ࡱ> <  %      H ]mvFSUjhf][ bjbj [ΐΐ/ˈˈˈ@ϊD t:.,,$k-!$%x+++++++1l4+ˈ{%Q )!{%{%+k.EIEIEI{%d~Lˈk+EI{%+EIEI* |3]kn+kP 3:AR+.0.+D5> D5+D5ˈ+ {%{%EI{%{%{%{%{%++EI{%{%{%.{%{%{%{%D5{%{%{%{%{%{%{%{%{% :  Quantitative Techniques Subject No. 11  EMBED Word.Picture.8 Strathmore University  Distance Learning Centre  P.O. Box 59857, 00200, Nairobi, Kenya. Tel: +254 (02) 606155 Fax: +254 (02) 607498 Email:  HYPERLINK "mailto:dlc@strathmore.edu" dlc@strathmore.edu  Copyright ALL RIGHTS RESERVED. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, and recording or otherwise without the prior written permission of the copyright owner. This publication may not be lent, resold, hired or otherwise disposed of by any way of trade without the prior written consent of the copyright owner. THE REGISTERED TRUSTEES STRATHMORE EDUCATION TRUST 1992 Acknowledgment We gratefully acknowledge permission to quote from the past examination papers of the following bodies: Kenya Accountants and Secretaries National Examination Board (KASNEB); Chartered Institute of Management Accountants (CIMA); Association of Chartered Certified Accountants (ACCA). We would also like to extend our sincere gratitude and deep appreciation to Mr.Vincent Nyauma for giving his time, expertise and valuable contribution, which were an integral part in the initial development of this Revision Kit. He holds the following academic honours, B.Tech (Production Engineering) and CPA. He is a senior lecturer at Strathmore University, at the Information Technology Centre. Contents  TOC \h \z \t "Heading 1,2,Heading 2,3,Header,1"  HYPERLINK \l "_Toc31175139" Acknowledgment  PAGEREF _Toc31175139 \h ii  HYPERLINK \l "_Toc31175140" Part I: Introduction  PAGEREF _Toc31175140 \h v  HYPERLINK \l "_Toc31175141" General Examination Techniques  PAGEREF _Toc31175141 \h vi  HYPERLINK \l "_Toc31175142" Syllabus  PAGEREF _Toc31175142 \h vii  HYPERLINK \l "_Toc31175143" Topical Guide to Past Paper Questions  PAGEREF _Toc31175143 \h ix  HYPERLINK \l "_Toc31175144" Part II: Revision Questions and Answers  PAGEREF _Toc31175144 \h 1  HYPERLINK \l "_Toc31175145" Questions - Past Papers  PAGEREF _Toc31175145 \h 1  HYPERLINK \l "_Toc31175146" Answers - Past Papers  PAGEREF _Toc31175146 \h 34  HYPERLINK \l "_Toc31175147" Part III: Comprehensive Mock Examinations  PAGEREF _Toc31175147 \h 111  HYPERLINK \l "_Toc31175148" Questions - Mocks  PAGEREF _Toc31175148 \h 111  HYPERLINK \l "_Toc31175149" Answers - Mocks  PAGEREF _Toc31175149 \h 126  HYPERLINK \l "_Toc31175150" Statistical Tables  PAGEREF _Toc31175150 \h 155  References We gratefully acknowledge use of the Kenya Accountants and Secretaries National Examination Board (Kasneb). Other reference books include Principles of operations research for management by F S. Budnick, D. Mcleavey, R. Mojena 2nd edition Quantitative techniques simplified by N.A Saleemi Quantitave techniques for managerial decision by U K Srivastara, G R Shenoy and S C Sharma Quantitative techniques by T Lucey 4th edition Part I: Introduction How To Use This Revision Kit Questions and answers have been presented following from the structure of the quantitative techniques paper. Looking at the chapter arrangement the structure can be seen. After getting the concept from the Distance Learning Course Package (DLC package) and trying the exercises at the end of the topics, you can now attempt the questions from the revision kit. Questions have been picked from various books before questions from past papers. Answer progressively to the questions from the past papers. In this way you will have been introduced to exam situation. Do not look at the answer while answering the question. First attempt the question, and then look at the answer. Where you have gone wrong is where you need to work on. Read the model answers and get more information from the DLC package and the books recommended. As you progress in answering, time yourself to work in the shortest time possible remembering the exam techniques highlighted later (formulas, explanations of steps, number of significant figures). At the end, there are three mock papers. Do at least one paper in exam situation (timing yourself for three hours continuously). If possible do all the questions in 5 hours. This will ensure you have grasped all the concepts of quantitative techniques. Then mark for yourself using the model answers. If possible do all the mock papers and mark for yourself. This will give you leverage on what to expect in exams. General Examination Techniques Structure Of The Paper The paper consist of two sections Section 1 and Section 2 Section 1 has five questions and usually you are required to answer any 3.The section mostly covers the whole syllabus but does not concentrate on operations research. Every question contains both mathematical and essay parts. Section 2 has three questions and usually you are required to answer any 2. The section mainly covers operations research but can come from any topic. Questions go more into detail on a specific topic. The areas mostly covered here are network analysis, decision analysis/game theory and linear programming. In depth knowledge of a particular topic is required General Trend The examiner has been deviating slightly from common questions of mathematical nature. Knowledge of the topics is necessary other than just the mechanics of the calculations alone. The examiner wants you to know more about the little topics that are covered like transportation and assignment, time series and smoothening. How To Tackle Questions Follow the instructions (Complete the number of questions required in each section without answering more than required) Always start with questions that are easy for you. Be time conscious. Do not spend more than 1.8 minutes (1 minute 48 seconds) per mark awarded. Be careful on things like number of significant figures and decimal points. (Not giving too many or too few depending on the question) Write the formula that you are to use before actually using it. Try as much as possible to explain the calculation as you go through it step by step Answer what is required and not what you know about a particular subject. Tit Bit Quantitative techniques subject has several equations/formulas. These are to be memorized. To make it easy, you are requested to do as many exercises as possible to enable ease of remembrance of the formulas and steps. Definitions, advantages, disadvantages and area of use of the various formulas and theories have to be known. This is there for every question Syllabus CPA PART II - SECTION 4 PAPER NO.11 QUANTITATIVE TECHNIQUES  OBJECTIVE To provide the candidate with quantitative techniques for use in solving managerial problems. SPECIFIC OBJECTIVES A candidate who passes this subject should be able to: Apply quantitative methods in solving optimisation problems in management. Use statistical methods in decision making. Design statistical and mathematical models for estimation and forecasting. Use network analysis in project management. CONTENT Linear Algebra and Calculus Sets and set theory Functions and graphs Linear equations, higher-order equations, inequalities and simultaneous equations. Matrix algebra Application of matrix algebra to input-output analysis and elementary makovian processes Differentiation and integration of polynomial, exponential and logarithmic functions. Application of calculus to economic models. Descriptive Statistics and Index Numbers Measures of location Measures of dispersion Bivariate data: Regression analysis, covariance and correlation, coefficient of determination, estimation and forecasting Multivariate data: Multiple linear regression, least squares method, coefficient of correlation and determination, estimation and forecasting. Unregistered price and quantity indices. Weighted, index numbers Fixed base and change of base period; chain base indices Tests of perfection; consumer price index, stock market index and production indices. Probability Definition of probability - relative frequency approach, sample space Events; mutually exclusive events and independent events Laws of probability, conditional probability and Bays theorem Permutations and combinations Discrete probability distribution functions; Binomial multinomial, hyper-geometric, geometric and Poisson distribution Continuous probability distribution functions: Exponential, normal, student -t, chi-square and fisher distributions Sampling and Estimation Random, cluster, stratified and systematic sampling The central limit theorem Sampling distribution of the mean and difference between means Sampling distribution of proportions and differences between proportions Tests of hypothesis Analysis of Time Series Analysis Time series models, trends, seasonal variations and forecasting. Linear Programming Definition of decision variables, objective and constraint functions Formulation of a linear programming problem, graphical solution, introduction to simplex method, interpretation of computer assisted solution printout. Duality: Economic meaning of shadow prices and other computer output data An initiative (but not computational) understanding of integer, dynamic and convex programming techniques Application to transport and assignment problems. Decision Theory Decision making under conditions of risk and uncertainty The expected value criterion, the minmax and maxmax criteria, conditional payoff and opportunity cost tables Decision trees and sequential decisions Game theory Network Analysis Activities sequencing and Gantt Charts Identification of critical path Floats and non-critical activities Crashing of projects Stochastic critical path analysis Cost and Resources scheduling Topical Guide to Past Paper Questions Chapters arranged according to exam question flowTopic 1Linear algebra, matrices algebra application, calculus and set theoryTopic 2Measures of central tendencies, measures of dispersion and index numbersTopic 3Basic probability and probability distributions Topic 4Sampling and sampling inferenceTopic 5Measures of relationship, linear regression, correlation and time series analysisTopic 6Linear programmingTopic 7Decision theory and game theoryTopic 8Network analysisTopic 9Mock paper and model answers Part II: Revision Questions and Answers Questions - Past Papers  LINEAR ALGEBRA & CALCULUS QUESTION ONE A company is selling an item for ps= Sh.5. Profit is related to selling price p by  EMBED Equation.3  Is the profit an increasing function or a decreasing function when ps = 5? If the price is changed to Sh.4.75, find whether profit would increase or decrease and find the change in profit. At what price is the profit maximized? QUESTION TWO Demand function for a firm is given by  EMBED Equation.3  P is the price of the product, Q is the quantity demanded, and the total cost (C) is given by  EMBED Equation.3  At what price and quantity will the firm have maximum profit? If the firm aims at maximizing sales, what price should it charge? QUESTION THREE Two CPA students were discussing the relationship between average cost and total cost. One student said that since average cost is obtained by dividing the cost function by the number of units Q, it follows that the derivative of the average cost is the same as marginal cost, since the derivative of Q is 1. Required: Comment on this analysis. Gatheru and Kabiru Certified Public Accountants have recently started to give business advise to their clients. Acting as consultants, they have estimated the demand curve of a clients firm to be; AR=200-8Q Where AR is average revenue in millions of shillings and Q is the output in units. Investigation of the client firms cost profile shows that marginal cost (MC) is given by: MC=Q2-28Q+211(In million shillings) Further investigations have shown that the firms cost when not producing output is sh.10 million. Required: The equation of total cost The equation of total revenue An expression for profit. The level of output that maximizes profit The equation of marginal revenue. (Q 1 June 2002) QUESTION FOUR XYZ Company Limited invests in a particular project and it has been estimated that after X months of running, the cumulative profit (Sh.000) from the project is given by the function  EMBED Equation.3 , where x represents time in months. The project can run for eleven months at most. Required: Determine the initial cost of the project. Calculate the break-even time in months for the project. Determine the best time to end the project. Determine the total profit within the break-even points. (Q 1 Dec 2001(b) QUESTION FIVE The number of shoppers queuing at any given time in a certain supermarket in downtown Nairobi can be approximately represented by the equation: y = x3  14x2 + 50x over the range 0 d" x d" 8.5, where y is the number queuing and x is the time in hours after the store opens at 9.00a.m. (So that, for example 10.30a.m. is x=1.5, and 5.30p.m. - when the store closes is x= 8.5). Required: The management wants to know when they should deploy more cashiers and the number queuing at that time. Determine the number of man-hours spent per day by shoppers queuing. An electronics firm carries out a small-scale test launch of a new low-priced pocket calculator. It estimates from this test that if it went into full-scale production it would sell between 1,000 and 2,500 calculators per month, and that its monthly revenue in thousands of shillings over this range of sales could be represented by the equation: R = - x2 + 5x Where: x is the monthly output in thousands of calculators (it is assumed that it sells its entire output). From experience of calculator production, the firm estimates its marginal cost in thousands of shillings could be represented by the equation: MC = x2 x + 2 and that its fixed costs will be Sh.500 per month. Required: Determine the average cost and revenue equations for this firm. Determine the profit-maximizing output, the price that should be charged to maximize profit, and how much each calculator will then cost to make. (Q 1 June 2001) QUESTION SIX Define the following terms as used in Markovian analysis: Transition matrix. Initial Probability vector Equilibrium Absorbing state A company employs four classes of machine operators (A,B,C,D): all new employees are hired as class D and, through a system of promotion, may work up to a higher class. Currently, there are 200 class D, 150 class C, 90 class B and 60 class A employees. The company has signed an agreement with the union specifying that 20 percent of all employees in each class be promoted, one class in each year. Statistics show that each year 25 percent of the class D employees are separated from the company by reason such as retirement, resignation and death. Similarly 15 percent of class C, 10 percent of class B and 5 percent of class A employees are also separated. For each employee lost, the company hires a new class D employee. Required: The transition matrix. The number of employees in each class two years after the agreement with the union. The equilibrium state in number of employees. (Q 2 June 2002) QUESTION SEVEN Two firms A and B in Nairobi Industrial area make glue. The cost functions for making glue for the two firms are as follows: Firm A - C = 0.2x + 200 Firm B - C = 0.6x + 50 Where x is litres of glue produced in 000. Required: By drawing a graph of these functions, show whose firms costs increase more rapidly. On the study of costs and revenue for production of biro pens by a small company ACO Ltd, the following expressions were determined. Before production starts a set-up cost of Sh.1500 existed. AR = 600 0.5q Average revenue MC = 140 8q + 0.15q2 Marginal cost Required: The expression for profit. At what quantity q is profit maximized. Is this quantity q also the point that revenue is maximized? QUESTION EIGHT Explain the following terms as used in calculus: Turning point. Second order derivative condition. Partial derivative. Mixed partial derivative. Saddle point. Drumstick Chicken Wings Ltd supplies chicken wings for Kuku Inn with the following demand and cost functions for a given week: P = 100 0.01 x - Price TC = 50x + 30,000 - Total cost Where: x number of chicken wings supplied. Required: Total revenue for Drumstick Chicken Wings Ltd. Determine the number of chicken wings that maximize weekly profit. What is the difference in profit if the Drumstick Chicken Wings Ltd. objective is to maximize revenue rather than profit? QUESTION NINE Given the following input output matrix and demand vector of shoes S, rubber R and glue G industries, determine the production vector. Input output matrix  EMBED Equation.3  Demand vector  EMBED Equation.3  If in (a) above the demand of industries changes as follows: S decreases by 10 units R increases by 5 units C increases by 10 units. What should be the production levels? DESCRIPTIVE STATISTICS AND INDEX NUMBERS QUESTION ONE The index of industrial production in the Utopia country by July 2001 is given below: Sector WeightJuly 2001 Index (1994 = 100)Mining and quarrying Manufacturing: Food, drink and tobacco Chemicals Metal Engineering Textiles Other manufacturing Construction Gas, electricity and water 41 77 66 47 298 67 142 182 80 361 106 109 72 86 70 91 84 115 Required: Calculate the index of industrial production for all industries and manufacturing industries. Comment on your results. Explain some of the uses of index numbers. What are some of the limitations of index numbers? (Q 2 Dec 2001) QUESTION TWO The table below shows the income per month of borrowers and the percentage of all mortgages as provided by Building Society Mortgages in the year 2000. Income per month of borrowers (Sh.)Percentage of all mortgages Under 30,000 30,000 - 34,999 35,000 - 39,999 40,000 - 44,999 45,000 - 49,999 50,000 - 59,999 60,000 - 69,999 70,000 - 99,999 100,000 - 149,999 150,000 and over  5 2 3 5 10 15 18 21 17 4 Required: Calculate some suitable measures of central tendency and dispersion and comment on your results. Explain with an example the value of descriptive statistics in the accounting function. (Q 3 (a) Dec 2001) QUESTION THREE A machine produces circular bolts and, as a quality control test, 250 bolts were selected randomly and the diameter of their heads measured as follows: Diameter of head (cm)Number of components0.9747 - 0.9749 0.9750 - 0.9752 0.9753 - 0.9755 0.9756 - 0.9758 0.9759 - 0.9761 