equation Equation.39qm4nIqI = ObjInfoEquation Native P_1082268587F`VɃ ɃOle  2 FMicrosoft Equation 3.0 DS Equation Equation.39qmߌ|~ItI E(X)==xp(x) allx " =2.52CompObjfObjInfoEquation Native _1082268597FʙɃ`ܻɃOle CompObjfObjInfoEquation Native D FMicrosoft Equation 3.0 DS Equation Equation.39qm(I̫I V(X)= 2 =x 2 p(x)" 2allx " =6.86"(2.52) 2 =6.86"6.35=0.51 FMicrosoft Equation 3.0 DS Equation Equation.39qmx?BEFGHIJKLMNOPQRSVYZ[\]^adefghknopsx{|}~y)=xyp(x,y)" Xally " allx " ally " allx "  Y FMicrosoft Equation 3.0 DS Equation Equation.39qObjInfoEquation Native _1082276172FP˽ɃLɃOle  mhmIyI =COV(X,Y) X  Y FMicrosoft Equation 3.0 DS Equation Equation.39qm mIyI  X+Y2CompObj fObjInfoEquation Native <_1082276493F`tɃ@Ƀ =(x+y) 2 "  " p(x,y)" X+Y2 =185.00"(10.00) 2 =85.00 FMicrosoft Equation 3.0 DS Equation Equation.39qOle CompObjfObjInfoEquation Native hmLmIyI E(X+Y)= X+Y = X + Y =E(X)+E(Y)V(X+Y)= X+Y2 =V(X)+V(Y)+2COV(X,Y) FMicrosoft Equation 3.0 DS Eq_1082381034EF pHɃ`ɃOle CompObjfObjInfo!uation Equation.39qmIyI E(X)= X =V(X)= X2 = 2 n X = n Equation Native ",_1082383993F\ɃɃOle 'CompObj(f FMicrosoft Equation 3.0 DS Equation Equation.39qlmIyI E(X 1 "X 2 )= X 1 "X 2  = 1 " 2  2  X 1 "X 2ObjInfo*Equation Native +_1151236109FpɃ`l3ɃOle 6  = 12 n 1 + 22 n 2  X 1 "X 2  =  12 n 1 + 22 n 2 FMicrosoft Equation 3.0 DS EqCompObj7fObjInfo9Equation Native :p_1151236359cF <Ƀ@Ƀuation Equation.39qTRTFd Ƀ@Q]Ƀn 1 +1n 2 )  FMicrosoft Equation 3.0 DS Equation Equation.39q `hI8cI  FMicrosoft Equation 3.0 DS Eq&*k r,$r 3*$`|<ͅ\GT#ljXp5z{>!ho6{m)o r4s |"y"`|VΕ"'3%gq1AgX[`! b>1AgX @0# kxڥkA~o&D"X[l"($S"T[ X0]M^BJ*xUTD? 9 xu>v⤍Pu$潷#3,e/hdYXHXB8"&ñ.ѵs鄞#Qcp o%Y<@dƁ˳;S @Dsd ;}Turn2't< i3\7-{"{[{u~߼1Sʢ_ F#4N9"!屘;J;!]e >ϧo`ךP$b5|&/4k(cn)&[cP>UbThE^OL>n9w1A :+q%[;f-޴9x}`1.brL_=gM4H :}}6`k3ZR \F[Dd D lb F c $A:? ?3"`?299`^N=`!y99`^N@3PGxڕkQ6vm[i-鏰/BElФJ</RDAɈ (!PPiyy|f3ov} lk ΀ebv*LԺ$~anMt\ίW5xdXK+7@{PFy&s,m&}9K eFqQ^wV-jd/G٫k:iʶ-/ޛ@ɩbeDydE_M\nV4$٧99{Jڌ5-8_ݿO1PRfP: ^>_WMndD+>Wק 1qbkV۳TX+= SŝD_5uFDd & b G c $A;? ?3"`? 2Piď:[.IlC`!dPiď:[.I<`_^Y2xڝklUn..lwkۥtnwwv+FC_M5$8%?gq\*݆ϗxN"܃_ M҈.-ӈA#:,Q2Yr.OKb; e9YQx"4g4GiDSɈv$!"q?vN@+ԙ\O[ւF*6FT~N'LlyBk*Tli=cboFi9HތC:PR=2md ?lzL坽T,WWSN(!|Qؼ 6VR6ۡ: `2nfc"kU"jχlx2V+ ĽeJEbEAUD\ YTD%z#t\I<S/Ke뻻po;H~ӫ=H?dpYil} iީ#_[%+l?9 Vޱ|j$t]>0|rZ9j@NDgiM^LwL='l\|_AYPIV;QL{!'yo@?Ъ5w0 A1 wVuZwX vNN=wXטL5^! vtokw$ vG;49;)FB%ZZ3>6܄d=%y]#~2sع8>SD;ZTmgMO5vag \2av,UAxeu<8iʛZ) QaV^ZiZ)'(N/ޭT7pxkR6rf{w۠>{$iHK+T~1D t*ߧSX?>l=K)dCک|^wQT>//d?YԂ}yڟG Q܏Gp~,ep~ y֩֩ah؟֫7f&|Ǐ[\ ᘪfo8+2~Q.#@Dd0  # A2^TKEŃ:ϵz:q `!2TKEŃ:ϵzby;X6xY]hU~/_|Yc u4Ԫ04R(tTE].& &X*{!^BFī]zd t0ط$_%mraBrNysޜsBpO9 ${9.lֳ)'Z,1.:%X)?Kĥ2B=} uVWh4~mkbk{r1檎>hߧ-' Gq"b} B )a=7ORfώWLV/uˊs'.?(az'aFm$qnrܣY:"fyLՕjmRsb+l7nGbf *&_*_zwCwzi2%1*g܃E6wֻe8];m'nʋ+c؋/RyjMlVA٨8G!WoDda0  # A2=A|e4~xS `!A|e4~x3O90xݛklTEs-}`#cL!_$QSG @1%mˣBP 0_(!!1B X.o#4DMsνwv`9ߜ33;wV!y/PIb9~I9T *ETHG9ryImPET_T*Ve6|m2v+{\#H=O?HBʫS-'"+(8kxZG#˾H[~=ҾyQ-?Rn7Óg7srd$KA]+߯r47%k|rƩd2t=T /HmѴV33 nżE=l$,*L.)*έjՌUzxg-&T95,qد[Jf39 YQf^?]^.)̾T9״кZ׭uZסuZ7Wj]h]M?UNDQn֝ҺSZBN,$;aXSO1ߝRurtꑗ"fe!+?Q[V!~H/5K̑pHuh.-Ȅ%f7Rb\[,겑2GR"ۊUX [, 6KU24 )l28g:&\[4&KT e,:+2nj%Du5ORC'T'Bcvbj9x:h͖ZB[ ۢ:a:A}`8*28$r%%LR٢HUf\N-AB|o8 H[֫CFC9hGH[XRآbRv]awG5YPj9*UP ]h5`4VIѾHbRCfzA[tzP _[Mk:ۡHQ0QxEDAhߣ DPm آHA#A[t;}-~>#>"l{DD`ڏ x{EnD>vv [|Dk#2R"`-Qmv#v|/uPoBL`]&씺*&r^ۡ>qsQaznꄷEFU`>څ>o/j-]sP#ln[Cȓ{E?)[ܤRCub.8н<[ܠ{RCuT)> `2Xo7Q'Voaj-~~LTs ^%_1H53l;^ykĩFpN?R9z)T\Oc \&28;A 91NgtLp'Qd:˔_;<~:JZ;RMSavvOǛZyg?239zNӝ/=K<҂NeGԿ+:b]ّ̻eĭ@>RmB3g:skv~vP~Zw|ҭ*=_X;;Q+X3/Jf|w0靍p;q_:U3ܹg샳At+W"=N7;ҿ3鶃9ik3Vҡe[Dd$@<  C A? 2$"u]Z`!