ࡱ> DFCZ -bjbj tjj(lNNNb8Db| j"v{x{x{x{x{x{x{$/} O {-N{k#{k#k#k#llNv{k#v{k# k#.s",Nb{ @`!b6 !rvJb{{0|vY"Yb{k#bb MACROBUTTON MTEditEquationSection Equation Section 1 SEQ MTEqn \r \h \* MERGEFORMAT  SEQ MTSec \r 1 \h \* MERGEFORMAT QMETH 520: Managerial Applications of Regression Models Winter, 06: TTH 3:30 - 5:20 PM in BLM 311 (http://faculty.washington.edu/htamura/qm520) Instructor: Hiro Tamura: 362 MacKenzie Hall: (206) 543-4399; htamura@u Office Hours: TTH 2:00 - 3:20 PM Course objective: A fundamental task of a manager is to make predictions. In many circumstances, the manager relies on her/his intuitive judgment. For a complex problem with important consequences, however, a manager is compelled to gather data for prediction. She/he can proceed to interpret cues and makes prediction using her/his expertise, similar to a physician who diagnoses the health of a patient from a variety of test results. Could the manager (an expert) improve on her/his intuition by using statistical methods? Works of outstanding psychologists have discovered that for many situations a simple mechanical (statistical) combination of cues outperforms a prediction by the experts. It is difficult for an individual to combine information from different sources. Regression and other statistical models fill this need by providing a mechanism that combines cues for prediction. For forecasting using regression, the manager first determines a variable to predict for each study unit in the target population. This variable is called the dependent variable. Next, the manager selects a set of variables that in her/his judgment could provide cues for prediction. These variables are called independent variables or explanatory variables. S/he then formulates a statistical model for combining the cues to predict the dependent variable. The statistical model is called a population regression model, and has unknown parameters that need to be estimated from data (sample). The regression with parameter values estimated from data is called the sample regression equation. The manager must examine the performance of the sample regression equation before using it. Here are main points for check: Does the sample regression explain the data reasonably well? Does every coefficient provide a reasonable interpretation of the effect of the independent variable? Is the population regression model a good approximation of the true underlying data generating process? Do the data contain any surprises that might be ill-affecting the sample regression? Can the regression be simplified by selecting a subset of the original independent variables without significantly lowering its performance? Software: We will use SPSS for computation. SPSS is one of the most popular software choices among practitioners, and well suited for analysis of cross section data. Excel worksheets can be read by SPSS, so Excel is useful for reading and editing data before analysis using SPSS. Texts & References (T1) Dielman, T. E. (2005), Applied Regression Analysis. A second Course in Business and Economics Statistics. Fourth Edition (R) Articles downloadable from the course web. Mandel, B. J. (1969) The Regression Control Chart. Journal of Quality Technology 1: 1 Comiskey, E. E. (1966) Cost Control by Regression Analysis, The Accounting Review 41: 2 Tamura, H. (1979) Using Statistics to Find a Reasonable Cost for Medicaid Payments. Journal of Contemporary Business 8: 2 Tamura, H., Lauer, L.W. and Sanborn, F. A. (1985) Estimating Reasonable Cost of Medicaid Patient Care Using a Patient Mix Index. Health Services Research 20: 1 Mochel, D. and Tamura, H. credit scoring systems, Credit Union Executive, The Winter 81: 5 Karpoff, J. M. (2001) Public versus Private Initiative in Arctic Exploration: The Effects of Incentives and Organizational Structure. Journal of Political Economy 109: 1 (To continue) Grading: Homework/Quiz 6 30% Exam (Take Home) 1 35 Project 1 35 Total 100% Guest Speakers for Special Topics Mike Bowcut, Director, Database Marketing & Analysis, REI Vandra Huber, Professor, Human Resource Management University of Washington Business School Schedule (subject minor revisions): I. Orientation 1. 1/3/Tu A. Course overview, explanation of project Statistics for management Regression analysis overview Standard regression vs. logistic regression Three performance measures of a regression Applications for Management Examples 2. 1/5/Th A. Introduction to SPSS Study Case: Introduction to SPSS Importing an Excel worksheet Define missing values; labeling variables and string variable values. Graphs menu Analyze menu Regression outputs B. Review of Simple Regression Analysis Text Ch. 2 and 3, Siegel Ch. 11 and 12 (where necessary) Study Case: Review Questions Interpreting scatterplot and correlation coefficient on line demonstration Computing and interpreting simple regression outputs II. Basics 3. 