0.9762 - 0.9764 0.9765 - 0.9767 0.9768 - 0.9770 0.9771 - 0.9773 0.9774 - 0.9776 0.9777 - 0.9779 0.9780 - 0.9782 2 6 8 15 42 68 49 25 18 12 4 1 Required: Determine whether the customer is getting reasonable value if the label on the circular bolt advertises that the average diameter of the head is 0.97642 cm. In what situation would a weighted mean be used? Describe briefly how to estimate the median on a grouped frequency distribution graphically. Why is the mode not used extensively in statistical analysis? The standard deviation is the natural partner to the mean. Explain. (Q 2 June 2001) QUESTION FOUR Explain the following terms as used in index numbers: Quantity indices. Base year. Chain index numbers. Retail price index. The table below reports the annual net salary amounts for an accountant who begun employment with an auditing firm in 1995. In addition, the values of the Consumer Price Index (CPI) for 1995 through 1999 is shown. YearSalary (Sh.)CPI1995 1996 1997 1998 1999360,000 370,000 390,000 395,000 400,000130.7 136.2 140.3 144.5 148.2 Required: Determine whether there is a difference in percentage salary increase between 1995 and 1999 in terms of stated (current) shillings amounts and in terms of constant (deflated salary, using CPI) shillings. (Q 2 Dec 2000) QUESTION FIVE Moving averages are often used in an effort to identify movements in share prices. Approximate monthly closing prices (in Sh. per share) for Toys Children Ltd. for December 2000 through November 2001 are shown below: MonthPrice (Sh.)December 2000 January 2001 February 2001 March 2001 April 2001 May 2001 June 2001 July 2001 August 2001 September 2001 October 2001 November 2001 40 38 39 41 36 41 34 37 35 37 40 41 Required: Use a 3-month moving average to forecast the closing price for December 2001. Use a 3-month weighted moving average to forecast the closing price for December 2001. Use weights of 0.4 for most recent period, 0.4 for the second period back and 0.2 for the third period back. Use exponential smoothing constant of  EMBED Equation.3  to forecast the closing price for December 2001. Which of the three methods do you prefer? Why? (Q 5 Dec 2001) PROBABILITY QUESTION ONE A problem is given to three managers A, B, C whose chances of solving are , S!, respectively. What is the probability that the problem will be solved? QUESTION TWO Three groups of children contain respectively 3 girls and 1 boy; 2 girls and 2 boys; 1girl and 3 boys. One child is selected at random from each group, show that the chance that the three selected, consist of 1 girl and 2 boys is 13/32. QUESTION THREE The following table gives a bi-variate frequency distribution of 50 managers according to their age and salary (in rupees). Salary in rupees Age in years1000-15001500-20002000-25002500-3000Total20-30 2 3 - - 530-40 5 4 2 1 1240-50 - 2 10 3 1550-60 - 1 8 9 18Total 7 10 20 13 50 If a manager is chosen at random from the above distribution, find the chance that; (i) he is in the age group of 30-40 and earns more than Rs.1500, (ii) his earnings are in the range of Rs.2000-2500 and is less than 50 years old. QUESTION FOUR Computer analysis of satellite data has correctly forecast locations of economic oil deposits 80% of the time. The last 24 oil wells drilled produced only 8 wells that were economic. The latest analysis indicates economic quantities at a particular location. What is the probability that the well will produce economic quantities of oil? QUESTION FIVE A firm recently submitted a bid for a turnkey project for a 500 MW power plant. If its main competitor submits a bid, the chances of bid being awarded to the firm is 0.3. If the main competitor doesnt bid, there is a chance of the firm getting the contract. There is a 0.50 chance that the main competitor will bid. What is the probability of the firms getting the contract? What is the probability that the competitors bid given that the firms bid is awarded? QUESTION SIX A firm has four plants scattered around the city producing the same homogeneous item at all plants. The first plant produces 30 per cent of the total production, second plant 25 per cent, third plant 35 per cent and the fourth plant 10 per cent. The firm has a single warehouse in the city for storing the finished product of all the plants without any distinction. From the past performance records on the proportion of defectives, it has been found that 5 per cent, 10 per cent, 15 per cent and 20 per cent from the items produced at plants 1, 2, 3, and 4 respectively are defective. Before the shipment of the items to a dealer, one unit is selected and found defective. What is the probability that the item was produced in plant 3? QUESTION SEVEN State clearly what is meant by two events being statistically independent.. In a certain factory that employs 500 men, 2% of all employees have a minor accident in a given year. Of these, 30% had safety instructions whereas 80% of all employees had no safety instructions. Required: Find the probability of an employee being accident-free given that he had: no safety instructions. safety instructions. An electric utility company has found out that the weekly number of occurrences of lightning striking the transformers is a Poisson distribution with mean 0.4. Required: The probability that no transformer will be struck in a week. The probability that at most two transformers will be struck in a week. (Q 3 June 2002) QUESTION EIGHT The past records of Salama Industries indicate that 4 out of 10 of the companys orders are for export. Further, their records indicate that 48 percent of all orders are for export in one particular quarter. They expect to satisfy about 80 orders in the next financial quarter. Required: Determine the probability that they will break their previous export record Explain why you have used the approach you have chosen to solve part (i) above. Grear Tyre Company has just developed a new steel-belted radial tyre that will be sold through a chain of discount stores. Because the tyre is a new product, the companys management believes that the mileage guarantee offered with the tyre will be an important factor in the consumer acceptance of product. Before finalizing the tyre mileage guarantee policy, the actual road test with the tyres shows that the mean tyre mileage is  EMBED Equation.3  kilometres and the standard deviation is  EMBED Equation.3 =5,000 kilometres. In addition, the data collected indicate that a normal distribution is a reasonable assumption. Required: Grear Tyre Company will distribute the tyres if 20 per cent of the tyres manufactured can be expected to last more than 40,000 kilometers. Should the company distribute the tyres? The company will provide a discount on a new set of tyres if the mileage on the original tyres does not exceed the mileage stated on the guarantee. What should the guarantee mileage be if the company wants no more than 10% of the tyres to be eligible for the discount? Explain briefly some of the advantages of the standard normal distribution. (Q 4 Dec 2001) QUESTION NINE In a particular life insurance office, employees Simiyu, Juki, Waithera and Baraza have a diploma, with Simiyu and Baraza also having a degree. Simiyu, Macharia, Waithera, Thuo, Mwanzia and Kungu are associate members of the Chartered Insurance Institute (ACII) with Thuo and Mwanzia having a diploma. Required: Identifying set A as those employees with diploma, set C as those employees who are ACII and set D as having a degree: Specify the elements of sets A, C and D. Draw a Venn diagram representing sets A, C and D, together with their known elements. What special relationship exists between sets A and D? Specify the elements of the following sets and for each set, state in words what information is being conveyed:  EMBED Equation.3 . What will be a suitable universal set for this situation? The purchasing department has analysed the number of orders placed by each of the 5 departments in the company by type of this financial year as given in the table below: Order typeSalesPurchaseProductionAccountsMaintenanceTotalConsumables 10 12 4 8 4 38Equipment 1 3 9 1 1 15Special 0 0 4 1 2 7Total 11 15 17 10 7 60 An error has been found in one of these orders. Required: Determine the probability that the incorrect order was not for consumables. Determine the probability that the incorrect order came from maintenance or production. Calculate the probability that the incorrect order was an equipment order from purchase. Under what conditions does P (A/B) = P (A)? What is the addition rule of probability and for what type of events is it valid? What is Bayes Theorem? (Q 3 June 2001) QUESTION TEN In each of the following three situations, use binomial, poisson, or normal distribution depending on which is the most appropriate. In each case, explain why you selected the distribution and draw attention to any feature which supports or casts doubt on the choice of distribution. Situation 1: The lifetimes of a certain type of electrical components are distributed with a mean of 800 hours and standard deviation of 160 hours. Required: Identify situation 1. If the manufacturer replaces all the components that fail before the guaranteed minimum lifetime of 600 hours, what percentage of the components have to be replaced? If the manufacturer wishes to replace only 1% of the components that have the shortest life, what value should be used as the guaranteed lifetime? What is the probability that the mean lifetime of a sample of 25 of these electrical components exceeds 850 hours? Situation 2: A green grocer buys peaches in large consignments directly from wholesaler. In view of the perishable nature of the commodity, the green grocer accepts that 15% of the supplied peaches will usually be unsaleable. As he cannot check all the peaches individually, he selects a single batch of 10 peaches on which to base his decision of whether to purchase a large consignment or not. If no more than two of these peaches are unsatisfactory, the green grocer purchases the consignment. Required: Identify situation 2. Determine the probability that under normal supply conditions, the consignment is purchased. Situation 3: Vehicles pass a certain point on a busy single-lane road at an average rate of two per 10 second interval. Required: Identify situation 3. Determine the probability that more than three cars pass this point during a 20 second interval. (Q 3 July 2000 Pilot Paper) QUESTION ELEVEN Define probability as used in Quantitative Techniques. What is Bayes Theorem? Explain how Bayes Theorem can be utilized practically. KK accounting firm has noticed that of the companies it audits, 85% show no inventory shortages, 10% show small inventory shortages and 5% show large inventory shortages. KK firm has devised a new accounting test for which it believes the following probabilities hold: P (Company will pass test/no shortage) = 0.90 P (Company will pass test/small shortage) = 0.50 P (Company will pass test/large shortage) = 0.20 Required: Determine the probability if a company being audited fails this test has large or small inventory shortage. If a company being audited passes this test, what is the probability of no inventory shortage? (Q 7 June 2000) SAMPLING & ESTIMATION QUESTION ONE Suppose it is known that the mean annual income of workers in a certain community is Rs.5,000 with a standard deviation of Rs.1,200. A researcher suspects the son of soil workers have higher than the average income. He draws a random sample of 144 local workers and obtains the sample mean of Rs.5,500. Can he say the local workers have significantly higher income than the total population? (Use  EMBED Equation.3 ). QUESTION TWO The sales Manager of a large sales force is interested in finding whether or not average number of weekly sales contacts per sales representative (15) has changed. The sample information was collected and it was found that 200 sales representatives had an average of 16.5 contacts per week with a standard deviation of 3.5. At 5 per cent level of significance, what should the sales manager conclude? QUESTION THREE Two different models are available for the same machine. The production statistics (number of units produced per hour) of these two models are given below. The data was collected on different days. Model A: 180, 176, 184, 181, 190, 137, Model B: 195, 194, 190, 192, 187, 185, 187, Will you conclude that Model A and Model B have the same productivity? QUESTION FOUR The General Manager of a large hotel in Bombay wishes to know whether or not the personal services provided to its customers are uniform throughout the hotel industry. Whether the clients in the less expensive rooms get the same service as that of clients of more expensive rooms. The data collected from the comment cards is tabulated below: Room Charge  Rs Rs Rs RsRating 50 100 150 200Excellent 10 25 28 28Good 24 30 16 16Average 80 82 21 13Poor 40 41 18 25 Were the customers treated differently according to their room charges? Test the hypothesis at  EMBED Equation.3  level of significance. QUESTION FIVE What is a one-sided confidence interval? When is it necessary? What is wrong with the following hypotheses? Null:  EMBED Equation.3  Alternative:  EMBED Equation.3  > 4.7hours If a Type II error is costly but a Type I error is not, why should you set the level of significance  EMBED Equation.3  at 0.10, 0.20 or even higher? Two researchers in marketing, Catherine Mbugua and George Otieno, reviewed the theory of product life cycle with respect to a specific product. They conducted a research project to determine the applicability of the theory to popular music records. As part of the study, they collected extensive information on a sample of 12 music records judged on the basis of certain well-defined criteria to be successes and a sample of 10 music records to be failures. They collected data on each music record for a period of 16 weeks from the date the record was released to the market. One item of information they collected on each music record was radio airplay. Measurement of this variable yielded the following means and standard deviations for the two samples of music records.  EMBED Equation.3  Sd Successes 16 2.32 Failures 4 2.18 Required: Can one conclude on the basis of these data that successful and unsuccessful (failures) differ with respect to mean amount of airplay? Let  EMBED Equation.3  Note: Test statistic is distributed as students t and is given by:  EMBED Equation.3  Where  EMBED Equation.3 = mean success  EMBED Equation.3 = mean failures  EMBED Equation.3 = common population variance  EMBED Equation.3  Sds = Standard deviation success. And Sdf = Standard deviation failures. What assumptions are we making in order to use the pooled estimate of the common population variance? Q 4 June 1999) QUESTION SIX Prior to an advertising campaign, 35 per cent of a sample of 400 housewives used a certain product. After the campaign, 40 per cent of a second sample of 400 housewives used the same product. Required: Did the campaign increase sales? A manager is convinced that a new type of machine does not affect production at the companys major shop floor. In order to test this, 12 samples of this weeks hourly output is taken and the average production per hour is measured as 1158 with a standard deviation of 71. The output per hour averaged 1196 before the machine was introduced. Required: Test the managers conviction i) Give some examples of the type of data that form a normal distribution. ii) Under what conditions can the normal distribution be used as an approximation to the binomial distribution? iii) What do confidence limits measure? (Q 4 June 2001) QUESTION SEVEN Mia Shillings Department Stores is planning to open a store in Westlands. It has asked the Superior Marketing Company (SMC) to do a market study of randomly selected families within a five-kilometer radius of the store. Among the questions it wishes SMC to ask each home owner are: family income, family size, distance from home to the store site, and whether the family owns a dog or a cat. Required: For each of the four questions, develop a random variable of interest to Mia Shillings Department Stores. Denote which of these are discrete and which are continuous random variables. Mia Shillings Department Stores has compiled the following data concerning its daily sales. The sales of each day of the week are normally distributed with the following parameters: Day Mean ( EMBED Equation.3 ) in Sh.Standard deviations ( EMBED Equation.3 ) in Sh.Monday 120,000 20,000Tuesday 100,000 25,000Wednesday 100,000 10,000Thursday 120,000 40,000Friday 140,000 20,000Saturday 160,000 50,000 Required: Which day of the week has the lowest probability that the store will sell between Sh.110,000 and Sh.150,000 worth of goods? Because of economic conditions, a firm reports that 30 per cent of its accounts receivable from other business firms are overdue. The firms policy is that if an accountant takes a random sample of five such accounts and finds that exactly 20 per cent of the accounts are overdue 10 per cent of the time, then a warning letter should be written to the particular firm. Required: Should the warning letter be written? (Q 3 Dec 2000) ANALYSIS & TIME SERIES ANALYSIS QUESTION ONE Unlisted plc hopes to achieve a Stock Market quotation for its shares. A profit forecast is necessary and, in order to achieve such a forecast, the company has experimented with a number of approaches. The following are details from a linear regression on the last 11 years profit figures: x = years (expressed 1to 11) y = annual profit figures  EMBED Equation.3  = 66  EMBED Equation.3 = 212.10  EMBED Equation.3 = 506  EMBED Equation.3 = 1,406.70  EMBED Equation.3 = 4,254.08  EMBED Equation.3  where  EMBED Equation.3  represents profit values estimated by the regression line. The following formulae are given: Standard error of the regression line  EMBED Equation.3  Coefficient of correlation (r) =  EMBED Equation.3  You are required: To obtain the simple least squares regression line of Y on X; To use the line to estimate profit in each of the next two years; To calculate the coefficient of determination for the line and to explain its meaning; To calculate the standard error of the regression line and to use this to obtain the 95% confidence interval for the line; On the basis of