$"u]Z& ;pmxkAn4M6mDnd EL'+DT&BAԫ7/ =**?œAAPu2R0w{o PH gJZu-34Z xCTb/r ʅ0xvs3Îbbpey|FyGQ`m5̫ z 6f2l+,V9YbY.ifS\at"bwTY*xAM5xvFs&jE߲̬jIh&U9S)mV5,=HcnV,?M=2lcT,QSGUV+x :rvtNdv`G8w{׆4gnWwmƜϯ2oGT#G*} 5?`}·xWek)?e\J;v/cek>w2=?Om! ?x{q3wz0waQu sџ'qx'.=H'{Fďś H%fݵf V0/gĺt DdB 0  # A2ن,4D`!ن,4^$ LDJxoǫ^8!&ZfEzwHfÞVZ"YhF" .CnD{`?`|7aߐH%zU9(1ϫOQ4cKY>݂WX,ز|[UZδ֒|Ţ[1r}=FXߟ.ׂ-_=\9\WP2}:?9*h^2BZ+uyYYWRqK}UJ\*ِE' Ldb*Q/ѽqrWu5[rIJaD/~us{+|TW.Uu˄zkq"29aHT@2nOe\.Fƭ)jzDL)=Q?Qhh=+JPvF@G)N,V;y{g ;TeZEGF؛i5Vё> $Tv,79< d-vhbu%6QXægsȺ̺9( l]c](̱"N Q8*rfJ/L81ۧYxJ-,pxJmV,i*e,]6AY /Se=vS)Kf֔)0-p^iCyUKyGXkἓ{QJuVXgj 5(!FKCUHؔx̪*oO"!m繐>Gez{5MǙmZ:ZUvu&zwߵzZjzzbyyK_Ctb۩#hz9,O>VO6 ܥfjL7j!!bqJH$"zo:ĜK⦪R%4UIl tr1 K#xJˎ`5ӷ:]:R=pn|VӘYUԫ5Ѻq&2k{x@&,!sDfDfݸtMkR)Kn9MAu.rJꪲM$X.zo1UY3P%deZL s[GAKl9:˨ꚩꒇ}w.ZV*kuڣ}EFpG+2=BwJaM;QE&oC]K;C6k =M MYSϱDN='cyc< iœM90>Àg*ճ+~V_|?3Yٞ,3yU}UܩߞqhlϯV-ϯ6tRj#vRmBHN*$TTI 5Dj褆!5rR#B;1&HMԄPSNjJR3'5#>RNjPH:CB#u줎 @j@mpM[ H!tRP,-pg]g93w\>8 a k|߇C3 bX|,1fG>|?zĥ&Z3/ eWV:j^÷Ῡcg)e\|5Ձ٢8F9*VYPeDRu?)^j_g g~o}#MūƞJX.0:1;I,҆wShS C.][\WTܶ{&d%]_`btBLD(C;yUi^ژl. ƫ8Λ+<¨Y547]1bY6zsrƗxZU|%gl`6ؚ V0X`y 0Y/Cؼ6 i],N226ī.j?d~Y{hW #kQWYyEei1"p7%i Ϭ\' _,RV *ҭ;K7 |-=AG:Rq a"߸(e OEp,'yU|'yK U%zU梁ѯR2)=Nb?k9a1]ޚVWn[VJ]gH$Ey?$]섳lqIZ"O2cBf RT99 %s3ճ.UR3rai?,4{ 2Gxٻ L=j{A=Szډ3E+vV{ё^4#E:fě:7wND鱗ƃjM%}Y ٞo4wkwq55J2Ǚg:_͞|ma\v_U>b||Ddx<  C A? 2dkN7b>/`!dkN7b 2dxS;KA19BP؉v>06Z%b@B`J{?BK (*qw…o"X A&-RoBz.5c%5)BB_P0 I '9Uqlyc)eQTtRKNtUGᛪ?9BvB"#<\c}<^]ݞDžnPSg.:Z|l 7#${ueg}#_g3N lqg|$y\#dhCg(P=|5v5̊"fEoVcE!Y53+x@YP|9;])moySzJ)4)vEX9n% ӕR9Wh7&yxvbDdT<  C A? 28P N/71`!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PeDdT<  C A? 2+ן<΃Vm-EL#N3`!ן<΃Vm-EL# `H(PxJAgu_a!*(1hVQ/$`qEP+<  )"qF0d;DJ"b"{dXu.VWiRZcBZ-4#wM1iu*63SI?CZr{b^.;ʌ|f.Sd'rSqZQ32R1jE΄Ȋlnr<=VVCSJ*U.G.FW} 2)4Y[3am RlZ83T%3[|Ԣ֌Kl&Wg6\Ŏ!Bi#P$ gpi!wUFAV$t,1]C~?uߣ4|Ddh<  C A? 27Zs\7Bϐl:\K 6`! Zs\7Bϐl:\K@`(Pxcdd``~$D@9@, fbd02,(1dbfaJ`q41E L1P5< %! @naJ`u!0)0c,A(AO`A $37X/\!(?71b2#N#&9q1TdHTf! KK5GU1ʁUq@EAUtDp70V#˷0rq"70aEo`jgk|FzH8('6!b&&#( >Ο,I ^3FP 27)? y p~*$t p8f`s'h,0s3Yy)p8Mha@6ɝY#Dߍ +9i``#RpeqIj.z1zVDdl<  C A? 2}7ykvk8`!}7ykvk>+`0DPhxkA߼Md5i&۴4B=xRl +bObabSRs(I< c`8/- gwofgDſ`[E/2&GZ-92F:y 5sqޛ= hIr׼渒ڣX B5C,zm;csbo3Z)mxKOrf#+333#4ޘ/Qo xy4ۙf?~ٕqh~ߦ~>1>Nq}ED3?'bҜoCٱgQ;?|_ذ|竚&)9arߢ[xʺ\n͈9?|C)N ^ݣ zC4>?@>`iS=Ɵ0L"J;/Kia3WcޅC]UVˀGߥNև7NyaUd//i*>uZ*xWV;+<|\y{ ڲ>?d{.%5dEgFT޵%u~AÕDd \Db H c $A<? ?3"`? 2-D!=MҾ+`!-D!=MҾt` 0xcdd`` @c112BYL%bpuy\c.`2¶g`!(eZZǀ{3PQ _eCbNN*H G/6^1XB/`2Bn/]p 3#DKx0`pa 277 >i=] `pgdbR ,.I ?:t?3e쨉QDd\D<   C A ? 27Cc%%Kj>`! Cc%%K`% 9xM/CḀJ۪W}n% KMHzUT ;XXלvAi|Q\_gفE}2^7a6S}(H>⡺iP})b?4,[zGEy9y߫t'od$]qͤ{}#Dd<  C A? 2[f't0;o1A`!g[f't0;6L 5xmQ=KA}3Ds x*v R0A,b?B-fk|O[bG lpS~o*kNcQH'4ɢ.:93?b%sT|0O/'`3W곺ޅ{?S8Bg腼g%%?̳i=.E^Dd D<  C A? 2t ݓXTC`!t ݓX0`pxJAg]AP- N1 hi $`Z=`Z++>Hlb⒉3 ̛W!3T{E}tj0tSw ?uHiNA78NmkaMB# Lάړ.|dкRyϮ%YC.LUTӁЋ0V{k_rCrDd(D<  C A? 2K"GES0Jʤ2hc'E`!"GES0Jʤ2hc@(e>xKK1'Yk>Ԣ,ZQDOTPxPPPAOEA]AD{EDLҍN .OfI`8fZ|*qdm˞8k=Ե1khMF'0D)bɃ~ `S&Lf\ڶR\0g%wn0~aw›gQLbx(:ǵy@s.a"Tэ<8;BTE}6_VDNh^|DJ#|t<Ӡqa`rOJ!s&ES<_ج=6x{=(<FI-@~j_vQ{P-|os7J_*VƝ}Ktt_cgg:5ojw,cjn努Ve_/{+=K1LpI<,59R¤{Dd <  C A? 2vBPQ'7QLH`!vBPQ'7QLP`xR=KAMrKB?ȑBl & V6*D8%BF,$WlLmaeʟ"VZsg Xn7ogv! e3K$D (JsZmf>'-LFOr:E<8ZtqM8Zk\I1PFKqu6 ͅs/iMkhD2kc845F->oP8<ܾs^9̵3)5 W qɕh}gu~#ןZ+rV$yF5ӜTSg7Y(H狨V#j?#[kԋU{`(8=JbDdTD<  C A? 2(w-K-FK`!w-K-F: *MAx;KAgyj11EM PȩI'"  E ϝۍ&,ygonx^!kq3F-" K3'e^Wp&AH]X0DLa{E|5jNPhdn倣z37w{T~] b~?kMQ3fsZ?D;f6]D.