1/10/Tu A. Population Regression Model Text Ch. 3; 4.1, 2 Study Case: Marketing a New Shampoo Formula Data Analysis Understanding each component of the population regression model  EMBED Equation.DSMT4 = conditional mean of Y given X bk = population regression coefficient of Xk e = disturbances DGP: data generating process for regression Using log transformation for variables SPSS menu for transforming variables B. Examination of data using scatterplot matrix pattern of association among variables (y vs. x) (x vs. x), outliers C. Basic Regression Outputs Text Ch. 3.4; 4.3 Interpreting the equation section of the output bk standardized regression coefficient,  EMBED Equation.DSMT4  Interpreting the ANOVA section of the output SSR, SSE, SST, and R2; MSR, MSE, MST, and adjusted R2 unconditional population regression model (hidden) 4. 1/12/Th A. Basic Hypothesis Testing in Regression Text Ch. 3.4; 4.3, 4.4 F-distribution, Excel FINV, and FDIST t-distribution, Excel TINV, and TDIST Distribution of t-stat and F-stat on line demonstration Testing significance of the sample regression F-stat=MSR/MSE Testing significance of an independent variable standard error of coefficient,  EMBED Equation.DSMT4 ; t-stat= bk/ EMBED Equation.DSMT4  two sided test vs. one sided test B. Effect of Adding / Omitting an Independent Variable Study Case: Marketing a New Shampoo Formula Comparison of Simple vs. Multiple Regression how each of three performance measures is affected? 5. 1/17/Tu Prediction of Y| xm for a new study unit A. Point vs. Interval Prediction Text Ch. 3.5, 4.5, Appendix D. Study Case: Forecasting Labor Hours For Moving Study Case: Marketing a New Shampoo Formula - Prediction Point vs. Interval prediction Interval prediction ignoring estimation errors Interval prediction accounting for estimation errors standard error of the regression se standard error of the estimate sm for simple regression (text p. 104) standard error of the estimate sm for multiple regression standard error of the prediction sp (text p. 106) key formula:  EMBED Equation.DSMT4 (text p. 106) Measures of prediction performance Deleted Residual, PRESS, R2_PRED, Sample splitting III. Diagnostics of the Sample Regression Equation 6. 1/19/Th A. Diagnostics Tests Assessing the Assumptions Text, Ch. 6:1-6.6 Study Case: Forecasting Labor Hours For Moving Residual Analysis key assumptions, RVSF plot, normal plot, testing for heteroscedasticity B. Diagnostics Tests Multicollinearity Text, Ch. 4.6 Study Case: Promotion Planning Study Case: Variables in Hospital System multicollinearity, inflated standard errors, VIF 7. 1/24/Tu Diagnostics Tests Influential Observations Text, Ch. 6.7 Study Case: Regression Control leverage, t-resid, Cooks D, DFITS 8. 1/26/Th A. PDCA Cycle for Modeling B. Review / Q&A IV: Modeling Techniques 9. 1/31/Tu Categorical Independent Variables Text, Ch. 7.1, 7.2; Ch. 4.4.2 Study Case: Salary Discrimination Study Case: Factors Driving Sales Study Case: Product Display symbolic representation, indicator (dummy) variables, additive vs. interaction terms. SS for full model, for reduced model; conditional SS 10. 2/2/Th Trends and Seasonality Text, Ch. 3.6, 7.3 Study Case: Ice cream Production Study Case: MLB Salary Study Case: Growth of Heart Transplant Surgery Study Case: Seasonal pattern of University Book Store Sales Study Case: Detecting Spurious Trend Standard trends, seasonal dummy variables, DW test, AR(1) disturbance 11. 2/7/Tu Selection of Independent Variables Text, Ch. 8; NKNW Ch. 8.3. Study Case: Modeling Hospital Operations backward elimination, stepwise regression; all possible regression, Cp. V: Qualitative Dependent Variable 12. 2/9/Th Review of Contingency Table Analysis Siegel, Ch. 17 Study Case: Pet Ownership and Patient Survival  EMBED Equation.DSMT4 distribution, Excel CHIDIST, CHIINV two way contingency table,  EMBED Equation.DSMT4 test of independence 2 by 2 table, relative risk, odds ratio 13. 2/14/Tu Basics of Logistics Regression Text Ch. 10.3 Model Interpretation Study Case: Logistic Regression Model Interpretation generalized linear models, binomial distribution, logit link B. Estimation and Significance Testing Study Case: Getting a Flu Shot maximum likelihood estimation 14. 2/16/Th Logistic Regression - Grouped Data Text Ch. 10.3, Study Case: Preference Testing weighted least squares 15. 2/21/Tu A. Multinomial Logit Regression Study Case: Awareness for Health Care multinomial distribution B. Ordinal Response Variable Study case: Awareness for Health Care Ordered probit model, ordered logit model Invited Speakers SP1. 2/23/Th Mike Bowcut, Director, Database Marketing & Analysis REI SP2. 2/28/Tu Vandra Huber, Professor, Human Resource Management University of Washington Business School Special Topics 16. 3/2/Th Analysis of the Correlations Among Independent Variables Janet R. Daling and Tamura, H. (1970) "Use of Orthogonal Factors for Selection of Variables in a Regression Equation - An Illustration." Applied Statistics (The Journal of the Royal Statistical Society-Series C) 19 Study Case: Selection of Factors for UW Admission factor analysis of the correlation matrix Review / Q&A 17. 3/7/Tu 18. 3/9/Th Final Exam Due. 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