the information given on your answer (a) to (d) to determine whether it is likely that the regression line will be a good estimator of profit. QUESTION TWO The following data have been collected relating to returns which would have been earned from an investment of an equal sum of money in the shares of E.T. plc and a group of market shares: Year Returns on E.T. plc shares (y) ( ) Returns of market shares (x) ( ) 1 2 3 4 5 6 7 8 9 10 7.8 11.0 15.2 23.1 29.7 37.4 44.6 52.8 60.2 63.9 11.1 12.3 18.5 25.4 28.7 33.8 37.7 39.6 44.7 45.5  EMBED Equation.3  The standard error of the regression coefficient is:  EMBED Equation.3  and where the standard error of the regression line is:  EMBED Equation.3  You are required to: Plot the original data on a graph, calculate the least squares regression of y on x and draw the regression line on the graph; Test the significance of the slope of the line at the 5% level; Discuss the outcomes of (a) and (b) in the context of a comparison of the two investments QUESTION THREE The following regression equation was calculated for class of 24 CPA II students. -  Standard error (0.0190) (0.034) (0.018) Where y=students score on a theory examination x1 = Students rank (from the bottom) in high school x2 = Students verbal aptitude score x3 =A measure of students character Required: Calculate the t ratio and the 95% confidence interval for each regression coefficient. What assumptions did you make in (a) above? How reasonable are they? Which regressor gives the strongest evidence of being statistically discernible? In writing up a final regression equation, should one keep the first regressor in the equation, or drop if? Why? (Q 5 June 2002) QUESTION FOUR Does finding a no linear relationship between two variables mean no relationship? Does a high correlation mean that one variable causes another variable to vary? In recent years environmentalists and health professionals have been concerned about the ill effects on the environment of the widespread use of insecticides. If human beings are to cope with the problem and make decisions about how to deal with it, they must understand the effect of insecticides on humans and other animals. In the kind of study often done to promote such understanding, two professors at University of Nairobi investigated the effect of a commonly used insecticide on sheep. Among other statistical analyses, they derived the following linear regression equation (n = 16):  EMBED Equation.3  This equation describes the relationship between the activity of a certain enzyme in the sheeps brain (Y) and the time (in hours) after the sheep has been exposed to the insecticide (X). Required: Suppose 30 hours have elapsed since a sheep has been exposed to the insecticide, what is the predicted value of Y? How would you describe the relationship between the two variables? The professors computed a coefficient of determination (r2) of 0.86 from the data. What conclusion can be drawn about the true relationship between the two variables? Let  EMBED Equation.3  What assumption are necessary to solve part (iii) above? (Q 5 June 1999) QUESTION FIVE Kenya Graduate School (KGS) offers a variety of graduate courses. However, its main emphasis has been on information science (IS) courses. Due to the laboratory equipment requirements for IS courses, KGS has to estimate in advance the expected students enrolments. Over the last 5 years, the students enrolments, by quarter, has been: Years Quarter 1991 1992 1993 1994 1995First 30 32 41 45 73Second 42 107 93 101 181Third 100 71 139 151 227Fourth 66 47 62 67 109 Required: Determine the estimates, by quarter, for year 1996. Justify the method you use. If linear multiple regression were to be used in order to determine the predicting equation, what other variables would be included? How would the expected enrolments be compared to the actual enrolments? Note:  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  (Q 4 Dec 1995) QUESTION SIX Briefly but clearly, explain the difference, if any, between regression analysis and correlation analysis. As a tax consultant to the Government of your country, you have been asked to estimate an empirical model on the demand for income tax evasion (ITE) in the country. You think true income (TI), Marginal tax rate (MTR), penalty rate (PR) and probability of detection (PROB) will be important variables. Using national time-series annual data from 1967 to 1995, you estimate the following regression equation: ITEt = - 52.59 + 33.44 log TIt + 0.93MTRt 0.20PRt 1.48PROBt (- 6.85) (13.97) (6.35) (- 2.93) (- 3.90) The calculated t statistics are reported in parentheses, and figures are in shillings. Required: Write down the population theoretical empirical model on the demand for income tax evasion (ITE). Provide a theoretical justification of the empirical model specification in (i) above, that is, expected signs of the regression coefficients and why. Interpret the constant term (-52.59), coefficient for true income (33.44) and coefficient of probability of detection (-1.48) in the context of the problem. Before collecting data, the principal tax collector and her staff believed that penalty rate had a negative influence on income tax evasion and should therefore be used as leverage on those who evade tax. From the regression results, should this be the case? Why? (Note: critical t-value = - 1.701 at 0.05 level of significance) (Q 5 June 1996) QUESTION SEVEN Find the moving average of the time series of quarterly production (in tons) of coffee in an Indian State as given below. After that, come up with a trend line to approximate the production in future. Production (in Tons) Year Quarter IQuarter IIQuarter IIIQuarter IV1983 - - 12 161984 5 1 10 171985 7 1 10 161986 9 3 8 181987 5 2 15 5 QUESTION EIGHT The number of plumbing repair jobs by Manji Plumbing Service in each of the last nine months in Nakuru town are listed below: Month Jobs March 353 April 387 May 342 June 374 July 396 August 409 September 399 October 412 November 408 Required: Forecast the number of repair jobs Manji Plumbing Service will perform in December. Use the least squares method. Note: Slope =  EMBED Equation.3  And Intercept = Y b1t What is your forecast for December using a three-period weighted moving average with weights of 0.6, 0.3, and 0.1. How does it compare with your forecast from part (i) above? What are the aims of time series analysis? Describe what a season is in the context of a time series and give some examples. Describe the stages in obtaining a time series trend using the method of semi averages. Why must forecasts be treated with caution? (Q 5 June 2001) LINEAR PROGRAMMING QUESTION ONE Solve the following problem using the simplex method: Maximize z = 7x1 + 5x2 Subject to 5x1 + 3x2 d" 50 (1) 4x1  2x2 d" 30 (2) x1, x2 e" 0. Verify your solution by solving graphically and relate tableaux to specific corner points. Determine the shadow prices and interpret their meaning. QUESTION TWO A company wishes to purchase additional machinery in a capital expansion program. Three types of machines are to be purchased: A, B, and C. Machine A costs $25,000 and requires 200 square feet of floor space for its operation. Machine B costs $30,000 and requires 250 square feet of floor space. Machine C costs $22,000 and requires 175 square feet of floor space. The total budget for this expansion program is $350,000. The maximum available floor space for the new machines is 4,000 square feet. The company also wishes to purchase at least one of each machine. Given that machines A, B, and C can produce 250, 260, and 225 pieces per day, the company wants to determine how many machines of each type it should purchase so as to maximize daily output (in units) from the new machines. Explicitly define your decision variables and formulate the LP model. Assess the validity of the four underlying LP assumptions for this problem. Solve and analyse the problem using a computer package QUESTION THREE A pension fund wishes to invest in one or more of six possible investments. Financial analysts have estimated the present value of effective annual estimate. The data in this table indicate that the present value of investing $10,000 in alternative 1 is the sum of $1,200 (0.12($10,000) for year 1, $1,000 (0.10(10,000) for year 2, and $800 (0.08($10,0000 for year 3, for a total present value of $3,000. Effective Annual Rate of returnInvestmentYear 1Year 2Year 31 0.12 0.10 0.082 0.14 0.10 0.103 0.15 0.12 0.084 0.10 0.12 0.155 0.08 0.12 0.186 0.25 0.15 0.05 Management has decided that $300,000 will be invested. At least $50,000 is to be invested in alternative 2 and no more than $40,000 in alternative 5. Total investment in alternative 4 and 6 should not exceed $75,000, as these are risky investments. If the objective is to maximize the present value of total dollar return for the three-year period, formulate the LP model for how much capital to invest in each alternative. Can you comment on how relevant each underlying LP assumption is to this problem? QUESTION FOUR An endowment fund manager is attempting to determine a best investment portfolio and is considering six alternative investments. The following table indicates point estimates for the price per share, the annual growth rate in the price per share, the annual dividend per share, and a measure of the risk associated with each investment. Portfolio dataAlternative 1 2 3 4 5 6Current price per share$80$100$160$120$150$200Projected annual growth rate 0.08 0.07 0.10 0.12 0.09 0.15Projected annual dividend per share $4.00 $6.50 $1.00 $0.50 $2.75 0Projected risk 0.05 0.03 0.10 0.20 0.06 0.08 In this case risk is defined as the standard deviation in return. Dollar return per share of stock is defined as price per share one year hence less price per share plus dividend per share. The fund has $2.5 million to invest, and it wishes to satisfy the following conditions: The maximum dollar amount to be invested in alternative 6 is $250,000 No more than $500,000 should be invested in alternatives 1 and 2, combined Total weighted risk should be no greater than 0.10, where  EMBED Equation.3  For the sake of diversity, at least 100 shares of each stock should be purchased At least 10 percent of the total investment should be in alternatives 1 and 2 combined Dividend for the year should be at least $10,000 If the objective is to maximize total dollar return (from both growth and dividends), formulate the LP model for determining the optimal number of shares to purchase of each investment alternative. (Assume that this is a one-year model and that fractional shares of securities may be purchased) QUESTION FIVE M & K Contractor pays his subcontractors a fixed fee plus mileage or travelling expenses for work per formed. On a given day, the contractor is faced with three electrical jobs associated with various projects (A, B and C). M & K contractor has four electrical subcontractors (Wesside, Federal, National and Universal) who are located at various places throughout the area. Given below are the distances in kilometres) between the subcontractors and the projects. Projects A B C km km km Wesside 50 36 16 Subcontractors Federal 28 30 18 Naitonal 35 32 20 Universal 25 25 14 Required: Represent the above problem in a network. Determine how subcontractors should be assigned in order to minimise the total costs. Explain the assumptions of the quantitative techniques you have used to solve part (a) above. (Q6 Dec. 1999) QUESTION SIX Regal Investments has just received instructions from a client to invest in two shares; one an airline share, the other an insurance share. The total maximum appreciation in share value over the next year is to be maximized subject to the following restrictions: the total investment shall not exceed Sh.100,000 at most Sh.40,000 is to be invested in the insurance shares quarterly dividends must total at least Sh.2,600 The airline share is currently selling for Sh.40 per share and its quarterly dividend is Sh.1per share. The insurance share is currently selling for Sh.50 per share and the quarterly dividend is Sh.1.50 per share. Regals analysts predict that over the next year, the value of the airline share will increase by Sh.2 per share and the value of the insurance share will increase by Sh.3 per share. A computer software provided the following part solution output: Objective Function Value = 5,400 Variable Number Reduced cost Airline shares 1,500 0.000 Insurance shares 800 0.000 Constraint Slack/Surplus Dual prices Total investment 0.000 0.050 Investment in insurance 0.000 0.010 Dividends 100.000 0.000 Objective Coefficient Ranges Variable Lower limit Current value Upper limit Airline share 2.500 3.000 No upper limit Insurance share 0.000 2.000 2.400 Right-hand Side Ranges Constraint Lower limit Current value Upper limit Total investment 96,000.00 100,000 No upper limit Investment in insurance 20,000.00 40,000 100,000.00 Dividends No lower limit 2,600 2,700.00 Required: Formulate the above problem. Explain what reduced cost and dual prices columns above mean. How should the clients money be invested to satisfy the restrictions? Suppose Regals estimate of the airline shares appreciation is an error, within what limits must the actual appreciation lie for the answer in (c) above to remain optimal? (Q 6 Dec 2001) QUESTION SEVEN A baker makes two products; large loaves and small round loaves. He can sell up to 280 of the large loaves and up to 400 small round loaves per day. Each large loaf occupies 0.01m3 of shelf space, each small loaf occupies 0.008m3 of space, and there is 4m3 of shelf space available. There are 8 hours available each night for baking, and he can produce large loaves at the rate of 40 per hour, and small loaves at the rate of 80 per hour. The profit on each large loaf is Sh.5.00 and Sh.3.00 profit on the small round loaf. Required: In order to maximize profits, how many large and small round loaves should he produce? Summarize the procedure for solving the kind of quantitative technique you have used to solve part (a) above. (Q 6 June 2001) QUESTION EIGHT A small company will be introducing a new line of lightweight bicycle frames to be made from special aluminium alloy and steel alloy. The frames will be produced in two models, deluxe and professional. The anticipated unit profits are currently Sh.1,000 for a deluxe frame and Sh.1,500 for a professional frame. The number of kilogrammes of each alloy needed per frame is summarized in the table below. A supplier delivers 100 kilogrammes of the aluminium alloy and 80 kilogrammes of the steel alloy weekly. Aluminium alloy Steel alloy Deluxe 2 3 Professional 4 2 Required: Determine the optimal weekly production schedule. Within what limits must the unit profits lie for each of the frames for this solution to remain optimal? Explain the limitations of the technique you have used to solve part (a) above. (Q 6 Dec 2000) QUESTION NINE Define the following terms as used in linear programming: Feasible solution Transportation problem Assignment problem The TamuTamu products company ltd is considering an expansion into five new sales districts. The company has been able to hire four new experienced salespersons. Upon analysing the new salespersons past experience in combination with a personality test which was given to them, the company assigned a rating to each of the salespersons for each of the districts .These ratings are as follows:  Districts Salespersons12345A9290949183B8488968281C9090938693D7894898488 The company knows that with four salespersons, only four of the five potential districts can be covered. Required: The four districts that the salespersons should be assigned to in order to maximize the total of the ratings Maximum total rating. (Q 6 June 2002) QUESTION TEN Explain the value of sensitivity analysis in linear programming problems and show how dual values are useful in identifying the price worth paying to relax constraints. J.A Computers is a small manufacturer of personal computers. It concentrates on production of three models- a Desktop 386, a Desktop 286, and a Laptop 486, each containing one CPU Chip. Due to its limited assembly facilities JA Computers are unable to produce more than 500 desktop models or more than 250 Laptop models per month. It has one hundred and twenty 80386 chips (these are used in Desktop-386) and four hundred 80286 chips (used in desktop 286 and Laptop 486) for the month. The Desktop 386 model requires five hours of production time, the Desktop 286 model requires four hours of production time, and the Laptop 486 requires three hours of production time. J.A Computers have 2000 hours of production time available for the coming month. The company estimates that the profit on Desktop 386 is Sh. 5,000. for a desktop 286 the profit is Sh.3,400 and Sh.3,000 profit for a laptop 486. Required: Formulate this problem as a profit maximization problem and mention the basic assumptions that are inherent in such models. An extract of the output from a computer package for this problem is given below: Output solution X1=120, X2=200, X3=200 Dual values Constraints 3 150 Constraints 4 90 Constraints 5 20 Sensitivity analysis of objective function coefficients: VariableLower limitOriginal valueUpper limitX1100250No limitX2150170200X3127.5150170 Sensitivity analysis on R.H.S ranges. ConstraintsLower limitOriginal valueUpper limit1320500No limit2200250No limit3801201304350400412.55195020002180X1=Monthly production level for Desktop 386. X2 =Monthly production level for Desktop 286. X3=Monthly production level for Laptop 486. Required: Interpret the output clearly, including optimum product mix, monthly profit, unused resources and dual values Explain the purpose of upper limits and lower limits for the variables X1,X2,X3 and constraints 1 to 5. Calculate the increase in profit if the company is able to produce a further 10 CPU 80386 chips. (Q 7 July 2000 Pilot paper) QUESTION ELEVEN Explain the following terms as used in the context of linear programming: Linear program Basic feasible solution Degeneracy Alternative optimal solutions Redundant constraints Range of optimality Artificial variables Reduced cost Dummy destination Assignment problem Kenya Canning Transport is to move goods from three factories situated in thika, Naivasha and Machakos. These goods will be distributed to