<)s*s8-GxU[۵%_]_W'eN8ZғɤВJ->iJBэ< y\C'%!R;u+l a] Mi]܌ _]aogHnφ~3&{磻I#sjMtKq[) YӞ b̐aDdLD<  C A? 2cV̫LfM`!cV̫Lf/xK@]RHS*t( *V7W;!b k@p..$R㽻UPl9r. (H([Q;-Jy̓T(q%=lWKA]UYӶ*?ic,@?]sP?Ǣv]} J%e >*\2B\\r6 13N~qմvE%;ws9gءhH*8 cdscRx˹󢛀Orm)U%D[ybd'sz!՚Ty.TOuFeyVŧT#CM[||0MMC M1ԸeODdD <  C A? 2pw kVF-VLQP`!Dw kVF-V 3 PFxW_hSW>&&in&M1ikDm0Y:(2,\Lv%cfTAADDd(0އ'I|C캇19Inwu)!{n)M0'|5Ư(w(N&ŝ1FA 3.jPMΓղ=2AȀ4cBdgk2!U3#K*\0b3ϳ+r;v;_EnbtO.vK.~㢋ur㼋 ykOP?C[QT/dPA[P-fRh &1v} _ggR1ԛB)D05!֐_y[,)j4)UGcqYVxF&.w8o+9\DTou\Qq!jo(ؘO8j)ʻ#r+Om*<*Rʯ;m OX! wB}+ ;z+ _a> /3({Ff1a?Lb~$W s,z8F]?"W9 cW!GG*Nd};ǢC큓âWunѝ1\GآQtp"QZȫK=Q@Ŵ7Qaq?C]bućW|eߏ#~#nNʜ?c'g/mibno>?S=~K#y[%#Nk|SERMex'Sb :Qoď7Ϣ ē\Ϣ᠜oQSM|}]i}!*G|Z՝Iqlʄ*ɏxU蝬zFO?C..QG,.2&i?tpn"r -ڊw^;.h@? (ckK˕7y|"_Fn9Dd#D<   C A ?  2ny3Etk#;tQU`!}ny3Etk#;t9\XKxkAMHflD_QTVh)*ٶЊ݋Ixaޔz x|{PЖo&;oA|0h0~r݀(k1Q8cqeKedgM3dZ-)ȂEQ!Ci:I8W6p`|;^0ywFE^x򦿐ԟ2kJ"v-!*NtLA%_7,;\׹2]'Lxh;Fc/廻)MS~ЩXX-ofMZr?7ZrV!Y?!'Nh^jW4?LȷTnTMhVwvS! wDDd|<   C A ?  2n)D|ayzŐ)X`!n)D|ayzŐ)` @VxRJActDd\<   C A ?  2Se8D} n|{/Z`!'e8D} n|{ `-WFhxK[Ag&3ږJ@l.ӧsEx$jǧc&j.{ Q0HT5WpYW};!_'v_>ĦPyw/l:3v2x}RTw8=}PM-tڛN(?[xDd <  C A? 2+Op'|2c`!Op'|22N8xKSQzJsMͶ{4% !GXO 4j/Q"!!,AcOc/+Zs;!۸|=%^/ vQBlfi+yqJ B| a6wA|X? ,&ԄƒbQ/?_){==JLZ'_&< Iآ;ޢ[AI!]']ZJ/&|u:^%tP6}6,m8-ތFFZsn?vYHxAkRV_qxx%hd&G\{WM4E 8[.'n$nΕg*E2B;,Fm"5G?hlky>,?OySO'_GD|0~(|wа_>~ɨL+^LlT١u,՝=s{+vf8I6>)U9ݒL;gSqy6s=*N8O :_Η{Soj8Qk<&t^Syt>0[=mMVw=s];7ﲙS8IO}\xZݜ.߲t 뙺3KϹ\|OAGœsGܥ/uނKiuM|UyMOC~b?6gM lڶdu~TCDd< / C A ? 2G" } 6Ȏʗ;O?`!G" } 6Ȏʗ;.*`wBPUxkA߼YMlSSnkR@DzSDAHbIzH=/=y) "9?@bOzPIsu~zuÒy;fL&2@,(7q`E":IT.u9UE@".qȢ]-6Firpu~lT؄4B9?L5"Σ(XmŹb9U{2 qCTSH-Z[es hm*xyș_,gvQoyꏽ닍&n̵N5`|d3%%͝J{J\G&_ u=_-q.&8H#~mԟNCCn!83A=h_dT^٫Gs>X)/CzAfM>w[mޛ!9*y@J;q_񙠵\opa V .ɴ=b]p2Dd(< ? C A? 2D3U^ɑrR|B`!D3U^ɑrR|2 @@ dPVx_HAgfםus=μJM/:"?uZ(ԑADP/]dCCQDP "eEEEQ`еsO}:Yw !BB2#1E85$LZ ;>D\9*V\df19f^W"U9)6t?E(@LM>}d*q z"ry]lYB'O7+5B$5 [hsDd<   C A ? 2p)D0."V\`!Tp)D0."V\,`_EP"xeSKkQ>̴M&HI MltBD]Yp &- H+K)]IVҕ?@Js\HqOB( {g`.|>\J#EZ(FF*(,;ܗņۛW\5NڛH)dfLo40 M{e{3is|;NZH z8n/'[k6":nⳜ\\S/%~57G}-AAR"`T1Y]dvuЩfo>y) L ***b)H 0 : H = L H A : H <Equation Native z_1082406068F aɃ`ɃOle CompObjf L c)H 0 : H > L H A : H = L d)H 0 :X H =X L H A :X H >X L FMicrosoft Equation 3.0 DS Equation Equation.39q4HyII X1244s2856n5758ObjInfoEquation Native P_1082406409F`ɃEɃOle  FMicrosoft Equation 3.0 DS Equation Equation.39qߔHyII a)TS:t obs =44"12 2857+5658  =226.5b)TS:CompObjfObjInfoEquation Native _1082450049FlɃ@ Ƀt obs =44"12 (28) 2 57+(56) 2 58  =3.86***c)TS:t obs =44"12 (2857) 2 +(5658) 2 =29.5d)TS:t obs =44"12(28) 2 57+(56) 2 58=0.47 FMicrosoft Equation 3.0 DS Equation Equation.39qtmIyI s p2 Ole CompObjfObjInfoEquation Native =(13"1)(5.1) 2 +(9"1)(8.7) 2 13+9"2=312.1+605.520=917.620=45,9=13+9"2=20t .025,20 =2.086             3           " ! $ # % & ( ' + ) * - , / . 0 1 4 2 6 5 7 9 8 d c ; < = > ? @ A B C D E F G H I J L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b f e h g j i l k  o p q r s t u v w x y z { | } ~  _1082451311F:ɃEmɃOle CompObjfObjInfo FMicrosoft Equation 3.0 DS Equation Equation.39qtX\IܮI (40.3"44.9)2.086 45.9113+19()  !"4.62.086 8.6  !"4.66.1!(Equation Native t_1082451328 F`~ɃɃOle CompObj  f"10.7,1.5): FMicrosoft Equation 3.0 DS Equation Equation.39qttmIyI H 0 : 1 " 2 =0H A : 1 " 2 `"0TObjInfo Equation Native _1082453467FɃ@-ɃOle S:t obs =40.3"44.9 45.9113+19()  ="4.6 8.6  ="4.62.9="1.58RR:t obs e"t .025,20 =2.086 FMicrosoft Equation 3.0 DS Equation Equation.39qtdHyII ('P 1^ "'P 2^ )1.96 'P 1^ (1"'P 1^ )n 1 CompObjfObjInfoEquation Native _1082453999F6ɃpɃ+'P 2^ (1"'P 2^ )n 2 ()   FMicrosoft Equation 3.0 DS Equation Equation.39qt`HyII a)'P S^ =0.40Ole CompObjfObjInfoEquation Native |'P L^ =0.06b)'P S^ =0.29'P L^ =0.51c)'P S^ =0.06'P L^ =0.40d)'P S^ =0.51'P L^ =0.29_1082454040%FtɃ@9ɃOle CompObjfObjInfo FMicrosoft Equation 3.0 DS Equation Equation.39qtLܢII a)H 0 :p S "p L `"0H A :p S "p L =0b)H 0 :Equation Native h_955995627 (MDEST)ɃɃObjInfoContentsp S "p L =0H A :p S "p L `"0c)H 0 :'P S^ "'P L^ =0H 0 :'P S^ "'P L^ `"0d)H 0 :'P S^ "'P L^ `"0H 0 :'P S^ "'P L^ =0V + FMicrosoft Equation 3.0 DS Equation Equation.39q_955996988"FɃ[ɃOle CompObj!#fObjInfo$Equation Native X_1082455247'FtɃYɃOle CompObj&(f     !$%&'(+03456789:;<=@EHIJKLMNOPQRSTUVWX[`cdefghknopsvwxyz{|}~<dhICompObjDF?fObjInfoGAEquation Native B$uation Equation.39qdhIRetail   "2 X Financial vs Hotel      2 X365.36 2 X338.72 2 X Fin>Hotel   !2  Financial vs Chem    $2 371.12 2 331.81 2 Fin>Chem $"2  Financial vs Manuf    # 2 370.73 2 330.85 2  Fin>Manuf # 2  Retail vs Hotel     2 %-22.26 2 348.21  2 NSD2 ( Retail vs Chem   $2 ( %-16.50 2 ( 342.86  2 ( NSD2 \ Retail vs Manuf   # 2 \ %-16.89 2 \ 342.15  2 \ NSD2 Hotel vs Chem   $ 2 J5.76 2 331.81  2 NSD2 Hotel vs Manuf   # 2 J5.37 2 330.85  2 NSD2 Chem vs Manuf$  # 2 <-0.39 2 321.55  2 NSD'  I\p Larry Winner Ba=f1=hx,^9X@"1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_) 0.0000 0.000 0.00000                + ) , *      ` Sheet1-Sheet2.Sheet3VBPharmaceuticalsCommunicationsFoodFinancial ServicesRetailHotel and Travel Chemicals ManufacturingIndustrynmeansumOverall mean-65.11ANOVASourceDFSSMSFTreatments (Industry)ErrorTotalIndustry Pairs Pharm vs Comm mean diff critical diffconcludeNSD Pharm vs FoodPharm vs Financial PharmRetailFinancial vs Hotel Fin>HotelFinancial vs ChemFin>ChemFinancial vs Manuf Fin>ManufRetail vs HotelRetail vs ChemRetail vs Manuf Hotel vs ChemHotel vs Manuf Chem vs Manufn(mean-65.11)**2 std dev=s (n-1)s**2B {@ yAx/  =!0xGz@/ Nb0+)\(@DDDA 00 1>!1)\ؿ0 DDA1L5@1+)\(@DIr0FbFb( T>S0bt00 ^n006b 00Ԣ06b6b8b06b8btb04btbPercent0]"??(U\e0\ZT00ZT0_T0=l0Te0%L00%0Hb]0 vbtb NJT0e0YT0L0YT0LNJT0RbXbk0 'bȆ0bEXbEbC0tb@bbt\bc0tG0E\bT bbB b B  I  dMbP?_*+%MHP DeskJet 870C Series7d,,HP DeskJet 870C SeriesLPT1 ,,"d,,??U} $} $ }  }  } 2T0bbb b@@@@ @b @b bb    @   ? A A@@@!LT@ DD!SȦ R@  DD 'YF@DDD,bٽ]@DDD R@O@С@!Q@ DD!0P-  DD 'TN-@DDD,ףp=@DDD H@u@\@!q= #@ DD!\$ DD 'Lxճ@DDD,r@DDD 5@@g@!Q@ DD!M5P@ DD 'd>]@DDD,0nADDD $@@@!fffff6{@ DD!vQ5 DD '0s,@DDD,?&@DDD 5@@@!33333@ DD!k? DD 'ۚ$@DDD,F$@DDD N@t@ܟ@!$@ DD!EG DD 'r@DDD,CW@DDD S@@h@!ףp=i@ DD!H3 DD '.6@ DDD,>yXթ@DDD  # u@ %)  V[GP@ %D  # NrX@ %#  O!A %        ~ @NrX@ D !=5@ DD!E6{!@ DD ~ @u@ O!AD !&@ DD !u@ DD!|$A DD      !Q @ DDA։ٽ9@+)\(@DDDA  !Q/@ DDA.;@+)\(@DDDA  !\(Z#E@+)\(@DDDA 8cp&VjfB T0!b"#$b%b&'()*b+@,-@@.@ /@b0 1@ (! (\  DDA ,aV?@ +)\(@DDDA  !)!!Q@  DDA!zy5@!+)\(@DDDA ! "*!"?! DDA"\4@"+)\(@DDDA " #+!#QS" DDA# w# ]@@#+)\(@DDDA #, $-!$Hz&@# DDA$,DE@$+)\(@DDDA $ %.!%< ףp%$ DDA% w# ]@@%+)\(@DDDA % &/!&p= ףp% DDA&t+)8@&+)\(@DDDA & '0!'& DDA'3vNȏ6@'+)\(@DDDA ' (1!(HzU@' DDA(MH@)+)\(@DDDA (2 )3!)ףp= WP@( DDA)"\C@)+)\(@DDDA )4 *5!*HzQ@( DDA*u>F?@*+)\(@DDDA *6 +7!+QQ@* DDA+n1>@++)\(@DDDA +8 ,9!,(\B6+ DDA,MH@,+)\(@DDDA , -:!-0, DDA-F?@/+)\(@DDDA / 0=!0xGz@/ DDA0n1>@0+)\(@DDDA 0 1>!1)\ؿ0 DDA1L5@1+)\(@DDDA 1( T>@  I  dMbP?_*+%"??(U>@  I  dMbP?_*+%"??(U>@ SummaryInformation(kmDocumentSummaryInformation8_1082473966pF`QɃtɃOle Oh+'08@Xp  Larry Winnerx Larry WinnerxMicrosoft Excel@*jFY՜.+,D՜.