Nairobi, Mombasa and Kisumu. Informaion about the units (in kilograms) to be moved is given below: FactorySupply (Kg)Demand (Kg)DestinationThika200250 NairobiNaivasha100125 MombasaMachakos150125 KisumuNaivasha cannot transport to Mombasa. The costs per Kg. (in shillings) are as follows: NairobiMombasaKisumuSh.Sh.Sh.Thika285Naivasha6129Machakos4510 Required: Draw a network diagram for the above problem Set a linear programming model for this problem NOTE: Do not solve. (Q 5 Dec 1995) QUESTION TWELVE Explain and graphically show the following terms as used in linear programming: Alternate optimal or multiple solutions Unbound solutions Infeasible solutions The Copper Wire Company is a manufacturer of copper wires. Ben Muturi, the company executive director has just received a memo from Stella Musingo, one of his young engineers. Stella informs Ben that; with the forthcoming purchase of new blast furnace to produce copper, it seems an opportune time to completely review their method of producing copper. Currently, a variety of ingredients are entered into the blast furnace in order to produce copper. The production manager, Joe Kiilu makes the decision, as he has for the last 25 years, on the variety of ingredients. It is not an easy decision. The company wants to keep the costs of producing copper down The ingredients into the blast furnace have to interact to produce copper. Furthermore, the chemical reactions have to be such that the copper produced is viscous enough to flow out of the furnace. Stella has identified the problems with the current method. First, Joe is retiring in six months and there is no obvious replacement to him. Second, the company never knows if they are minimizing the production costs. Third, sometimes the furnace does not freeze-up. Stella believes that there is a quantitative technique, which can be used to decide what ingredients to enter in to the furnace. Once the model has been developed, special skills such as Joes will no longer be required. The company will also be confident that they are minimizing costs. In addition, they would be able to eliminate the furnace freeze-up problem Required: What potential quantitative technique can be used to solve this problem? Why? Specify the general formulation of the quantitative technique. (Be sure to clearly spell out the meaning of all the variables or parameters used) If a group were formed to develop an appropriate model, what organizational personnel should be included? What specific skills would each provide? (Q 6 June 1998) QUESTION THIRTEEN Briefly and clearly compare and contrast between a transportation problem and an assignment problem as used in linear programming. Downtown Motors Company (DMC), has hired a market services firm to develop an advertising strategy for promoting Downtowns used car sales. The marketing firm has recommended that Downtown use spot announcements on both television and radio as the advertising media for the proposed promotional campaign. Advertising strategy guidelines are expressed as follows: Use at least 30 announcements for combined television and radio coverage. Do not use more than 25 radio announcements. The number of radio announcements cannot be less than the number of television announcements. The television station has quoted a cost of Sh.1,200 per spot announcement and the radio station has quoted a cost of Sh.300 per spot announcement. Downtowns advertising budget has been set at Sh.25,500. The marketing services firm has rated the various advertising media in terms of audience coverage and recall power of the advertisement. For Downtowns media alternatives, the television announcement is rated at 600 and the radio announcement is rated at 200. Required: Perform an analysis of advertising strategy for DMC. Include a consideration of the following items: Determine the optimal number of television and radio spot announcements. Explain the relative merits of each advertising medium. Determine the rating that would be necessary in order to increase the number of television spots. Which restrictions placed on the advertising strategy would DMC want to consider relaxing or altering? Why? What is the use of any possible increase in the advertising budget? (Q6 Dec 1998) DECISION THEORY QUESTION ONE The following is a payoff table for a particular venture. States of nature12345D1150225180210250DecisionD2180140200160225AlternativesD3220185195190180D4190210230200160 Determine the optimal decision using: Max-min criterion. Max-max criterion. Min-max regret criterion. Maximum expected payoff (assuming equal likelihood of states of nature). QUESTION TWO Assume that Table question 1, is a loss table rather than a payoff table. Determine the optimal decision using: The min-max criterion, The min-min criterion, The min-max regret criterion, and The minimum expected loss criterion (again assuming equal likelihood of states of nature). QUESTION THREE The following table is a payoff table for a particular venture.  States of nature123456D1280300260360400450DecisionD2320420540300280380AlternativeD3200360400440250320D4350260390500380260 The relative likelihood s of occurrence for the states of nature are f (1) = 0.18, f (2) = 0.10, f (3) = 0.16, f (4) = 0.24, f (5) = 0.20, and f (6) = 0.12. Required: Determine the decision alternative that maximizes expected payoff. Determine expected value under certainty. What is the expected value of perfect information? QUESTION FOUR A trust officer for a major banking institution is planning the investment of a $ 1 million family trust for the coming year. The trust officer has identified a portfolio of stocks and another group of bonds that might be selected for investment. The family trust can be invested in stocks or bonds exclusively, or a mix of the two. This trust officer prefers to divide the funds in increments of 10 percent; that is, the family trust may be split 100 percent stocks /0 percent bonds, 90 percent stocks /10 percent bonds, 80 percent stocks /20 percent bonds, and so on. The trust officer has evaluated the relationship between the yields on the different investments and general economic conditions. Her judgment is as follows: If the next year is characterized by solid growth in the economy, bonds will yield 12 percent and stocks 20 percent. If the next year is characterized by inflation, bonds will yield 18 percent and stocks 10 percent. If the next year is characterized by stagnation, bonds will yield 12 percent and stocks 8 percent. Formulate a payoff table where payoffs represent the annual yield, in dollars, associated with the different investment strategies and the occurrence of various economic conditions Determine the optimal investment strategy using the max-max, max-min, Hurwicz ( EMBED Equation.3 ), equally likely, and regret criteria. Suppose that a leading economic forecasting firm projects P(solid growth)= 0.4, P(inflation)=0.25, and P (stagnation)= 0.35. Use the expected value criterion to select the appropriate strategy. What is the expected value with perfect information? QUESTION FIVE An urban cable television company is investigating the installation of cable TV system in urban areas. The engineering department estimates the cost of the system (in present worth Sh.) to be Sh. 7 million. The sales department has investigated four pricing plans. For each pricing plan, the marketing division has estimated the revenue per household in present worth Sh. to be: Plan Revenue per household (Sh.) I 150 II 180 III 200 IV 240 The sales department estimates that the number of household subscribers would be approximately, either 10,000, 20,000, 30,000, 40,000, 50,000 or 60,000. Required: Construct a payoff table for this problem. What would be the companys optimal decision under the optimistic approach and the minimax regret approach. Suppose that the sales department has determined the number of subscribers will be a function of the pricing plan. The probability distributions for the pricing plans are given below. Probability under pricing plan. Number of subscribers I II III IV10,000 0 0.05 0.10 0.2020,000 0.05 0.10 0.20 0.2530,000 0.05 0.20 0.20 0.2540,000 0.40 0.30 0.20 0.1550,000 0.30 0.20 0.20 0.1060,000 0.20 0.15 0.10 0.05 Which pricing plan is optimal? Briefly explain the main difference between the approaches used in part (b) and (c) above. (Q 7 Dec 2001) QUESTION SIX Explain the following terms as used in decision analysis. Decision making under risk versus uncertainty. Decision trees versus probability trees. Minimax versus maximax criterion. Pure strategy versus mixed strategy games. Games with more than two persons versus non zero-sum games (Q 7 Dec 2000) QUESTION SEVEN Define the following terms used in game theory: Dominance. Saddle point Mixed strategy Value of the game Consider the two person zero sum game between players A and B given by the following pay-off table: Player B Strategies A B C D Player A Strategies 1 2 2 3 -1 2 4 3 2 6 Required: Using the maximin and minimax values, is it possible to determine the value of the game? Give reasons. Use graphical methods to determine optimal mixed strategy for player A and determine the value of the game. (Q7 June 2000) NETWORK ANALYSIS QUESTION ONE Consider the simplified scenario for the development of a consumer product through the market test phase shown in the table below. ActivitySymbolPreceding activitiesTime estimated (weeks)Design promotion campaign Initial pricing analysis Product design Promotional costs analysis Manufacture prototype models Product cost analysis Final pricing analysis Market test A B C D E F G H - - - A C E B, D, F G 3 1 5 1 6 1 2 8 Draw the network for this project. Calculate the slacks and interpret their meaning. Determine the critical path and interpret its meaning. Construct a time chart and identify scheduling flexibilities. QUESTION TWO For the product development project in question 1 consider the detailed time estimates given in the following table. Note that time estimates in the preceding exercise are equivalent to modal time estimates in this exercise. Time Estimates (weeks) ActivityOptimisticMost likelyPessimisticA B C D E F G H 1 1 4 1 4 1 1 6 3 1 5 1 6 1 2 8 4 2 9 1 12 2 3 10 Re-label your network in the question 1 to include expected duration  EMBED Equation.3  (in place of activity duration dij and variances ij. Use equations below  EMBED Equation.3  and ij2 EMBED Equation.3    EMBED Equation.3  or Compare slacks to those in question 1. Has the critical path changed? Determine the following probabilities: That the project will be completed in 22 weeks or less. That the project will be completed by its earliest expected completion date. That the project takes more than 30 weeks to complete. QUESTION THREE Consider the cost-time estimates for the product development project of question one above as given in the table below. Time Estimates (weeks)Direct Cost Estimates ($ 000)Activity Normal Crash Normal CrashA B C D E F G H 3 1 5 1 6 1 2 8 1.0 0.5 3.0 0.7 3.0 0.5 1.0 6.0 3.5 1.2 9.0 1.0 20.0 2.2 4.0 100.0 10.0 2.0 18.0 2.0 50.0 3.0 9.0 150.0 Indirect cost is made up of two components: Fixed cost of $5,000 and a variable cost of $1,000 per week of elapsed time. Also, for each week the project exceeds 17 weeks, an opportunity cost of $2,000 per week is assessed. Construct a time chart for the minimum total cost schedule. QUESTION FOUR Consider a project which has been modelled as follows: ActivityImmediate Predecessor (s)Completion Time (hours)A B C D E F G H I J K L - - A A A B, C B, C E, F E, F E, F D, H G, J 7 10 4 30 7 12 15 11 25 6 21 25 Required: Determine the projects expected completion time and its critical path. Can activities E and G be performed at the same time without delaying the completion of the project? Can one person perform activitiesA, G and I without delaying the project? By how much time can activities G and L be delayed without delaying the entire project? By how much time would the project be delayed if activity G were delayed by 3 hours and activity L by 4 hours? Explain. (Q 8 Dec 2001) QUESTION FIVE Explain the following terms used in network analysis: Network planning; Activities; Events; Critical path; Float. Consider the following project network and activity times (in weeks).  Activity Activity Times (weeks)A B C D E F G H 5 3 7 6 7 3 10 8 Required: How long will it take to finish this project? Can activity D be delayed without delaying the entire project? If so, by how many weeks? What is the schedule for activity E? (Q 8 June 2001) QUESTION SIX A small construction project involves the following activities: Normal Crash Activity Time (days) Cost (Sh.) Time (days) Cost (Sh.)1.2 1.3 2.4 3.4 3.5 4.5Clear ground A Lay foundation B Build walls C Roofing and piping D Painting E Landscaping F 6 5 3 7 4 2 60,000 30,000 10,000 40,000 20,000 10,000 5 3 2 4 3 1 70,000 50,000 15,000 55,000 30,000 17,500 Required: Determine the shortest time and associated cost to finish this project. If a penalty of Sh.4,500 must be charged for every day beyond 12 days, what is the most economical time for completing the project? Explain the four different methods or approaches for organizing and displaying project information. (Q 8 Dec 2000) QUESTION SEVEN State four attributes of the Beta distribution which made it to be chosen as representing the distribution of activity times in project evaluation and review technique (PERT). Lillian Wambugu is the project manager of Jokete Construction Company. The company is bidding a contract to install telephone lines in a small town. It has identified the following activities along with their predecessor restrictions, expected times and worker requirements: Activity Predecessors Duration Weeks Crew Size WorkersA B C D E F G H - - A A B B D, E F, G 4 7 3 3 2 2 2 3 4 2 2 4 6 3 3 4 Lillian Wambugu has agreed with the client that the project should be completed in the shortest duration. Required: Draw a network for the project. Determine the critical path and the shortest project duration. Lillian Wambugu will assign a fixed number of workers to the project for its entire duration and so she would like to ensure that the minimum number of workers is assigned and that the project will be completed in 14 weeks. Draw a schedule showing how the project will be completed in 14 weeks Comment on the schedule you have drawn in (iii) above. (Q 8 June 2002) Answers - Past Papers  TOPIC 1 QUESTION 1 When ps = 5, P is as follows: P5 = 50000 ( 5 6250 ( 52 = Sh.93750 When ps = 6, P is P6 = 50000 ( 6 6250 ( 62 = Sh.75000 When ps = 4, P is P4 = 50000 ( 4 6250 ( 42 = Sh.100000 Since P4 ( P5 ( P6 where Pp is Profit at a given price p, then the function is decreasing at ps = 5 When ps = 4.5, P = 50000 ( 4.5 6250 ( 4.52 = 98437.5 The profit will increase by 98437.5 93750 = Sh.4687.50 The first differentiation of profit with respect to price equated to zero is as follows:  EMBED Equation.3  = 50000 12500p = 0 ( ps =  EMBED Equation.3  = 4 The second differentiation with respect to price gives  EMBED Equation.3  = - 12500 Since  EMBED Equation.3  ( 0 it means it is a maximization function. QUESTION 2 Let profit = z Profit z = PQ C = (12 0.4Q) Q (5 + 4Q + 0.6Q2) = 12Q 0.4Q2 5 4Q 0.6Q2 = 8Q Q2 5 For maximum profit, the differentiation of z with respect to Q equals zero.  EMBED Equation.3 (2Q = 8( Q = 4 So P = 12 0.4Q and for Q =4 = 12 1.6 = 10.4  EMBED Equation.3 = - 2 Q ( 0 Profit is maximized. Profit is maximised at a price of 10.4 and when quantity = 4 To maximize sales then,  EMBED Equation.3  = 12 0.8Q = 0 ( Q =  EMBED Equation.3 = 15 and since  EMBED Equation.3  then sales is maximized So P = 12 0.4 ( 15 = 6 QUESTION 3 Taking the following to mean: TC Total cost AC Average cost MC Marginal cost Q Number of units Then AC =  EMBED Equation.3  And MC =  EMBED Equation.3  These are the relationships that link TC, AC, and MC. To comment on the CPA students analysis, The derivative of AC is as follows,  EMBED Equation.3  Since  EMBED Equation.3  then the students comment is wrong in getting marginal cost. The student is right though in saying that AC = EMBED Equation.3 . Total cost function can be obtained from expression of marginal cost (MC) since,  EMBED Equation.3 Then  EMBED Equation.3  Integrating both sides gives: TC =  EMBED Equation.3  Given MC = Q2 28Q + 211 then TC =  EMBED Equation.3  =  EMBED Equation.3  A is a constant of integration. Given that when Q = 0,TC = Sh 10 million then A = 10 So the total cost function is as follows: TC =  EMBED Equation.3  The total revenue (TR) function can be obtained from Average revenue (AR) function as follows, AR =  EMBED Equation.3  So TR = Q ( AR = Q ( (200 8Q) = 200Q 8Q2 Profit equal to TR TC. Since TR and TC expression have been obtained from (i) and (ii), then profit P is as follows, P = TR TC = 200Q 8Q2 ( EMBED Equation.3 ) = 11Q + 6 Q2 -  EMBED Equation.3  - 10 The level of output that maximizes profit is got by equating the derivative of profit P with respect to Q to zero, as follows  EMBED Equation.3  = 11 + 12Q Q2 = 0 The solution to this quadratic equation is as follows: Q =  EMBED Equation.3  Where a, b, c are the coefficients of the equation as follows: a = - 1, b = 12, and c = 11. So Q =  EMBED Equation.3  So Q =  EMBED Equation.3  or Q =  EMBED Equation.3  Since two points of maximum profit exist, then the Q that gives more profit is the one to be used. At Q = 11, P = 11 ( 11 + 6 ( 112 -  EMBED Equation.3 - 10 = 151.333 million At Q = 1, P = -11 ( 1 + 6 ( 12 -  EMBED Equation.3 - 10 = 15.333 million So the level that maximizes profit is Q = 11. Marginal revenue (MR) can be obtained from Total revenue (TR) as follows:  EMBED Equation.3  TR = 200Q 8Q2 So  EMBED Equation.3  = 200 16Q QUESTION 4 The initial cost of the project is determined when the time is zero. That is when the project is started. Given Profit P = 10x x2 5, then the initial cost of the project is when x = 0. Profit = 10 ( 0 02 5 = - 5 The initial cost is sh. 5000. Equating the profit function to zero and solving the function for the time determines break-even time in months for the project. P = 10x x2 5 = 0 Since this is a quadratic equation, the solution is as follows, x =  EMBED Equation.3  Given a = - 1 b = 10 c = - 5 Then  EMBED Equation.3  or  EMBED Equation.3  =  EMBED Equation.3  or  EMBED Equation.3  = 0.527 or 9.472 months Break-even time is 0.527 and 9.472 months. The best time to end the project is when profit is maximum. This is determined by differentiating the profit function with respect to time and equating to zero as follows:  EMBED Equation.3  ( x = 5 The best time to end the project is after 5 months. To obtain the total profit within the break-even points, the profit function is integrated within those break-even points as follows, Profit = EMBED Equation.3   EMBED Equation.3  =  EMBED Equation.3  = 117.96 (- 1.30) = 119.26 ( 1000 = Sh. 119,260 Question 5 To obtain the time when there are a maximum number of people queuing, the derivative of the equation is equated to zero.  