+, PXd lt| UF  Sheet1Sheet2Sheet3  Worksheets 6> _PID_GUIDAN{C9EDD060-C501-11D1-9505-444553540000} FMicrosoft Equation 3.0 DS Equation Equation.39q}IToI CompObjoqfObjInforEquation Native $_1151318532uF Ƀ@`ɃOle CompObjtvfObjInfowEquation Native . FMicrosoft Equation 3.0 DS Equation Equation.39qxbtc (f o "f e ) 2 f e =(observed i "expected i ) 2 expected i FMicrosoft Equation 3.0 DS Equation Equation.39q^,c  2 =(f o "f e ) 2 f e " =29.95+1_1151318462zFzɃ@ɃOle CompObjy{fObjInfo|Equation Native z1Table SummaryInformation(DocumentSummaryInformation84.78+16.74+8.25+33.99+16.77+70.10+34.52=225.10 Oh+'0l   ( 4 @LT\d QMB 3250 MB  Larry Winneroarr Normal.dotr Larry Winnero70r 8]_dPW:1Ug_:D.Kqe,dWqB,Gf+찻tkg#Y٬uКu$t7_T=F#8KNzkϕ2*%NjL%q$.b18] y >FٸHY&%xλW9W .Ґ8HȌ~Y bCMJ;ו0lh&},yRV{uKn(w=eqW]~KWv ۙWW~f>޷ﯘ trt&o_XR9e:6s> Bg`zU9_bDdT<  C A? 28P N/7?`!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PeDd<  C A ? 21#&h{"O`!1#&h{"O2`@ (V'PVxMhAߛlvIvIҚPjXћ`I ($vAă҃ػ^X= +~\13Co/DkDk7#{zmP;_:6xby\zD4YHZ;?,J=rU?+AӒgt5ѩQYzV3u_@+N{\_.k#?)Qɯ:\m 5(.5}8{*2ȋ=ՑQ*Z)OFIp"O^%foCxLzBZК8Őlo${ ogѓDdL<  C A ? 2wwYs`$]_h`!WwwYs`$] _ %xQJP=3ƀ҅;U~. ]VR @Opm837č3;%4%"` jMV4B\UEi"z}_,^a6!>"c(=uMprݟs`L_:6q|І;R }'2͌Se4oN!?=RǞ#<痖O9Y"G>);'gKID"۱]ݶ c*>LW#D_Dd<  C A ? 2\e۪~Ţg{`!_\e۪~Ţ `Ho*`'-xV[hU>sf6{s6ٴuM6OEi L f R>( R RA1A\6P#J$B9g?},9 ! BTD-J>XQn˘Ϙ!pEOݙVw8huir#|9ZDNFJQX豩fXGyAD_{zkz՚!'-TWPX.Ԙߊ4fc+,8vu(1bTtUszԒ;\<vxE4USvPztqOp|۽e- ۛZ  O?/n۝lLϾΉhݜ!E]07#Cqۻ[vΧ C@F!_/2>1rŐ|wR흌O; z*g8|AfHb%Lܺw͙O =cMfЇNCgdu@!Nf{|BD(З쿡dsn>PzT!5'Dd&<  C A? 2wq_plf p?`!wq_plf p`> 8`dxMLA?`iA+,UI+PEJM#$M4@F$1Jxedc!Ļ:ofw&6yol> S"$6 !ɒ)4Qj)GCm] 4+>z_G:18XZ: |8J:M|>6zYH 0ȼؓ>6 Jjϕ+e2y8:4S\!_r ͳߜAvώf8ws漗ETmc _N (yOK؂s dsPh2?c?Z+M>P&Q#Aa/ZI+[Ӑ+.Ս-hRWJSZ?'&~dzDd"<  C A? 2 ~e4K޸`! ~e4K޸6}T`\xeMLSAg= J-@ -A5NI  MĘWO$\Lzx i4)F{fg[^KX6ā9H- <ͦnɢp~tg׊Qi zBMJ=iF,Mn.nJuZ AAOR8*t0!무F]_ cwjTUT=.A>cٞx>"n\%[$)=:$ '.>&ϵ~ ? VJ[ٵ*%s}P 3 wcƉ ?Oo3ٌKW)>dӼ~.SE3Üq}Jqq??OIIe/dCf13\$=g5欉wDԉ?'},|/;Zޑ3f|G G=|)c'-0 <'{"W窕@˖Rm=ChGSφF_S~G{~\8SPso^kf%N۶|Ocեi:[Y & {?3R!qDd8D<  C A? 2~ԤЊsJL}XDIQ`!~ԤЊsJL}XDI p/xVKHTQ>ΨwJi 󅎏,\ 3*AN#(aԦE`P p%- Ej-&*Ms1F8~:xTP Gu HbJ|33t^lj% QuVt5^'ɡɱ$c3 t|¸c$ՃҊDQU VAV8hD_Qu@-:GC ƶ&VZ0(: qWJ7qk *]zO;_90$V=k4h69Di)ՖQ.^IZp=^B\L\LEؒ/$`K%C} x:5tkdR Des';v4FgG:bCjxq/UA9u# ,9uoU@H_VZ&m@ZF/u3G5YΣ[|<}E)巶BUHo _Vo-,~#M癁+'ޥ-F/pqF7y?2=̛P5yU~ ?a0B3^Z6ok(gnxNBY% :y\M2ruzxeBjc/cu%/ O#;,ŰaدL FWڝ:F (ID2@>e \ 3Dd$<  C A? 2j`B=ɬҵ4`!wj`B=ɬҵ4b@$'HD ExMkAǟyf_&1iSOKR[RhSœH H@9x z\0ū@Cjya&%{gv@ en$DDi*"Ӥ""3H;Y^{XF^ Cʓ dSNc=<8]o8@FdaB|4F,6eFNƾ;jY^+;DŮ|ѝj b%uHo%'[z8d X*%[뼩 5٧|- Ad~7柝A(O:yt\Y'e_.>'=?EZ 9fYoIMdz5狚#wz;jy:Om~S 0E |A/ĩGV0[TT,Dd <  C A? 26( :.!ɋx`!p6( :.!ɋ@ _>xkA߼lW[(ؘVRAkzR (tSa+X͓JO"x Mpݙ5a|}7o ` (Ū=ȘaicrpXy]=N>A* mz#hࢗ;k{(*d!*GQo[Z:}MQd[=".&JV7_3U7N8dX}oYrk'֦׼PINA'^oH&bI#NC/g^oHI_z?BbRSNf鿞83N~"◨mW޻}>zppV1A]Y,'4Iz =C:^|sT)_&zU󅾿?;=%7hlۆ}}} 'B+=ꈘ?R@|ZD_u]ܐ:;b)ttM*_b-^x=X]EOϐDDd<<  C A? 2T*_ba!`!T*_ba` %(xMhA~fk(Ei"E!xPQS,^VR,~@/B B=t"E=I( djE4Lۃξ!b0㬍).DZ&RV9)q7F]fv ˱b~ճg1,16~_r l(8󇯗KSMĺ{l>w[(^P4Q&Wm? _dP'L;tht?45d| @ 3Qx3N/]"_YɢaC[}޺ H3YB \_-M`|A޶ i=/T[#{U#A)=d >4:A=o63}Lwg'U%O+O/[^pf Q?~sզ9CnPUi/ҷ4!_x |WſΞ.9'}KMO)~Dn5ՓO^d3EvA}D~O?"]U=+Iv .&gB7zL#ztQ7,MB}>GYQ]ғ;\ugr%λSӥr_!}d4Dd<  C A? 2 ;2mn`!x ;2mbd2xDFx @C x}L J)/m =Qvb4&OlDW9I%R 'հ GtDdx< ) C A? 2/ұTv8<`W%' $7P`!/ұTv8<`W%' $7t" x}UMkSAw/Iӗ4M? &|U.lWT*MhA".r!AY+) .JAdEDDDR0,L2g3wwn&x0Ԙdz6C4+FY)9RbiB^.&aF:r lƵ~JD7lv;M}5>,6ﶚKK7vZ6t&^LK6rj1Y(:k8vFNae(k!KRYpk%BTc*F,s3/šw4!bqл9W gڜ^f*#>(!j OQBH q$$c&>H^' yԃ߭cTa և+I AF|Bz A Q=TǠ փ@ZUx '>U>ZJ%NRw!9ޅ=conٶ=g;ݔƇ4~o^jz_ ؏;^7CyTmi[.[do@lNnZsndlylA{]nxDd<  C A? 2e}Rv> ?YCs`!e}Rv> ?YC,@_E8 xT=hSQ>KRGӊIIMCIšJl%b1I$:CtC 'qp,A}:s|&L1 c8p8HH#b5n_ 4!C6lhINR3V,T̋KsH#g(B!U GIWF'mC6u=+}Y[)Ȉa_<Iv'v2EDm\=b\g'US L;ļ7o Z^}ѨI1p [lwϲ9cyvh %|>_*>}ҋ*W%p3vOBR1eIQ(*3]|6ϧUHںw1t (U !b.Z ӫ7}ыΨz\ԑ ١g Vmq81KyQ/>b:]1wSu,`'Fr懎b\lM޳㴎U`c.3_U~aNpg[FZ͵j p!ִDd < 7 C A2? 24C:N@Kk`!C:N@Kk(e xS=KAGTbS3hNL* &$ VVb!"66)-ڈ?BS Q 7YxcQO~y"Jʅ< A%&ܡ)(FYuN1ΤFp EŴiaWGLh Ͳ[/;rB4[4wV `Bx cj̜Iq&5'nf+#}ƽD"R^ DFsoڅfym{ Z2O =ČW0i`5oM ߶½~W'APM?AχN] XrZꎘCC[cBp0'y7ZDdt < 8 C A3? 22 I bj`! I bΤ q(+xO@]u?2(!`E!@ l c @ DLL`H( rﮭ t}^{ww o0xШoQBR*5!t}O9Zs/Sk'`vIb/(q~;XD8?Xܩee>kc{p4፱VsS!Zg KٻwbDdT<  C A? 28P N/7`!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PeDd<<  C A? 2b0V*eh@S> "`!60V*eh@SR` %(xMh@dg.-E补BD/z{X/bkɶbjQPAуDVуxCQH? ]I2ηuqvyoIL :!J#&>So䊄phZJdE!p^eSh K M"Oќe ɻKƃsp@aHNgxb;N}z; jzP; +P[Xt5PBm\sF'Cu:Ԙ'4QbK]cݣ-jG{ŝ/űߧb&zA~e"AyE6cP Eyz2-כ!כ #oEy*pUyڽN?c`<4j~SSM^CwS ɯ|Qٷ$;ᘜoLLF({ؿ,oåp(纜o`~:;uQ̯\Q5Fwޣa?>aѹh=h=mҰJp6~ (ˀ,;`gp3^^%:{t.7\k=R{e _20|+YR0?Fq48D9Q*ڞ)bDdT<  C A? 28P N/7 `!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PeeDdh < - C A? 2i_{kZ,l`!i_{kZ,| @00JwxV]hUܙٟɦ݆Z$ ¢>ڇ6SPc &J," RI*0C|EKեUs> Kwι;{9F4B)ԃ'X aT%s &Rǽ 8$gVVV9TAl6~{=4>?=ph T NwbqzVN٪H^Q.D4_ bDt3UPITE*lъZea+e2ڦu* ,G3G4>ъv ۲]ȹ m'hh;I=v-ڮL d>՞7 B/҃M1-L#|_gx7{R_*)Lf7K,bW c#~ϖ;r?Ԉ,bU3 ;qUY*ϨwӂQ܏6Fx2o "yjz+ң1>k.*X_ WıNe2mY:άcd  `$u&kX5ȸ5pi?d 0}di:+k05_oUG>U_.h{h\ (3xYO3/3xzǖ5􍜬WLE!cŀ3~]i~E5e j 胚zɔ5O0WU*РCVxGb<fQWx4EE|e5𕙢\_"_Y+}eEr̢11F#W,7tXqw]!PQY$nf o7ɺ(u ?K_0 w  gn,7tq)?g҂Sr|]ec20ڄep^U_Ws}?08C.+'u1 %[iىJXj',ߒ'bKmfܨ||XAk Uq#7W& _i}߇1!,OJVQqGbDdT<  C A? 28P N/7(`!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PeDdN< ) C A? 2RG=XAm|f.3`!&G=XAm|f돊 x xTAK[A}lbP( (P=BԈ1B$ b/=BR(=>("w{@Y9_Ky3߸\̃\y-6!)MS{(H컳ZM"2|5;p"9Eni(x(jORrY* EiuK#ߍ=/ Ry`'< ꝨÌΌ0%Zu$fK\5׹,({D{RE0Kbe+jt?=?F軬xIi5[_0=??mϟuD<ތy\Ь̈ /mLd*ˣְ6T4u#o&p&}l0̓^ qL NpLD&)fCޙJ h75RL&u}#DdHx< . C A? 2h}#FIZh`!h}#FIZ@X- x1hSA{.%*ZD`!,N"AL4 8HҡSdpTnXABq}`Bw{ IBh+D$=r0WFJmȗvKjt=1MrdM$PF_%dWpNy(d%W Γ,J9IJ6=$OP: qQ^AK~|x؟_(/fEcPDy9|Ay9e񺠧Sqg7sp'{ʜϨ#Pmv->L?H Dd)00  # A2 sFOBJ pVn  `! sFOBJ pVnDz-S` {f xoǻ{` v6&~wQr؇aH1kG%(+-qY), !R"U1F)~Xwէ[Uկf0èa G }XʴQem(Y-^ϏA/^khI>OKȓe5* /{W_}Dl*jf4-ڐ4Ц`F<^ݲ֖pw4n1Ӯ%TbّbJ8>SO4Ûl$ h#o/wVT7Ә`Q0{F*kV#֧J7nWfJZXl.v~.6dC\0뛫0{zODCC0􎥵Q|T|\"NJyP3oL5a[g~٫?W; ͍:QO+79 >i~ȟٿƎ2wM^vdJǡ&/ 3=cP&Ah %}$-;qZP-]+*uw?{5cl,ֹ;Jx菀7g{>cNơDDNh;^A'X8{ DD&r};ȉ[Π7G?n{Eo;鵵}J0DRaq^P/^n]CoA tNb =׋NӢk< k#ܫC|́[fGFtsF,Fxq(b̜PxE|}VM@}Wy-eCR96vLw2eoʥ<æ! (a6|/M2J@x9yYx\,ll{y\Q Ν$ԕ;ɔC)=l-3ѳFJ찣k=$E$RbԆVYYac{I Q 8N;UU<u0qгIMoe25ߓp$; FjSƟaʝ_莈Lif0?3{H7Y-[{}uli-(9z|X6֛0>ƋyS5sUȻN3 OvE y;D&yZ;RҜ|Nݹ~De5(XpIx. ƭ}P+I㔯4̯m\K1}!iAC$ }DFJLL5?,׋w!>+^i=KK;ǰp ,BYdm~UYY 9\X-l.vUUؓ}\4'ͭ&O37t2 ʻє 8נ>%ZTց+rY_$(J\)sT &ejeEN>ԡ\* lJ]X(1>0)w̽k fp_{qC( X"!V#שNFS`'jPZMHLTF :PM,ѢjR*1ϒٳj@%Qi-x!"]A5d첮r;g5 S_IsV٧7K>?Z5 +R>N)ꔖ:E:jIQKZjPuQK]$UE]RW uCQ7 BQ-uP@K= #E=R]-Kz^ja"0u5)Rf5f 5y-5OZK-Emi-Bm+j[Km꺢k넺Z6+꾖OEhB=US-P\K='+ERU[:SME5TP\-jFQ3ZjPmETPZRTԦ$E]RW>R E} =ERPQCB=Q-PLK=#KER/ _)RuXK&(GK9:Z$(ꌖ:C(CQec "uYQeB=Vc-PoHm(,I:sE}>'3E=RG%%),IfYRXw)wr?9bDdT< T C A? 28P N/78`!8P N/7  ȽXJtxcdd``a!0 ĜL  312Ec21BUs30)0)0Qcgb  P#7T obIFHeA*CT f0 PeDd b ^ c $AO? ?3"`?2.:ÝT`q B `!:ÝT`q P%xڝS/A~v[۵M8 (#$6t/qp㈸9898H? Jy3D{ya0(>Ċ``!cbXqnQhFXWb$FMc^(<xKT@gv 0%fan iǤjMfy!9÷sºib%2dab)6^g>s ̫#_J%9O>e˺wx|Isl75j"%Lol%KgW/g,嘵)\8UɾOA Kytlƃn}ĔE=TFӠ'9aU RB 0;e35(qag$R~+anBD62ĮyPYB6t¾ &k >"=2a6;Dd K"b ] c $AN? ?3"`?2WS,qB"DE `!WS,qB"D(60UoxڥOAT(F H#QOp$H\k iO&DO&77D4F yov'EͶ3;yy&a/H>Aܷ8cx^'kŽfBZ (wP9&ӥ +*4\G!d> Z1a=h:Tf:sg!y.jx#UQ{XXj$cz|=k;(g} 7ύ2g82$3t_ O0@z)^\/m2Iz'%y$HD7qG;Nzww~k1+h#W޶EzYf% "ڲ#N3&e[~a'm:OXqO/AuJ |Xy2Wxl CT#PyOg{·tש3k9ٚ#,~Ɵj;!