EMBED Equation.3  = 3x2 28x + 50 = 0 This is a quadratic equation with the following solutions, x =  EMBED Equation.3  Given a = 3 b = - 28 c = 50 Then  EMBED Equation.3  or  EMBED Equation.3  = EMBED Equation.3  or  EMBED Equation.3  = 6.92 or 2.41 The number of shoppers queuing at these particular times is as follows, y = 6.923 14 ( (6.92)2 + 50 x 6.92 = 6.96 or y = 2.413 14 ( (2.41)2 + 50 ( 2.41 = 53.18 The management should deploy more cashiers after 2.41 hours, that is at 11.25 am. The number of people queuing at this particular time is 53. The number of man hours spent is equal to Y ( x. To get the man-hours spent per day, the function is integrated within the limits 0 ( x ( 8.5  EMBED Equation.3   EMBED Equation.3  = 839.3 0 = 839.3 man-hours Average cost AC is given by the following expression. AC =  EMBED Equation.3  where TC Total cost TC =  EMBED Equation.3 and given MC = x2 x + 2 Then TC =  EMBED Equation.3  = EMBED Equation.3  where A is a constant of integration. Given the information that when x = 0; TC = 500, then A is determined as follows,  EMBED Equation.3  (A = 500 So the TC function is as follows,  EMBED Equation.3  The AC function then is  EMBED Equation.3  Average revenue AR is given by the following expression AR =  EMBED Equation.3  Given R = - x2 + 5x then AR = - x + 5 Profit maximizing output is obtained by equating the differential of profit to zero and solving for the x values as follows: Profit P = R TC  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  x2 + x 3 = 0 Since this is a quadratic equation, the solutions are obtained as follows: x =  EMBED Equation.3  Given a = - 1 b = - 1 c = 3 Then  EMBED Equation.3  or  EMBED Equation.3  = - 2.3 or 1.3 So x = 1300 calculators. Price to charge = AR = - 1.3 + 5 = sh. 3700 Cost per calculator = AC =  EMBED Equation.3  Question 6 Transition matrix is that which contains the probabilities of moving from any one state to another. Initial probability vector is the vector that contains the current state before transition. Equilibrium state is the state that a system settles on in the long run. Absorbing state is one in which cannot be left once entered. It has a transition probability of unity to itself and of zero to other states. To make the transition matrix, we can make a loss probability table and retention probability table first. Loss Probability table To ATo BTo CTo D Total LossFrom A0000.050.05From B0.2000.10.3From C00.200.150.35From D000.200.2 Retention Probability table Retention=(1 Total loss) A 1 0.05 = 0.95 B 1 0.3 = 0.7 C 1 0.35 = 0.65 D 1 0.2 = 0.8 So the transition matrix will be as follows.  ToABCD FromA0.95000.05B0.20.700.1C00.20.650.15D000.20.8 NOTE: D only looses to C, since whatever it looses due to separation is immediately replenished. i.e. 25% loss is immediately returned by more employment. So the loss is zero. The retentions are placed on the diagonal of transition matrix before the loss probabilities. Notice that summation in rows of transition matrix is equal to one. The matrix can be interchanged to be  FromABCD ToA0.950.200B00.70.20C000.650.2D0.050.10.150.8 In this case the initial vector is post multiplied as follows(Transition matrix EMBED Equation.3  Initial vector To get the initial probability vector after two periods, just multiply the initial vector with the transition matrix twice. Initial vector  EMBED Equation.3  The first period  EMBED Equation.3  Calculations 60(0.95+90(0.2+150(0+200(0=75 60(0+90(0.7+150(0.2+200(0=93 60(0+90(0+150(0.65+200(0.2=137.5 60(0.05+90(0.1+150(0.15+200(0.8=194.5 The second period.  EMBED Equation.3  Calculations 75(0.95+93(0.2+137.5(0+194.5(0=89.85 75(0+93(0.7+137.5(0.2+194.5(0=92.6 75(0+93(0+137.5(0.65+194.5(0.2=128.273 75(0.05+93(0.1+137.5(0.15+194.5(0.8=189.275 Approximately in A ( 89, B ( 92, C ( 128, D ( 189 The equilibrium or steady state is determined from the following matrix and equation as follows:  EMBED Equation.3  (1) and A + B + C + D = 1 (2) From the matrix multiplication (1), the following expressions are determined in terms of A. 0.95 A + 0.2 B = A( 0.05 A = 0.2 B ( B = 0.25 A (3) 0.7 B + 0.2 C = B ( 0.075 A = 0.2 C ( C = 0.375A (4) 0.65 C + 0.2 D = C ( 0.13125 A = 0.2 D ( D = 0.65625A (5) 0.05 A + 0.1 B + 0.15 C + 0.8 D = D (6) From equation (2), (3), (4) and (5), A + 0.25 A + 0.375 A + 0.65625 A = 1 (2.28125 A = 1 So A = 0.4384 B =0.25(A=0.25( 0.4384 = 0.1096 C = 0.375(A=0.375(0.4384 = 0.1644 D =0.65625(A=0.65625(0.4384 = 0.2877 The number of employees in each class at equilibrium is obtained as follows: A = 0.4384 ( 500, B = 0.1096 ( 500, C = 0.644 ( 500, D = 0.2877 ( 500 500 = 200 + 150 + 90 + 60 (the initial state) So at equilibrium  EMBED Equation.3  Question 7 The graph will look as follows  EMBED Excel.Chart.8 \s  From the graph, firm Bs cost function increases more rapidly than firm As even though it starts from a lower value of 50,000. Profit=Revenue-costs Revenue=AR(q=(600-0.5q)(q=600q-0.5q2 Cost is got from the integration of the marginal cost function as follows Cost= EMBED Equation.3  A is a constant of integration and is determined as follows When q=0, cost=1000. Substituting this in the expression of the cost gives A=1500 So cost=140q-4q2+0.05q3+1500 and Profit =600q-0.5q2-(140q-4q2+0.05q3+1500) =460q+3.5q2-0.05q3-1500 To find at what quantity qm profit is maximizes, the expression of profit is differentiated and equated to zero as follows:  EMBED Equation.3  Solving for the values of q  EMBED Equation.3  So qm=83 biro pens Revenue is maximized where:  EMBED Equation.3 which is different from qm=83 where profit is maximized Question 8 Turning point is the point of local minima or maxima for a function. This point has zero slope. Second order derivative condition states that if the first derivative equals zero and the second derivative is defined then the given point is a relative minimum if the second derivative is greater than zero, or maximum if the second derivative is less than zero Partial derivative is the derivative of a multivariate function (function of more than one variable). It is usually with respect to each of the independent variable. Mixed or cross partial derivative is obtained by first getting the derivative of multivariate function with respect to one variable then the second derivative with respect to the second variable. A saddle point is a stationery point that is neither a maximum nor a minimum. Here the difference between the product of pure second partial derivative (second derivative of a function with respect to one variable) and square of mixed partial derivative is less than zero Total revenue = Px = (100-0.01x)(x = 100x-0.01x2 Profit = Revenue-cost = 100x-0.01x2-50x-30000 = 50x-0.01x2-30000  EMBED Equation.3 Chicken wings To maximize revenue  EMBED Equation.3  So profit when revenue is maximized is Profit=50(5000-0.01((5000)2-30000=-30,000 Maximum profit =50(2500-0.01((2500)2-30000 125000-0.01(6250000-30000=32,500 So the difference in profit is 32,500-(-30000)=62,500 Question 9 The Leontief open model is Mx+d=x So rearranging the equation (I-M)x=d x=(I-M)-1d Where M-matrix of technical coefficients x-required production d-the external demand I-Identity matrix Given that M= EMBED Equation.3  d= EMBED Equation.3 Then (I-M)= EMBED Equation.3   EMBED Equation.3  Determinant (I-M) = EMBED Equation.3  = 0.21 - 0.004 - 0.009 = 0.197 Ad joint (I-M)=Transpose of the co-factors of (I-M) Co-factors of (I-M)=  EMBED Equation.3  So adjoint (I-M)=  EMBED Equation.3  And EMBED Equation.3  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3 the production vector will be  EMBED Equation.3   EMBED Equation.3  TOPIC 2 Question 1 Sector WeightJuly 2001 Index (1994 = 100)WSummationPWP SummationMining and quarrying41413611480114801Manufacturing:Food, drink and tobacco771068162Chemicals661097194Metal47723384Engineering2988625628Textiles67704690Other manufacturing1429112922Total of manufacturing6976976198061980Construction182182841528815288Gas, electricity and water808011592009200Totals1000101269 Index of industrial production is  EMBED Equation.3  Index of manufacturing industries is  EMBED Equation.3  The index of manufacturing industries is lower than when it is combines with the other industries. Index of industrial production shows that there was a slight increase of 1.1% from the base year 1994 while manufacturing industries shows that there was a decrease of 11.1% from the base year 1994 Uses of index numbers includes the following Determination of cost of living Determination of income variation with years Changes in stock prices Relationship between industries and time Countries economies can be compared easily Acts as a control measure Limitation of index numbers include: There is usually a problem of choice of sample. This means it may not show a good representative The exact effect is usually clouded by the general index Choice of the best index to represent a given data is usually complicated Question 2 The following table will assist in determination of mean, standard deviation, mode, median. mid-pointPercentageCumulative  EMBED Equation.3 Income per monthx/1000ffrequencyfx19999.5-29999.524.999555125.0013462.8629999.5-34999.532.49952765.003940.9434999.5-35999.535.4995310106.505139.4035999.5-39999.537.99950100.000.0039999.5-44999.542.4995515212.505913.3644999.5-49999.547.49951025475.008637.7249999.5-59999.554.99951540824.997187.5859999.5-69999.564.999518581169.992544.7069999.5-99999.584.999521791784.991381.2199999.5-149999.5124.999517962124.9939347.73149999.5-249999.5199.99954100800.0060624.291007688.95148179.79Mean EMBED Equation.3  Median determination Median Frequency: Q2 frequency= EMBED Equation.3 So Q2 class interval is 59999.5-69999.5 with its frequency being 18. This is read from cumulative frequency column. The interval and frequency can then be read. Median= EMBED Equation.3 Shillings Where  EMBED Equation.3  - lower boundary of Q2 class  EMBED Equation.3  - Q2 frequency  EMBED Equation.3  - Frequency of class after Q2 class f - Cumulative frequency at the start of Q2 class  EMBED Equation.3  - class interval of the Q2 class Mode determination The highest frequency is 21 so modal class is 69999.5-99999.5 Mode= EMBED Equation.3 Shillings Where  EMBED Equation.3  - lower boundary of modal class  EMBED Equation.3  - Frequency of modal class  EMBED Equation.3  - Frequency of class just before modal class f2 - Frequency of class just after modal class  EMBED Equation.3  - Interval of the modal class Standard deviation  EMBED Equation.3  Shillings The mean, median and mode show that the mortgages are mostly taken by high income earners. The standard deviation shows that there is a wide variation in income in relation to people taking mortgages. Descriptive statistics deals with the way raw data is transformed into useful information. For example debt collection from debtors can be studied for a given year. The age debtors report will indicate the number of debtors for a given time. This can be used to approximate the provision for doubtful debts. Note: This is a typical real life problem where there are unequal intervals and open ended frequency distribution Notice the way to create continuity by including the last digit of 0.5 The open ended frequencies at the start and end of the distribution is covered up by having two times the class interval just ahead or just preceding Question 3 The data ensuring continuity and to assist in calculation of the mean is as follows No. of component EMBED Equation.3 (Diameter-0.97) ( 10000xffx46.5-49.548296522.549.5-52.55163061039.752.5-55.5548432826.555.5-58.55715855769.858.5-61.560422520728.261.5-64.56368428492.164.5-67.566493234165.267.5-70.569251725584.770.5-73.5721812961105.373.5-76.575129001409.076.5-79.5784312765.779.5-82.581181283.5250160418292.3 Mean  EMBED Equation.3  Standard deviation EMBED Equation.3  H0: (=0.97642 customer is getting reasonable value H1: ((0.97642 customer is not getting reasonable value The test statistic is  EMBED Equation.3  From normal distribution tables z at 5% significance level (two tiled test) is 1.96. Since calculated z is less than z5% then H0 is accepted. That means the customer is getting reasonable value when the diameter is indicated to be 0.97642. When relative importance of different items is different. It is usually used where intervals are involved To estimate the median of a grouped frequency distribution graphically: Find cumulative frequency at midpoints of class interval. Draw the curve of cumulative frequency as the midpoints ogive. Get the middle part of frequency by dividing total frequency by 2. Draw horizontal line from this point to ogive, then a perpendicular line to cross x-axis, where it crosses the x-axis is the median. The mode represents the most typical value of a distribution and it should coincide with an existing item. The mode is not affected by the presence of extremely large or small items. It is not used extensively because of the following limitations: It is often not clearly defined Exact location is often uncertain It is unsuitable for further algebraic treatment It does not take into account extreme values The mean is the average of values being looked at. The standard deviation is the square root of the average of square of deviations from the mean. In that case standard deviation cannot be obtained if the mean is not obtained first.  EMBED Equation.3  Standard deviation  EMBED Equation.3  Mean Note: Notice the way to create continuity 0.5 is reduced from the lower class limit and added to the upper limit. Furthermore, notice the way the factor (i) 10-4 is removed and the assumed mean amount 0.97 is also removed. These are put back at the end in calculation of the mean. Question 4 Quantity indices relate quantities in a given period to the quantity in another period (base period). Base year is the starting of a period to measure a given variable that changes with time. Chain index is where a given variable in one period is related to the same variable in the previous period. Retail price index is the index that relates prices of commodities from one period to another. Difference of 1999 salary from 1995 as: Stated terms EMBED Equation.3  Constant EMBED Equation.3  There is a difference as shown hereby. There is a difference in percentage increase. In terms of stated shillings there is an increase of 11.1% while in terms of constant shillings, there is a decrease of 2 % Although the rest of the years changes were not required, it has been shown here for clear understanding Year Salary CPI%age change in terms of current shillingsConstant salary%age change in terms of current shillings1995 360,000 130.7 - 275,440-1996 370,000 136.2 102.8 271,65998.61997 390,000 140.3 108.3 277,976100.91998 395,000 144.5 101.3 273,35699.21999 400,000 148.2 101.3 269,90598Constant salary (or deflated salary)  EMBED Equation.3  For example for 1998 deflated salary  EMBED Equation.3  Question 5 Month Price3 monthWeightedExponential3 month(=0.35Dec-00 40---Jan-01 38---Feb-01 39--39.300Mar-01 4139.00038.80039.195Apr-01 3639.33339.60039.827May-01 4138.66738.60038.487Jun-01 3439.33339.00039.367Jul-01 3737.00037.20037.488Aug-01 3537.33336.60037.317Sep-01 3735.33335.60036.506Oct-01 4036.33336.20036.679Nov-01 4137.33337.80037.841Dec-0139.33339.80038.947 March 2001 moving average  EMBED Equation.3  April 2001 moving average  EMBED Equation.3  So December 2001 will be  EMBED Equation.3  March 2001  EMBED Equation.3  April 2001  EMBED Equation.3  So December 2001 = EMBED Equation.3  Note: This table is not necessary other than for the exponential smoothing method alone. The moving average and weighted moving average methods just need the last three months to forecast for December 2001. Latest forecast = Previous forecast +  EMBED Equation.3 (latest observation Previous forecast). e.g Feb = 40 + 0.35(39 40) = 39.3 March = 39.3 + 0.35(39 39.3) = 39.195 Note: That the January forecast was taken to be December 2000 sales since there was no previous month. The forecast for December is obtained from the table as 38.947 The method I prefer is the exponential smoothing. This is because it incorporates all the data without cut off (like moving average), Greater weight is given to more recent data. TOPIC 3 Question 1 The product of the probabilities of each manager solving a problem gives probability of solving a problem. (since one manager solving a problem is independent of the others) P (solving)= EMBED Equation.3  Question 2 The best way to solve this is by use of a probability tree as follows: Let G be the event of a girl being chosen And B be the event of a boy being chosen  Sum of the required probabilities gives the following.  EMBED Equation.3  Question 3 Let A be the age group 30-40 B be the earnings more than 1500 Then P (B/A)= EMBED Equation.3  Then the probability of B given A Where: P(AB) - Probability of A and B occurring. P(A) - Probability of A occurring. Let A be the age group below 50 years B be the earnings varying between 2000-2500 Then P(B/A)= EMBED Equation.3  Question 4 Let A be the event drilling a well and B be the computer analysis showing an economic well. Then  EMBED Equation.3 . But then since computer analysis and drilling of economic well are independent then  EMBED Equation.3 . So that  EMBED Equation.3  Question 5 Let G-firms bid awarded H-competitor submitting a bid  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  Where  EMBED Equation.3 - event that competitor does not submit a bid  EMBED Equation.3  Question 6 Let E, F, G, H- be items from plant 1,2,3,4 respectively and D- be defective item  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  = 0.1125  EMBED Equation.3   EMBED Equation.3  So  EMBED Equation.3  Question 7 Statistical independence of two events means that the probability of first event occurring given the second event has occurred is equal to the probability of second event occurring given the first event has occurred. That is, occurrence of one event does not affect the occurrence of the other event. Given two events A and B,  EMBED Equation.3  for independent events. Let A be -the event of the employee having accident in a particular year I be - the event of the employee being instructed in a particular year The probability of employee being accident free given that he had no safety instructions is given by the following expression  EMBED Equation.3  Given; P(A)= 0.02 - probability of employees having a minor accident in a given year P(I/A)= 0.3 - probability of employees having had safety instructions given they had accidents in a given year.  