>6ÁMgr3#Eyϩ}M!9 mywE.?\Q=qW;.p-f54'kݻ [4@4 NormalCJ_HmH sH tH B@B Heading 1$$@&a$5CJ,OJQJB@B Heading 2$$@&a$5CJ$OJQJ0@0 Heading 3$@&5B@B Heading 4$$@&a$5CJOJQJD@D Heading 5$$@&a$5>*CJOJQJB@B Heading 6$$@&a$5CJ OJQJ:@: Heading 7$@&5>*OJQJ>@> Heading 8$$@&a$ 5OJQJP @P Heading 9 $$d@&^a$5CJ$OJQJ<A@< Default Paragraph Font8B@8 Body Text$a$ CJOJQJ<P@< Body Text 2$a$ 5OJQJ8Q@8 Body Text 3$a$OJQJ2J@"2 Subtitle$a$5CJ (U@1( Hyperlink>*B*LC@BL Body Text Indent h^h5CJOJQJ,>R, Title$a$5CJ(6Or6 Style1$a$5CJOJQJ8Tp2Kd},Hd} 9Rk3Le~-F_x'@^w &?Xq #     !"#$&%'k,-./0123456789:;<=?@CFGIKLMNOQRSTUWVX[Z\^]`_cbadefghij8Tp2Kd},Hd} 9Rk3Le~-F_x'@^w &?Xq #&      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPZ #3A,%ҸAZ'jR_$%&I)+**+,0--.S/00V139BIOUk   #&(,/13579>?BEGHLPTVXZ^cfhijmps{I ..7s5cr(-@/5:(@A B/CC6EEECFFFUGG?ACEFHKLNQRTVXZ[]^`bceghjmnpqsuwxz|}  !$%)+-048;=@CFIKNORUY[]`bdglnqtvxz}~; i=,%.5n?A*CEE$FF/GGJJYLM@OOSPPQSSW[gmknspv|y&ʕޙGMgҵڽVg_>*fY FW (3=*BGO,TY^b(hmrx }7: 1|F V157;=@DGJMPSWY\_adfilortvy{~ "'*.26:<ADJMQSW\_aekoruwy|TTTTTT$&auw:NP.BD';= !!!!5!7!g!{!}!!!!,,,/-C-E-_-s-u----...//////00041H1J1w11123313E3G344454I4K4;5O5Q5555666677777?$?&?@AAB$B&BXlnSgiѕӕ|Ö !=QSg{}ϴѴey{%'Զ޷FZ\yθ$&;OQ?SUVjl1EGH\^3GISgiX:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::358OQTkmp-/2FHK_adxz}'),CEH_adxz} 469MORfhk.03GIL`bey{~(*-ACFZ\_sux  "$';=@Y[^rtw  !#&:<?SUXlnq  &:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::,{| 2$i NA꧖9JgZ2$ M]JFc &`2$(w/9e2$dJx󫎁Q?,Xji2$k,Lr8Mk2$% G6!L~0n2$=')|$Qv2$ulD),Qz2$c_ 4"$>ldzd~6Awwl2$iDBM_4o 2$K76S;k> 2$Cc!J圄Y!wO2$ƀT$Lr2$Re_r@I)s2$c|dO#Rz2$OE ~q~H;,3T~2$ b>1AgXg2$19ڦ K`r 2$TKEŃ:ϵz:2$-D!=MҾ2$99`^N2$Piď:[.IlS2$A|e4~x2$ن,42$$"u]Z2$|A5dN`[K"$F}!0)"$.9Aiy)v2$)f=)hF$C2$ן<΃Vm-EL#H2$dkN7bJ2$8P N/7Lb$X3$_ -M2$Zs\7Bϐl:\Km2$}7ykvko2$?_um~i._Ar2$[f't0;ot2$t ݓXv2$"GES0Jʤ2hc'w2$vBPQ'7QLy2$ny3Etk#;t{2$w-K-F,~2$cV̫Lf02$w kVF-VL 2$Cc%%KY2$n)D|ayzŐ)l2$e8D} n|{/2$Op'|2+2$Fk㯒X[S22$D3U^ɑrR|2$G" } 6Ȏʗ;{2$ I b 2$ ~e4K޸2$Yt͒ k4ޜ2$p)D0."V\2$wwYs`$]_n2$wq_plf p͢2$\e۪~Ţg2$1#&h{"O2$~ԤЊsJL}XDI2$QRYڱ򧂲~2$j`B=ɬҵ42$ ;2m'2$T*_ba2$/ұTv8<`W%' $72$6( :.!ɋx]2$:ÝT`q ս2$C:N@Kk߿2$e}Rv> ?YC2$0V*eh@S>2$G=XAm|f.2$i_{kZ,.2$ו47Oo߮02$P|mj7پRgh!S2$h}#FIZ2$sFOBJ pVn i$2$WS,qB"D 2$.=_;٦n@S2$mvzXm0E@%e{*%(  \  S A ?"\  S A ?"\ Microsoft Word 8.0@+W@_@ɃTBz ՜.+,D՜.+,8 hp  Compaqm&O   QMB 3250 Titlel(RZ _PID_GUID _PID_HLINKSAN{9E743D61-EF80-11D8-9EAF-00045A9E307E}A "=http://www.careerbank.com/lnorm1  FMicrosoft Word Document MSWordDocWord.Document.89q  S  A ?"\   S  A ?"\   S  A ?"\   S  A ?"\  S A ? "\  S A ?"D   A  "V  C A  ? "h  s *A  ? ?  "V  C A  ? "V  C AP  ? "V  C A  ?"V  C A ?"V  C A ?"V  C A ?"V  C A ?"V  C A ?"V   C A ?"V ! C A ?"V " C A ?"V # C A ?"V $ C A ?"V % C A ?"V & C A ?"\ ' S 'A ?"D (  A"D )  A"V , C A ?"V - C A! ? "V . C A ?"V / C A# ?!"V 0 C A$  ?""V 1 C A !?#"V 2 C A. "?$"V 3 C A& #?%"V 4 C A' $?& "V 5 C A( %?'"V 6 C A) &?("V 7 C A+ '?)"V 8 C A, (?*"V 9 C A- )?+"V : C A* *?,"V ; C A/ +?-"V < C A0 ,?."V = C A% -?/"V ? C A2 .?1"V @ C A1 /?2"V C C A4 0?3"V F C A3 1?4"V G C AQ 2?5"V I C A8 3?6"V K C A7 4?F"V L C A! 5?J"V M C A< 6?K"V N C A9 7?L"\ O S OA; 8?M"V Q C A: 9?G"V R C A6 :?H"V S C A= ;?I"V T C A? <?U"V U C AC =?X"V V C A@ ??Z"V W C AA >?Y"V X C AB @?["V Z C AE B?V"V [ C AF A?W"V \ C A5 C?\"V ] C A! E?O"\ ^ S ^A> D?N"\ _ S _A! G?Q"V ` C AG F?P"V a C AH J?T"V b C A! I?S"V c C AI H?R"V d C AJ K?]"V e C AK L?