EMBED Equation.3 - probability of employees not having safety instructions in a given year Then  EMBED Equation.3 =1-(P(A) +P(I)- P(AI)) The shaded area shown here.  And P(AI)= P(I/A) P(A)=0.3(0.02=0.006 P(I) = EMBED Equation.3  So  EMBED Equation.3 =1-(0.02+0.2-0.006)=0.786 and  EMBED Equation.3  Probability of employee being accident free given that he had safety instructions is given by the following expression  EMBED Equation.3   EMBED Equation.3 =(P(I) - P(AI)) The shaded area shown And since P(AI) = 0.006 then  EMBED Equation.3 = 0.2-0.006=0.194 And so  EMBED Equation.3  Poisson distribution is expressed by the following:  EMBED Equation.3  Where x - event transformer being struck. e-natural logarithm  EMBED Equation.3  2.718  EMBED Equation.3 -mean=0.4 The probability x = 0,  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  Question 8 Given the proportion p is expected to be 0.48 and from past records  EMBED Equation.3  was found to be 0.4 and n = 10, then np = 4.8 and n(1-p) = 10(1-0.48) = 5.2. Binomial distribution is appropriate for this event.  EMBED Equation.3  Probability of breaking the record is expressed as follows:  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  The binomial distribution, because of the situation of success or failure with known probability. Sample size is small so that np<5 even though n(1-p)>5 slightly. Normal distribution could not be used. Given  EMBED Equation.3  mean  EMBED Equation.3 Standard deviation  x = 40000km guarantee mileage  EMBED Equation.3  From normal distribution tables:  EMBED Equation.3 . The company can distribute the tyres since P(x>40000 km) = 24.2% which is more than the required 20%. Given that  EMBED Equation.3 , from normal distribution table the Z value is 1.2817. So  EMBED Equation.3   EMBED Equation.3 km The advantages of normal distribution (shows importance and uses too) include the following: It explains many physical characteristics like heights/weights of people and dimensions of items produced in a production process Normal distribution assists in sampling from determining the sample size to estimating parameters from statistics Is good for testing hypothesis and setting confidence limits It can be used to approximate other distributions which do not depart so much like binomial and poisson It is used in statistical quality control to set control limits Question 9 The sets can be presented as below:  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  The Venn diagram showing these set is as follows:  The special relationship between sets A and D is that Baraza has both degree and diploma though not being a member of ACII.  EMBED Equation.3  These are the ones who have both a diploma and membership to ACII  EMBED Equation.3  This is a combination of those with degrees or membership to ACII or both.  EMBED Equation.3  The one with a degree and membership to ACII A suitable universal set will be Insurance staff. i) Error being not for consumable means it comes from either equipment or special order types. So the probability of error not for being consumable is:  EMBED Equation.3  ii) Probability of the order coming from maintenance or production is:  EMBED Equation.3  The probability of incorrect order of equipment coming from purchase is: P(Purchase)  EMBED Equation.3 P(Equipment/Purchase)=  EMBED Equation.3  The condition for  EMBED Equation.3  is that it is only possible only if the events A and B are independent. That means the occurrence of A does not affect the probability of occurrence of B. Addition rule states that, if an event can occur in more than one ways (all mutually exclusive), the probability of it happening at all is the sum of the probability of it happening in the several ways. Bayes theorem given two events A and B is as follows:  EMBED Equation.3  Probability of occurrence of event A given that B has occurred is given by the probability of both events occurring divided by the probability of event B occurring. Question 10 i) Situation 1 is normal distribution. The lifetime is a continuous function, with the mean known and a standard deviation known. We can only know a given range and not a particular value. ii)  EMBED Equation.3  from normal distribution table:  EMBED Equation.3  Given that  EMBED Equation.3 , from normal distribution table the Z value is 2.33. So  EMBED Equation.3   EMBED Equation.3  hours Given that the sample size n = 25, and mean of the sample  EMBED Equation.3 hours then:  EMBED Equation.3 . From normal distribution table:  EMBED Equation.3  This is a binomial distribution since it is accept/reject situation (have two events that are mutually exclusive) with respective value of mean that is known. Probability of purchasing a consignment is found as follows: Given that p = 0.15 then q = 1-0.15 = 0.85  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  This is a Poisson distribution since there is a given rate, which is a mean for vehicles passing. In a large interval, the number of vehicles passing is scattered. The interval is small. The mean number of vehicles passing per 20 second interval is  EMBED Equation.3  The probability of more than 3 vehicles passing this point is expressed as follows;  EMBED Equation.3   EMBED Equation.3  Question 11 Probability is a measure of the likelihood of obtaining a particular outcome from an experiment. Given an experiment has n trials with no influence to each other, then having m outcomes of an event A, the probability of event A P(A)= EMBED Equation.3 . It is between 0 and 1. Bayes theorem is as follows  EMBED Equation.3 Which is probability of occurrence of event A given that event B has occurred is given by the probability of occurrence of both events divided by the probability of occurrence of event B. Bayes theorem can be used to revise subjective probabilities made from beliefs. This is so when more information is added to what already exists. The probability tree is as follows Let event T- Passing of test N-No shortage S-Small shortage L-Large shortage Probability of failing test=0.085+0.05+0.04=0.175 Probability of having large or small inventory shortage given the failed test EMBED Equation.3  The probability of passing test=0.765+0.05+0.01=0.825 Probability of no inventory shortage given the failed test EMBED Equation.3  TOPIC 4 Question 1 The null and alternative hypotheses are as follows: H0: ( = 5000 Have same income H1: ( > 5000 Have higher income  This is a one tailed test. Given  EMBED Equation.3  = 5500 n = 144 ( = 1200 ( = 0.05 then the test statistic is  EMBED Equation.3  From normal distribution tables Z at (=0.05 is 1.65. Then, since calculated Z>1.65 then we reject H0 accept H1. Meaning the local workers have significantly higher income than the total population. Question 2 The null and alternative hypotheses are as follows: H0: ( = 15 contact/representative has not changed H1: ( ( 15 contact/representative has changed  This is a two tailed test. Given  EMBED Equation.3  = 16.5 n = 200 s = 3.5 ( = 15 ( = 0.05 then the test statistic is  EMBED Equation.3  H0 is rejected and H1 is accepted. The average number of weekly sales contact per sales representative has changed significantly. From normal distribution tables for two tailed test, Z at (=0.05 is 1.96. Question 3 Calculation of  EMBED Equation.3  is done first  EMBED Equation.3   EMBED Equation.3  Model A Model Bx1 EMBED Equation.3 x2 EMBED Equation.3 118028.4411952521761.78219416318487.1131900418140.11419245190235.115187961371418.78618525Total1,0481,811.33718791,33088  EMBED Equation.3   EMBED Equation.3  Ho  EMBED Equation.3  means are equal H1  EMBED Equation.3  means are not equal F-test  EMBED Equation.3  (Variances are significantly different. So: So  EMBED Equation.3  Since calculated t > t5% = -2.2 then the Ho is accepted. That is, the two models have same productivity. Question 4 Observed frequencies Room Charge. Rating50100150200TotalExcellent1025282891Good2430161686Average80822113196Poor40411825124Total1541788382497 The expected frequencies= EMBED Equation.3  For example, expected frequency for excellent rating when the charge is 50 Rupees is  EMBED Equation.3  Expected frequencies Room Charge Rating50100150200TotalExcellent28.232.615.215.091Good26.630.814.414.286Average60.770.232.732.3196Poor38.444.420.720.5124Total1541788382497The null and alternative hypotheses are as follows H0 : There is no difference in treatment according to room charges. H1 : There is a difference in treatment according to room charges. A table to assist in calculation of  EMBED Equation.3 is as follows. Where fe-expected frequency fo-observed frequency fofe(fo fe)2/fe1028.211.752532.61.772815.210.772815.011.772426.60.253030.80.021614.40.181614.20.238060.76.148270.21.982132.74.191332.311.534038.40.074144.40.261820.70.352520.50.9861.74 For  EMBED Equation.3  degrees of freedom and 0.05 level of significance  EMBED Equation.3  Since calculated  EMBED Equation.3  is greater than EMBED Equation.3  then Ho is rejected. So there is a difference in treatment to customers according to rate charged. Question 5 One-sided confidence interval is the limit of one side of a distribution. It is usually necessary when testing something above or lower side of the parameter of population given. The parameter can be mean or variance. The decision rule is not clear-cut. The null hypothesis should indicate the decision point, and not a region as is indicated by  EMBED Equation.3 . Since hypothesis is usually a belief about a certain aspect different levels of type 1-error can be set depending on ones outlook of error and cost. (Adverse, neutral or risk taker).  EMBED Equation.3  and  EMBED Equation.3  > t0.005, 18 = 2.88 So, reject Ho (Yes. One can conclude that successful and unsuccessful records differ with respect to mean amount of airplay. The assumption made is that the two means of the population are equal. Question 6 Given proportions  EMBED Equation.3 ,  EMBED Equation.3  , and n1 = n2 = 400, the hypothesis is as follows. Ho : P1 = P2 Campaign did not increase sales. H1 : P1 < P2  EMBED Equation.3  Campaign increased the sales. At 5% level of significance (Z0.05 = 1.65) Note: If one is not given the level of significance, then you can choose 5% which is not very strict or very tight. The test statistic chosen is  EMBED Equation.3   EMBED Equation.3  So,  EMBED Equation.3  > Z0.05= - 1.65 So, the null hypothesis is accepted. This means that the campaign did not increase sales Ho :  EMBED Equation.3  New machine does not affect production H1 :  EMBED Equation.3  New machine affects production Given n = 12; S = 71;  EMBED Equation.3 . Then The test statistic  EMBED Equation.3  At 5% level of significance Z0.025 = -1.96 Since calculated Z =- 0.155 > Z0.025=-1.96, then the null hypothesis is accepted. That is, the new machine does not affect production. - Age distribution of students in a school. - Grades of a given examination. - Lifetime of electrical goods. - Production from a given machine. Normal distribution can be used as an approximation to the binomial distribution when the number considered is large, n>30, np (product of number and probability of success) and nq (product of number and probability of failure) are greater or equal to 5. Confidence limits measures the extent that we are sure a particular value of parameter lies. Question 7 Random variable is family income in Shs. Y for a given family x. This is a discrete variable. Family size in number z for a given family x. This is a discrete variable. Distance from home to store site w in kilometers for a given family x. This is a continuous variable. Own dog/cat or not for a given family x. This is discrete. DayN EMBED Equation.3  EMBED Equation.3 Lower z1 pUpper z2 pRequired probability Monday120,00020,000-0.5 0.19151.5 0.43320.6247Tuesday100,00025,0000.4 0.15542 0.47720.3218Wednesday100,0010,0001 0.34135 0.50.1587Thursday120,00040,000-0.25 0.09870.75 0.27340.3721Friday140,00020,000-1.5 0.43320.5 0.19150.6247Saturday160,00050,000-1 0.3413-0.2 0.07930.262 The lowest probability that the store will sell between Sh.110,000 and Sh.150,000 is 0.1587 occurring on Wednesday. Lower limit  EMBED Equation.3   EMBED Equation.3  x1, x2 lower and upper limit respectively x1=110 x2=150  EMBED Equation.3  - mean and standard deviation of the days sales. z1, z2 the standard normal variable as lower and upper limit. Required probability is  EMBED Equation.3  depending on the side that x1, x2 lie on against  EMBED Equation.3 . Reading from normal distribution table, (usually given in exams), then ve sign is used if x1, and x2 lie on the same side and the +ve sign is used if the x1 and x2 lie on different sides. This is a case of hypothesis testing Ho: p = 30% So no need to issue a warning letter. H1: p > 30% Issue a warning letter.  EMBED Equation.3  , p = 0.3, n = 5 then  EMBED Equation.3 < 5  EMBED Equation.3  < 5 so t test is appropriate t test:  EMBED Equation.3  Given 10% level of significance t10%, 5 = 1.48 > calculated t = - 0.488 so we accept the null hypothesis and the warning letter should not be issued. TOPIC 5 Question 1  EMBED Equation.3  Where a and b are determined as follows  EMBED Equation.3   EMBED Equation.3  So given that  EMBED Equation.3 =66,  EMBED Equation.3 =212.1,  EMBED Equation.3 =506,  EMBED Equation.3 =1,406.7,  EMBED Equation.3 =4,254.08 x = number of years, y = annual profit Then  EMBED Equation.3 =1.219 And  EMBED Equation.3  So  EMBED Equation.3  12th year profit  EMBED Equation.3  13th year profit  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  99.44% of the variation in annual profit can be predicted by change in actual values of numbers of years.  EMBED Equation.3  Given 95% confidence interval for the line, at 9 degrees of freedom the t value is  EMBED Equation.3  The confidence interval for the regression line is:  EMBED Equation.3 and given  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  The regression line will be a good estimator of profit because r2 was high (meaning that variation in profit can be highly explained by actual number of years). The standard error of regression line was also very small. Question 2 ET plc returnGroup returnEstimated yYearyixy(yi-y)217.811.14.978.0121112.36.8816.89315.218.516.732.3423.125.427.6921.07529.728.732.9410.5637.433.841.0413.25744.637.747.246.97852.839.650.266.45960.244.758.363.39 The graph will look as follows  EMBED Excel.Sheet.8  Given (y = 345.7 (x = 297.3 (y2 = 15711.59 (x2 = 10285.83 (xy = 12577.02 Then  EMBED Equation.3  And  EMBED Equation.3  so  EMBED Equation.3  b = 1.589  EMBED Equation.3  So  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  So the confidence limit of b given t95%,8=2.31  EMBED Equation.3  The line made from the least square method, best fits the data even the variation of slope is very small. Question 3  EMBED Equation.3  Confidence interval =  EMBED Equation.3   EMBED Equation.3  Calculated t Confidence interval For X1:  EMBED Equation.3   EMBED Equation.3  X2:  EMBED Equation.3   EMBED Equation.3  X3:  EMBED Equation.3   EMBED Equation.3  The assumptions include: Error or residuals are independent and normally distributed for a given value of x. Expected value of error is equal to zero Variance of errors is the same for all xs. These assumptions are set up to enable one to come up with a projection of the population from the sample. So they are reasonable. X1 gives the strongest evidence of being statistically discernible because the t statistic calculated is within the required range. The decision to keep or drop the first regressor will be based not only on t-test, but also looking at the r2 and standard error of the regression in general. The main objective is to include the regressor that reduces standard error of regression and r2 value is large. Other than just having the t test alone. In this case since t calculated is within the required range and standard error of regression is low, then it will be appropriate to include the first regressor x1 in the final regression Question 4 Not finding a linear relationship does not necessarily mean that a relationship does not exist. Other relationships may exist that are non-linear. May be logarithmic, exponential or quadratic. Linear relationship is of the form  EMBED Equation.3  for a 2 variable for example. Correlation measures the direction and extent one variable (dependent) is affected by another variable (independent). So high correlation means the independent variable causes the dependent variable to vary. Given that x=30 then:  EMBED Equation.3  The relationship is linear with a given value of 27.32 even without exposure to insecticides. This value of y increases for any hour of exposure to insecticide by a factor of 1.3. Coefficient of determination r2=0.86 Ho: r = 0 A relationship exists H1:  EMBED Equation.3  A relationship does not exist.  EMBED Equation.3  >t0.975%,14=2.14 So we reject H0 and accept H1 that a relationship actually exists Assumptions include: Relationship is linear Independent variable x is known, so used to predict y Errors are normally distributed with expected value of zero for any value of x Variance of errors is a constant Errors are independent Note: Test statistic is distributed as students t with n 2 degrees of freedom and is given by:  EMBED Equation.3  t = r; n 2; =2.14 from t-tables Question 5 Let y be enrolment and x be quarter of a year. Then  EMBED Equation.3 where  EMBED Equation.3   EMBED Equation.3 . So  EMBED Equation.3 =5.29 And  EMBED Equation.3  giving the expression for y as follows:  EMBED Equation.3 . 