^"V f C AL M?_"V g C AM N?`"V h C A! O?a"V i C AD P?b"V j C AO Q?c"V k C A ?"b l C $A" norm10"VB m C D9"VB n C D8"VB o C D:"VB p C D7"VB q C D="VB r C D>"VB s C D<"VB t C D;"VB u C DA"VB v C D@"VB w C DB"VB x C D?"\B y S DE"\B z S DD"\B { S DC"B S  ?j=x=>*?l?>D?GQHeIM4NHPPSSaVVW[[Ob.d&kloo.uvvvvdg>'uܵ,>S.Nf(+x-y-z-A.///p0L1M1N12555O^_{_dfgimnooppSpw'ԆM)k4!%4V4 / 4 84 } 4 --4Xx!#4`(%4#tU% &4g txqC1444J 4D 4-5 4 4 r4 h!Z4!4" & 4#4$a4&?4%4).t'!4(>),C*t,x!4.G\4-T4/p4041H ]42$43h (144h YD45468 *47h48X/s49!4:#D4;44<t44=p4l(h%.t?84@h}4!4CVu!4FX$4GD 4IL4p tntm tottts tq trtxx!px!tvHpHtuHp"ptwHx!t{ tzXX]tyX]tKH 4Q&4R"4S8D4LT4Mp4N,x4Ox4^ 4]T4`(d4_F4c 4bT4a y?4TH +4Z@$@4[4UH T 4W4V4Xn4\H y4dh. L 4e!4f(`p4g'14hT4i4j##4 _1081527477 _1081531700 _1081587217 _1081610499 OLE_LINK1 OLE_LINK2 OLE_LINK3 _955987952 OLE_LINK4 OLE_LINK5 OLE_LINK6 OLE_LINK8*;@@@@@ *;()-99:KLP\lmpr-46MOfh >@()-99:KLP\lmpr-46MOfh >@Z\su  "$;=TVmo7NPgi/1HJacz|.0GI`by{(*AC]_vx Larry Winner Larry Winner Larry Winner Larry Winner Larry Winner larry.winner Larry Winner/C:\windows\TEMP\AutoRecovery save of notes1.asd Larry Winner/C:\windows\TEMP\AutoRecovery save of notes1.asd Larry Winner/C:\windows\TEMP\AutoRecovery save of notes1.asd Larry Winner-\\SAMBA\WINNER\public_html\mar5620\notes1.docT[:hI iET DZS ) ml*6:`" S"?# u Hb5[ !-Sm 1phxPt) LC NVܨg5{ (朥4t  / Dlĵ<YxJB BQo,h} p^  DR~' P'R Iz'Xl w(t++ph=- !- @D->4E&3.lK / "] 1 a 59>AdB6>NX=;`Xk=Lpww[>,A(Hvy CvQ> @^C,!RF VG DMH 2J J aK 3L>[޷]8NtyCO28VvP^P^G#fRơ<2MRꖈ&+1X UP[ #r]#!x]Զn^  _P]R2@aŎn@ &bfVd1Pe(\_}MewRgm#Ki ;GoHd0mo8Lf|t 0u Sv(.3\Bx ky i{<}`:F^`o() hh^h`OJQJo(^`o()0^`0o() hh^h`OJQJo(hh^h`o()M^`Mo()h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hH^`o()M^`Mo()^`o()hh^h`o()h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hH^`o()^`o() hh^h`OJQJo( hh^h`OJQJo(^`o(.hh^h`o()^`o()h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hH hh^h`OJQJo(hh^h`o()^`o()^`o() hh^h`OJQJo(h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHhh^h`.hh^h`o() hh^h`OJQJo(^`o()^`o()^`o()h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hH hh^h`OJQJo()hh^h`.h88^8`OJQJo(hHh^`OJQJ^Jo(hHoh  ^ `OJQJo(hHh  ^ `OJQJo(hHhxx^x`OJQJ^Jo(hHohHH^H`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHhh^h`o()hh^h`o()  ^ `>*OJQJo()^`o()e\e^e`\o()^`o()^`o()M^`Mo()^`o()^`o() hh^h`OJQJo( hh^h`OJQJo(hh^h`o()hh^h`o(. hh^h`OJQJo( hh^h`OJQJo(^`o()h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hH^`o()))^)`o()^`o()h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHhh^h`o()hh^h`o()^`o()^`o()hh^h`o()^`o()0^`0CJOJQJo()^`o()**^*`o()^`6o()^`o()^`o() hh^h`OJQJo(^`o()^`o() hh^h`OJQJo( hh^h`OJQJo(h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hH hh^h`OJQJo(hh^h`o()h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hH^`o()TVd@ &b0mo+1X;GodB6yCO}Me<}BRFaKAml*y CK /T 2@at LCSmJ0uww[>DR~'#fRVGf|t^iu 5{)ky!-}2JNVa 53\Bx"] 1=-3LUP[-YDMH#r]#KiI5[ w(@^C)exp^    wRgDl!x][:P'( _/X=;Iz'VvPk=SMR]8N4i{?# BQo0&3.@D-++1`" SvT @ؤTUrXrYZ[klno''g g gggggggggggg,g-g.g/g0g1g2g34567=>?@ABCDEFjJjKjLjMjNjOjPjQ(W(X(\(]^_`defijJpJqrtJvJwJxJyJJJJFFFFFFIIVVP@PXP@P^P@PbP@PtP@PxP@PPP@PP@PP@PP@P P@PP$@PPPPP<@P$PL@P*PX@P.P0Pd@P4Pl@P8P:P<P|@PLPNPPPRPTPVPXP@P\P^P`P@PhPjPlPnPpPrPtPvPxP@P~PPPPPPP@PP(@PP4@PPP@@PPPP@PP\@PPl@PPx@PPPP@PP@PP@PP@PP@PP@PP@PP@PPPP@PP@PPP@P PP @PP(@PP0@P"PH@GTimes New Roman5Symbol3& ArialE& Century Gothic?5 Courier New;Wingdings"1hXZe탈&FpTBz&D;!0dO(QMB 3250 Larry Winner Larry WinnerCompObjj