1996Quarter x EMBED Equation.3 First21144.7Second22150.0Third23155.3Fourth24160.6 There is an overall trend of increased enrolment with time. Other than the seasonal variation, the relationship can be seen to be linear. So the regression equation is appropriate. The other factors to be included are income, level of education and population growth. The expected enrolment will be followed as a general trend with seasonal variations. Justification of calculation.  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  And r=0.6 meaning there is a positive correlation and 77% of the variation is explained by the quarters. Calculation of  EMBED Equation.3  QuarterEnrolment yy21309002421764310010000466435653210246107114497715041847220994116811093864911139193211262384413452025141011020115151228011667448917735329181813276119227515292010911881Sum211254 Question 6 Regression analysis checks the study of relationship between variables (independent and dependent). Correlation analysis on the other hand looks at the strength of relationship of variables. Both regression and correlation analysis are related since they both look at variables behaviour to each other. The regression line will be: ITEt=-52.59-0.20PRt-1.48PROBt For any coefficient of the independent variables that is above the t value at o.o5 significance level is eliminated. Note: This should not be followed as a rule generally. Here it has been used since there in no much information about the standard error of regression and the correlation coefficient. Otherwise the main aim is to include the variables that give the least standard error and highest coefficient of determination. The 52.59 constant term indicates that people usually pay up their taxes without evading. The 33.44 coefficient of true income indicated that people would tend to evade tax more when their income is increased. The 1.48 coefficient of probability of detection indicates that people will tend to reduce evasion when they realize there are higher probabilities of being detected. From the regression results, it is true, that it had a negative influence on income tax evasion. Question 7  x A=yQuarterly moving averageCentred moving average T  x2 xy A / TDeseasonalised values A / S1983311211211.0642164328.1228.519841358.259150.66.7488.02418.1251640.1234.9028.2535108.525501.1769.2178.7546178.75361021.9438.6298.7519851778.7549490.8009.4478.752818.6256480.1164.9028.539108.7581901.1439.2179.0410169.251001601.7308.1229.5198611199.25121990.97312.146921239.25144360.32414.7069.50313891691040.8897.3738.5414188.3751962522.1499.1378.25198711559.125225750.5486.7481021628.375256320.2399.8046.753171528925513.8254185324902.538Total171 160 21091465 Approximating the trend to be linear, then Trend line - T = a + b (Quarter number. a =  EMBED Equation.3  b =  EMBED Equation.3  given that "x = 171 "x = 2109 "y = 160 "xy = 1465 n = 18 b = EMBED Equation.3  a =  EMBED Equation.3  So, T = 9.9673-0.1135 ( Quarter number Notes: Any number of years moving average can be used. Quarterly moving average has been chosen in this case. Since it is not centred, centering is done as shown. The trend can also be obtained from the time series as required here. The summation for quarter numbers and the actual production are obtained. The additional values of summation of x2 (quarter number squared) and summation of xy (production ( quarter number) are obtained from the additional columns indicated. The values of a and b of the trend line equation can then be obtained as shown. Though not required; It was possible to obtain deseasonalised data before obtaining the tend line. This means a better forecasting equation is obtained (moving average and trend equation would have been used) Seasonal factor S is obtained by averaging the error variation A/T for each quarter as per the second table. Since the summation of the average is not equal to 4 (seasonal aspect) it has to be corrected by the factor 4/3.941. The deseasonalised data is then obtained. Notice the way here a multiplicative model was chosen because of the way the seasonal aspect keeps on changing. Determination of S 12341983----19840.60.1231.1761.94319850.80.1161.1431.73019860.9730.3240.8892.14919870.5480.239--TotalAverage0.7300.2011.0691.9413.941Corrected0.7410.2041.0851.970 So the summations will change to be as follows "x = 171 "x = 2109 "y = 156.643 "xy = 1507.171 n = 18 b = EMBED Equation.3  a =  EMBED Equation.3  So, T = 5.1556 + 0.0393 ( Quarter number Question 8 Month No. (t) Jobs Y t YtMarch 1 353 1 353April 2 387 4 774May 3 342 9 1026June 4 374 16 1496July 5 396 25 1980August 6 409 36 2454September 7 399 49 2793October 8 412 64 3296November 9 408 81 3672Total 45 3480 285 17844 b slope = EMBED Equation.3  a intercept = EMBED Equation.3  With least square method T- trend = a + b (No. of month. Given a = 349.7 and b = 7.4, then T = 349.7 + 7.4 ( No. of month. December ( No. of month is 10, So the forecast is T = 349.7 + 7.4 10 = 423.7 repair jobs H" 424 repair jobs. Weighted 3 monthMonthJobsmoving averageMarch353-April387-May342-June374356.6July396365.7August409384September399401.6October412401.7November408407.8December408.3 December s forecast here is 408 jobs. This is lower than that approximated by least square method of 424 jobs. Note: June = 0.6 ( 342 + 0.3 ( 387 + 0.1 ( 353 = 0.6 ( May jobs + 0.3( April jobs + 0.1 ( March jobs Time series analysis is a method of studying the behaviour of variables changing with time. The main aim is to come up with a given pattern to enable one forecast the variable in a future period. Season is a pattern formed within a given period over the trend (which is the general change over time). For example the change from winter to summer in a given year will explain a season in temperature readings / rain in any given year. Another example is the consumption of alcohol during the month, starting a lot then reducing during the mid-month and then increasing at end-month. The method of semi-averages uses two averages to obtain a trend from a given data. Firstly the variable values are divided into two halfway. The first half number of variables are averaged and the average put at the centre of them. Second half number variables are also averaged and the average put at the centre of them. Secondly, the trend line is drawn by joining the two average points. Forecasts must be treated with caution because of the following: Since historical data is used; it may not necessarily mean that is expected in the future. Furthermore, the extension cannot be pushed far ahead from the last historical data -Accuracy reduces. TOPIC 6 Question 1 Formulation of problem in standard form. z 7x1 5x2 0s1 0s2 = 0 5x1 + 3x2 +1s1 + 0s2 = 50 4x1 2x2 + 0s1 + 1s2 = 30 I Basic solutionBasisx1x2s1s2SolutionRatios153105010s24-201307.5(z-7-5000(IIs1011/21-5/412.52.27(x11-1/201/47.5-15 Ignorez0-17/207/452.5(IIIx2012/11-5/222.27-9.99 ignorex1101/113/228.6363.29(z0017/11-2/1171.8(IVx25/311/3016.65s222/302/3163.29z2019/11089.06 Stop the iteration here since the z function row has no more negative coefficients. Solution. x2 = 16.65 meaning produce 16.65 units of x2. s2 = 63.29 meaning amount of slack for resources 2. x1 = 0 meaning do not produce x1. s1 = 0 meaning resource 1 is used up. Graph is as follows.  EMBED Excel.Chart.8 \s  The various corner points in relation to the tableaus are indicated on the graph. I (0,0), II(7.5,0), III(8.63,2.27) and IV(0,16.65) Shadow prices for the problem are s1 = 19/11 If one unit of resource 1 is added then the z function will increase by 19/11.That means we can pay up to 19/11 to add resource 1. If we produce any unit of x1 then profit will reduce by 2 units. Question 2 Let a, b, and c, be number of machines A, B, and C. These are the decision variables. Formulation of LP model Maximise Output U = 250a + 260b + 225c Subject to the constraints. Capital budget 25a + 30b + 22c d" 350  000 Floor space 200a + 250b + 175c d" 4000 Square feet a, b, c e" 1 Linear / Proportion  the number of units with capital budget and floor space are linearly related. Deterministic  the coefficients for the variables and constraints are known with certainty. Additive Buying one more of a given machine gives more production or additional production. Effect is additive. Divisible This requires that the machines and given constraints to be divisible. In this case the assumption does not hold. Here we have to take a machine as a whole and not or or fraction of the machine. Computer solution and analysis. Target Cell (Max)NameOriginal ValueFinal Valuezfunc sol03527.045455Adjustable CellsNameOriginal ValueFinal Valuesol a01sol b01sol c013.40909091ConstraintsNameCell ValueStatusSlackcapbudget '000' sol350Binding0flospace (sq ft) sol2796.590909Not Binding1203.409sol a1Binding0sol b1Binding0sol c13.40909091Not Binding12.40909Adjustable CellsFinalReducedNameValueGradientsol a1-5.681818182sol b1-46.81818182sol c13.409090910ConstraintsFinalDualNameValuePricecapbudget '000' sol35010.22727273flospace (sq ft) sol2796.5909090TargetNameValuezfunc sol3527.045455AdjustableLowerTargetUpperTargetNameValueLimitResultLimitResultsol a113527.0413527.04sol b113527.04513527.04sol c13.40909091173513.409090913527.04The solution of the problem is as follows The number of machines to buy is: A=1 B=1 C=13 The maximum output is 3527 pieces per day. The capital budget will be used up completely and the floor space will be having a slack of 1203 square feet. So the dual price of the capital budget is $10.22. Note: In exams, the solution from a computer package will be given and the student will be required to interpret the solution Question 3 Let x1, x2, x3, x4, x5, and x6 be the amount invested for the 3-year period for alternatives 1, 2, 3, 4, 5, and 6. Then the objective function will be: z Total dollar return. Maximize z = 0.3x1 + 0.34x2 + 0.35x3 + 0.37x4 + 0.38x5 + 0.45x6 Subject to the constraints: x1 + x2 + x3 + x4 + x5 + x6 = 300,000 $ Capital budget. x2 e" 50,000 $ min limit of alternative 2 x5 d" 40,000 $ max limit of alternative 5 x4 + x6 d" 75,000 $ max limit of total of 4 and 6 x1, x2, x3, x4, x5, x6 e" 0 Effective Annual rate of return Investment Year 1 Year 2 Year 3 Total 1 0.12 0.10 0.08 0.3 2 0.14 0.10 0.10 0.34 3 0.15 0.12 0.08 0.35 4 0.10 0.12 0.15 0.37 5 0.08 0.12 0.18 0.38 6 0.25 0.15 0.05 0.45 Computer solution and analysis. Target Cell (Max)NameOriginal ValueFinal Valuereturn sol0113200Adjustable CellsNameOriginal ValueFinal Valuex100x2050000x30135000x400x5040000x6075000ConstraintsNameCell ValueStatusSlackcapbudget 300000Binding0limitalt2 50000Binding0limitalt5 40000Binding0limitalt4&6 75000Binding0x10Binding0x250000Not Binding50000x3135000Not Binding135000x40Binding0x540000Not Binding40000x675000Not Binding75000 FinalReducedNameValueGradientx10-0.0499x2500000x31350000x40-0.0801x5400000x6750000 FinalDualNameValuePricecapbudget sol3000000.35limitalt2 50000-0.01limitalt5 400000.03limitalt4&6 750000.1TargetNameValueReturn sol113200AdjustableLowerTargetUpperTargetNameValueLimitResultLimitResultsol x1001132000113200sol x2500005000011320050000113200sol x3135000135000113200135000113200sol x4001132000113200sol x5400004000011320040000113200sol x6750007500011320075000113200 The present amount value to be invested in each of the alternatives is: Alternative 1- $0 Alternative 2- $50,000 Alternative 3- $135,000 Alternative 4- $0 Alternative 5- $40,000 Alternative 6- $7,5000 There is no slack in any of the constraints. The dual prices are: Capital budget $0.35 Limitation on alternative 2 $0.01 Limitation on alternative 5 $0.03 Limitation on alternative4 and 6 $0.1 Note: Even though solution was not requested, it has been presented to add on intepretation of LP computer analysis. b) Assumptions: Proportional Increase in more investment ( more return. Deterministic Estimates of present value for each alternative (though estimated). Additive To make up total dollar return, the investment return are added together. Divisible Fractional amounts of the investment options are possible. Question 4 Let x1, x2, x3, x4, x5, and x6 be the number of shares invested in alternatives 1, 2, 3, 4, 5, and 6. And z be the total return (growth and dividend) for the investment. Then the objective function will be: Portfolio dataAlternative123456Current price per share$80$100$160$120$150$200Projected annual growth rate0.080.070.100.120.090.15Annual growth (rate(Current price)$6.407.0016.0014.4013.5030.00Projected annual dividend per share$4.006.501.000.502.750.00Total return per share in $10.1013.5017.5014.9016.2530.00Total risk (risk rate(Current price)$431624916 z = 10.4x1 + 13.5x2 + 17.5x3 + 14.9x4 + 16.25x5 + 30x6 Constraints are: 200x6 d" 250,000 Maximum amount for alternative 6 in $ 80x1 + 100 x2 d" 500,000 Maximum amount for alternatives 1 and 2 combined in $  EMBED Equation.3  ( 4x1 + 7x2 + 0 x3  12x4 + 6 x5 + 4 x6 e" 0 Maximum total risk in $  EMBED Equation.3  Non-negativity and Minimum number of shares of each alternative 80x1 + 100 x2 e" 250,000 Minimum amount for alternatives 1 and 2 combined in $ 4 x1 + 6.5 x2 + x3 + 0.5 x4 + 2.75 x5 e" 10,000 Minimum dividend in $ 80x1 + 100 x2 +160 x3 +120 x4 +150 x5 +200 x6 = 2,500,000 Budget amount in $ Computer solution and analysis. Solution (Max)NameFinal ValueReturn 306910NameFinal Valuex1100x24920x3100x46500x56360x61250ConstraintsNameCell ValueStatusSlack1250000Binding02500000Binding05500000Not Binding25000030Binding0653220Not Binding4322072500000Binding0 Sensitivity analysisFinalReducedNameValueGradientx1100-0.580952726x249200x3100-0.557142712x465000x563600x612500ConstraintsFinalDualNameValuePrice12500000.03940476225000000.0300595235500000030-0.1130952156532200725000000.112857142LimitsNameValuereturn 306910AdjustableLowerTargetUpperTargetNameValueLimitResultLimitResultx1100100306910100306910x2492049203069104920306910x3100100306910100306910x465001002115506500306910x5636063603069106360306910x6125012503069101250306910 The solution is as follows: AlternativeNo. of shares1100249203100465005636061250Maximum return is $306,910 The constraints 1,2,3 and 7 are completely used up. The dual prices are as follows: ConstraintDual price $10.03940476220.0300595233-0.11309521570.112857142The slacks are in constraints 5 and 6 being $250,000 and $43,220 respectively Question 5 Degeneracy in linear programming is a case where one or more basic variables have values of zero in the optimal solution. It is dealt with by selecting another of the tied variables for removal resolving the cycling problem of degeneracy. The Network diagram is as follows  Arcs represent cost/revenue or capacities (cost of shelving in this case) Directed arcs show where flow is to. Net flow=Flow into a node (-ve)+ flow out of a node (+ve) Supply=demand. Nodes 1 and 2 are source nodes (with +ve net flow), nodes 3 and 4 are trans-shipment nodes (with zero net flow) and nodes 5,6,7 are sink node (with ve net flows) The linear programming model for this problem is as follows Taking xij to be units of shelves to be routed from supplies i to demand j Minimize  EMBED Equation.3  Subject to  EMBED Equation.3  node 1 (Apex)  EMBED Equation.3  node 2 (Maxima)  EMBED Equation.3  node 3 (Ujumi)  EMBED Equation.3  node 4 (Pizza)  EMBED Equation.3  node 5 (Oceanic)  EMBED Equation.3  node 6 (Homegrown)  EMBED Equation.3  node 7 (Down town) xij(0 Question 6 Let x1 and x2 be the number of shares invested in airline and insurance shares respectively. Then the objective function will be as follows: Objective function (maximize) z = 2x1 + 3x2 Share appreciation. Subject to the following restrictions: 1. 40x1 + 50x2 d" 100,000 sh. Total investment. 2. 50x1 d" 40,000 sh. Investment in insurance. 3. x1 + 1.5x2 e" 2600 sh. Dividends. 4. x1, x2 e" 0 Non-negativity of number. Reduced cost represents the amount that objective function coefficient of a non-basic decision variable must improve (increase in this case) to be put into the basis. In this case since reduced cost is equal to zero for both variables, it means they are in the basis. Dual prices on the other hand mean the amount that share appreciation will improve in case any of the limiting constraints is increased by one unit. This occurs for constraints that the slack is exactly zero. In this case total investment and investment in insurance do have positive dual prices while Dividends does have zero dual price (can not increase share appreciation if increased by one unit). From the computer solution, the clients money should be invested as follows, to satisfy the restrictions: 1500 shares to be invested in Airline shares, and 800 shares to be invested in insurance shares, to give an optimum quarterly share appreciation of sh. 5400. For the optimal decision to remain the airline shares appreciation should not be lower than 2.5, but can be any higher amount. That is, the optimum solution is insensitive to increase in the share appreciation. Question 7 Formulation of the problem. Take x1 to be the number of large loaves and x2 to be the number of small loaves. Objective function z = 5x1 + 3x2 Profit Constraints. Line 1 x1 d" 280 Maximum number of large loaves. Line 2 x2 d" 400 Maximum number of small loaves. Line 3 10x1 + 8x2 d" 4000 Space ( (1000-3) m3 Line 4 25x1 + 12.5x2 d" 8000 Hours ( (1000-3) 5 x1, x2 e" 0 Non-negativity. Using graphical method.  EMBED Excel.Chart.8 \s The feasible area is that enclosed by corner points ABCDEF Corner points are where the optimum feasible solution exists. The upper points are to be considered for maximization problem. At corner point A, Line 2 and x1=0 intersect So x2= 400 Putting these value in the objective function gives the following z = 5x1 + 3x2=5(0 + 3(400=1200 ProfitA At corner point B, Line 2 and Line 3 intersect x2= 400 and x1= EMBED Equation.3  Putting these value in the objective function gives the following z = 5x1 + 3x2=5(80 + 3(400=1600 ProfitB At corner point C, Line 3 and Line 4 intersect The equations for the lines are: Line 3 10x1 + 8x2 = 4000 (1) Line 4 25x1 + 12.5x2 = 8000 (2) Multiplying equation (1) by 25 and equation (2) by 10 gives the following (10x1 + 8x2 = 4000) ( 25 (25x1 + 12.5x2 = 8000) ( 10 250x1 + 200x2 = 100,000 (3) 250x1 + 125x2 = 80,000 (4) Deducting equation (4) from Equation (3) gives: 75x2 =20,000 (x2 =266.7 (x1 = EMBED Equation.3  Putting these values in the objective function gives the following z = 5x1 + 3x2=5(186.7 + 3(266.7=1733 ProfitC At point D, Line 1 and Line 4 intersect. x1 = 280 (x2 =  EMBED Equation.3  The profit at this point is then equal to: z = 5x1 + 3x2=5(280 + 3(80=1640 ProfitD At point E, Line 1 and x2 = 0 intersect x2 = 0 ( x1 = 280 z = 5x1 + 3x2=5(280 + 3(0=1400 ProfitE Comparing these profits, it is at point C that profit is maximized. So the solution is that: x1 = 186 No. of large loaves produced. x2 = 266 No. of small loaves produced. And the maximum profit is ProfitC=Shs. 1,733 NOTE: Two methods can be used to solve the problem. It is easily solved using the graphical rather than the simplex method, since it is just two variables and sensitivity analysis is not required. To solve this kind of problem (linear programming problem) the following procedure is followed: First, the problem has to be formulated. That is, the objective function and constraints are determined. Objective function is that which is to be optimised. Constraints are the limitations in resources. Secondly, the method of solving is determined. In this case, of a two-variable problem, the better method to use is graphical method, rather than simplex method. Thirdly, the constraints are taken as equalities and a line graph drawn. The unwanted regions are shaded out. Resulting region indicates the feasible region. The optimum point exists where there are corner points, which show extreme amounts. For maximization it is the outer ones to the right and up. For minimization it is the lower side. Lastly, the profit is determined at those points where there is maximum profit, is the point to be used. NOTE: This part simply asks for the procedure followed. Question 8 Simplex method will be appropriate. Formulation of problem. Objective function. Let x1 and x2 be the number of Deluxe and Professional bicycle frames produced respectively per week. z = 1000x1 + 1500x2 Profit sh. Constraints: 2x1 + 4x2 d" 100 Aluminum alloy 3x1 + 2x2 d" 80 Steel alloy x1, x2 e" 0 In standard form: 0 = z  1000x1  1500x2 + 0s1 + 0s2 100 = 2x1 + 4x2 + s1 + 0s2 80 = 3x1 + 2x2 + 0s1 + s2 Table 1x1x2s1s2SolutionRatios1241010025(s232018040z-1000-1500000(Table 2x21/411/402550s220-1/213015(z-2500375037,500(Table 3x2013/8-1/417.5x110-1/41/215z00312.512541,250Stop here The optimal weekly production schedule is as follows: Deluxe bicycle Frame = 17.5 H"17 Professional bicycle Frame = 15 Let 1 be the change in profit from Deluxe bicycle frame. 2 be the change in profit from Professional bicycle frame. So C1 = 1000 + 1 and C2 = 1500 + 2 limit of profit. From the final table: To avoid entry of s1 (312.5  1/41 > 0 ( 1 < 1250 s2 (125 + 1/21 > 0 ( 1 > -250 From the two conditions: -250 < "1 < 1250 and 750 < C1 < 2250 To avoid entry of s1 (312.5 + 3/82 > 0 ( 2 > -833.33 s2 (125  1/42 > 0 (-2 > -500 (2 < 500 So from the two conditions: -833.33 < "2 < 500 And C2 varies as follows 666.7 < C2 < 2000 NOTE: This problem could be solved graphically with part (i) Easily determined. Part (ii) Limits will be determined from equating slopes of the objective function which has coefficients with constraints nearest to it. For part (ii), accurate drawings will be required. Intuition will have to be followed and there will be an assumption that fractions are possible. The technique is really involving. Assumes fractions are possible, which is not really the case like here where we cannot make a bicycle frame. Question 9 A feasible solution is one that satisfies the objective function and given constraints Transportation problem is a special linear programming problem where there a number of sources and destinations and an optimum allocation plan is required. Total demand equal total supply Assignment problem is a special kind of transportation problem where the number of sources equals the number of destinations. That means for every demand there is one supply. This is a case of assignment problem. Assignment problems usually require that the number of sources equal the number of supply. Here there are 5 districts and only 4 salespersons. A dummy salesperson E is introduced with zero ratings. Districts12345A9290949183Sales personsB8488968281C9090938693D7894898488E00000By following the Hungarian method: Firstly: For each row, the lowest rating is reduced from each rating in the particular row. This results to a row reduced rating table. Then all the zeroes are to be crossed by the least number of vertical and horizontal lines. If the number of lines equal the number of rows (or columns = 5 in this case) then the final assignment has been determined. Otherwise the following steps are followed.  12345A971180B371510C44707D01611610E00000Secondly, for each column, the lowest rating is reduced from every rating in the particular column. In this case the table will remain the same since the dummy salesperson has ratings of zero for every district. Thirdly a revision of the opportunity-rating table is done. The smallest rating in the table not covered by the lines is taken (in this case it is one). This is reduced from all the uncrossed ratings and added to the ratings at the intersection of the crossings. Then all the zeroes are to be crossed by the least number of vertical and horizontal lines. If the number of lines equal the number of rows (or columns = 5 in this case) then the final assignment has been determined. Otherwise the following steps are followed.  12345A861080B261400C44708D01611611E00001 Third step is repeated as follows: 12345A64880B041200C22508D01611813E00023Still the optimal solution has not been reached. Third step is again repeated to give the following table:  12345A62680B021000C20308D0149813E00045An optimal assignment can now be determined since the number of lines crossing the ratings is equal to 5. Lastly, the assignment procedure is that a row or column with only one zero is identified and assigned. This row or column is now eliminated. The other zeroes are then assigned until the last zero is assigned. This step-by-step assignment is shown on the following table from the first one to the fifth one. District12345A6268Sales personB02100C2308D149813E0045 The assignment is as follows SalespersonDistrictRatingA583B482C290D178Total rating333The total rating is 333. Question 10 Sensitivity analysis measures how sensitive a linear programming solution is to changes in the values of parameters. These parameters include the coefficients of objective function, limiting resources and non-limiting resources. So sensitivity analysis involves changing any of these parameters and showing how the linear programming problem is affected. Dual values indicate the additional improvement of the solution due to additional unit of limiting resource. In that way, the additional improvement of solution is the price worth of paying to release a constraint Let x1, x2 and x3 be the units of desktop 386, Desktop 286 and laptop 486 Maximize profit Z=5000x1+3400x2+3000x3 Subject to x1+x2(500 limit of desktop models x3 (250 limit of laptop model x3 (120 limit of 80386 chips x2+x3(400 limit of 80286 chips 5x1+4x2+3x2(2000 hours available Assumptions x1, x2, x3 ( 0 Linearity/proportionality Divisibility Deterministic Additive The optimum product mix is that the numbers of units to produce are Desktop 386-120 Desktop 286-200 Laptop 486-200 Maximum profit is Z=5000(120+3400(200+3000(200=Sh1,880,000 Unused resources include the following JK computers can still produce 180 more desktop models (500-120-200) and 50 laptop models (250-200) For used up resources the prices to pay for any additional unit are as follows Sh150 for 80386 chip Sh90 for 80286 chip Sh20 for any hour The range for the variables x1, x2 and x3 are to indicate where the number of units can change without affecting the basic solution The range for the constraints indicate the extent the resources can be changed without altering the basic solution of the linear programming problem The dual value of 80386 chip is Sh 150. That is the addition increase in profit due to increase of one chip. So if the company increases the number of chips by 10, the additional profit will be 10(150=Sh1,500. Question 11 A linear program is one that optimises a linear objective function with given decision variables subject to conditions that the constraints are linear and the variables are non-negative. The basic feasible solution is the set of variable values at a corner point of the feasible region (set of values for decision variables that satisfy both non-negativity and the structural constraints) Degeneracy is a case where one or more basic variables have values of zero in the optimal solution. Alternate optimal solution is said to exist when the number of optimal solutions is infinite and a given constraints is binding (further improvement on objective function is prohibited) Redundant constraint is one that has no effect on the solution space. It contributes nothing to the solution space. Range of optimality represents the limits for which a solution remains optimal Artificial variable is one that is added to the LHS of starting basis so as to preclude yielding of infeasible (negative) solution for a mixed constraint situation. Reduced cost represents the reduction in profit due to addition of a non-limiting resource Dummy destination is one that is introduced so as to take care of excess supply. It has zero profit or cost. Assignment problem is a special kind of problem where there are a number of supplies to satisfy equal number of destinations   Minimize  EMBED Equation.3  Subject to  EMBED Equation.3  node 1 (Thika)  EMBED Equation.3  node 2 (Naivasha)  EMBED Equation.3  node 3 (Machakos)  EMBED Equation.3  node 4 (Nairobi)  EMBED Equation.3  node 5 (Mombasa)  EMBED Equation.3  node 6 (Kisumu) xij(0 i=1,2,3 j=4,5,6 Question 12 Alternate optimal solution exists when the number of optimal solution is infinite and the constraint parallel to objective function is binding.. More than one combination of values for the decision variables will result in the optimal value for the stated objective. Unbound solution is one that extends without limit. This reflects the nature of model constraints  Infeasible solution exists where there is no point that satisfies the constraints and non-negativity conditions.  Linear programming because it usually seeks to optimize a given function subject to various constraints. This is the case here. In formulation, the objective function is the one that is to be optimized. It has the following form Z=c1x1+c2x2+..cnxn Subject to the constraints: a11x1+a12x2+..a1nxn(( ( =) b1 a21x1+a22x2+..a2nxn(( ( =) b2 am1x1+am2x2+..amnxn(( ( =) bm Where ci- Coefficient of decision variable i. xi- Ith decision variable (activity that competes for limited resources) function to be optimise and forms the criteria aji- Coefficient of jth constraint for the Ith variable bj- Right hand side constant for the Ith constraint Number of structural constraint Number of decision variables Production technicians, measurement technicians, profit center personnel, cost center personnel. The required parameters will be reaction times, profit information, cost information and timing information. Question 13 Transportation problem involves movement of some homogenous commodity from various origins or sources of supply to a set of destinations each demanding specific levels of commodity. The main aim is to allocate supply from origins so as to optimize a criterion while satisfying the demand of each destination. Assignment on the other hand is a special kind of transportation model where the numbers of sources of supply are equal to destinations Let x1- the number of spot announcement on television x2- the number of spot announcement on radio Maximize rating Z=600x1+200x2 Subject to x1+x2(30 combined coverage x2(25 maximum radio announcement x2(x1 relation between radio and television announcement x1,2(0 Solving the problem by simplex method as follows Table 1x1x2s1s2s3s4Solbis11110003030s201010025(s31-1001000s4120030000012550021.25=z-600-20000000( Table 2s101.75100 EMBED Equation.3 8.7511.67(s20101002525s30-1.25001 EMBED Equation.3 -21.2517x110.25000 EMBED Equation.3 21.2585z0-50000 EMBED Equation.3 12750( Table 3x201 EMBED Equation.3 00 EMBED Equation.3  EMBED Equation.3 s200 EMBED Equation.3 10 EMBED Equation.3  EMBED Equation.3 s3001.2501 EMBED Equation.3  EMBED Equation.3 x110 EMBED Equation.3 00 EMBED Equation.3  EMBED Equation.3 z00 EMBED Equation.3 00 EMBED Equation.3  EMBED Equation.3 The iteration is stopped here since there is no more negative value in the z row. x1 should be  EMBED Equation.3 H"18 spot announcement on television x2 should be  EMBED Equation.3 H"12 spot announcement on radio Radio is likely to reach a large number of people even though it cannot really give the visual aspect which television has an advantage on. Letting  be a change on the coefficient of x1 then from the final table,  EMBED Equation.3 also  EMBED Equation.3  so that >200 for the basic solution obtained to change. In effect the rating required to change the number of television spot announcement is 600+200=800 and above. That is the rating should be above 800 for the number of television spots to increase. The restriction to relax are the s1 and s4.Combined coverage should be altered and the budget too because they do increase the total rating of advertisement (Combined coverage by  EMBED Equation.3  for one unit relaxed and  EMBED Equation.3  for every budget increase) Any possible increase in advertising budget will increase total rating by only EMBED Equation.3 , which is very small. TOPIC 7 Question 1 Optimal decision using: Max-min criterion Choose decision that maximizes the minimum profit. Min-max choose decision that minimizes the maximum loss. Worst outcomeD1 150DecisionD2 140alternativesD3 180(Decision takenD4 160 Max-max criterion Choose decision that maximizes the maximum profit. Min-min choose decision that minimizes the minimum loss. Best outcomeD1 250(Decision takenDecisionD2 225alternativesD3 220D4 230 Min-max regret criterion from regret table, choose the decision that minimizes the maximum regret. Regret = maximum payoff for a state of nature less the payoff of a given state in a decision alternative. E.g. regret for: D1(1 = 220 - 150 = 70 D3(1 = 210 - 190 = 20 Regret table: States of Nature12345MaxEitherD1700500070(DecisionDecisionD2408530502585alternativeD304035207070(Or thisD430150109090 Maximum expected payoff  assuming equal likelihood of states of nature, decision that maximizes the expected payoff determined is taken. For example: Expected payoff for D2 = Payoff (D2(1 + D2(2 + D2(3 + D2(4 + D2(5)/5 = (180 + 140 + 200 + 160 + 225)/5 = 181 Expected PayoffD1 203(Decision takenDecisionD2 181alternativeD3 194D4 198 Question 2 Min-max Worst outcomeD1250DecisionD2225alternativesD3220(Decision takenD4230Min-min Best outcomeD1 180DecisionD2 140(Decision takenalternativesD3 180D4 160 Min-max regret Regret = loss of a given state in a decision alternative less minimum loss for a given state of nature. E.g. regret for D3(5 = 180 - 160 = 20 Regret table: States of Nature12345MinD10850509090DecisionD23002006565(Decision takenalternativeD3704515302070D440705040070Minimum expected loss Expected lossD1 203DecisionD2 181(Decision takenalternativeD3 194D4 198 Question 3 Expected payoff for a decision = ((Payoff; ( f(() is Where i = 1, 2, 3, 4 decision alternative j = 1, 2, 3, 4, 5, 6 states of nature  Expected payoffD1 342.4Decision D2 359.6alternativesD3 330D4 378.6(Decision taken Expected value under certainty: Under certainty given any state of nature a decision maker will choose the alternative with the highest payoff as follows: States of nature123456Certain payoff350420540500400450Probability0.180.10.160.240.20.12TotalExpected value634286.41208054445.4 Expected value of perfect information is equal to expected value under certainty less the expected value under uncertainty Value in (b) Value in (a) = 445.4 378.6 = 66.8 Question 4 Let Di be the decision alternative, where i 0 stock 100 bond % of 1million family trust investment 10 stock 90 bond % of 1million family trust investment 20 stock 80 bond % of 1million family trust investment 100 stock 0 bond % of 1million family trust investment j  states of nature where j  1. Solid growth (12% bond: 20% stock) 2. Inflation (18% bond: 10% stock) 3. Stagnation (12% bond: 8% stock) The payoffs in $  000 are as follows: States of nature 1 2 3 Max Min -0.4 Equally D1 200 100 80 200 80 128 126.7 D2 192 108 84 192 84 127.2 128 D3 184 116 88 184 88 126.4 129.3 D4 176 124 92 176 92 125.6 130.7 D5 168 132 96 168 96 124.8 132 D6 160 140 100 160 100 124 133.3 D7 152 148 104 152 104 123.2 134.7 D8 144 156 108 156 108 124.8 136 D9 136 164 112 164 112 132.8 137.3 D10 128 172 116 172 116 138.4 138.7 D11 120 180 120 180 120 144 140 Payoff = (% bond x bond Yield + % stock x stock. Yield) ( $1,000,000 E.g. D1(2 = (0 ( 12% + 100% ( 20%) 1,000,000 and D7(3 = (60% ( 12% + 40% ( 8%)1,000,000 = (0.6 ( 0.12 + 0.4 ( 0.8)1,000,000 = $104,000 Max-max - D1 with payoff of $200,000. Max-min - D11with payoff of $120,000. Hurwicz - D11with payoff of $144,000. Equally likely - D11 with payoff of $140,000. NOTE: Payoff in Hurwicz = (0.4 ( Max payoff + 0.6 ( min payoff) for a given decision alternative E.g. D3 = (0.4 ( 184 + 0.6 ( 88) $126,400 Regret table: States of nature123MaxD10804080D28723672D316643264D424562856D532482448D640402040D748321648D856241256D96416464D10728472D11800080 Either D1 or D11 with a regret of $80,000 will be taken NOTE: Regret = maximum payoff for a given state of nature less the payoff of a given state in a decision alternative E.g. D7(2 = 180 - 148 = 32  States of nature1 2 3Probability0.4 0.25 0.35 Expected value$ 000 D1133D2133.2D3133.4D4133.6D5133.8D6134D7134.2D8134.4D9134.6D10134.8D11135(Best strategy Expected value for decision = Payoff in (1 ( 0.4 + Payoff in (2 ( 0.25 + Payoff in (3 ( 0.35 E.g. D1 = 200 ( 0.4 + 100 ( 0.25 + 80 ( 0.35 = 133 With perfect information States of nature123TotalStrategy under states of nature200180120167,000 Here the maximum payoff for any state of chosen total expected payoff: = 200 ( 0.4 + 180 ( 0.25 + 120 ( 0.35 =167,000 Question 5 Payoff = (Revenue / Household ( No. of households) Initial cost Payoffs in millionsNo. of householdsPlanRevenue10,00020,00030,00040,00050,00060,000I150-5.5-4-2.5-10.52II180-5.2-3.4-1.60.223.8III200-5-3-1135IV240-4.6-2.20.22.657.4 Optimistic approach means that the max-max criterion is used. PlanMaxI2II3.8III5IV7.4(Adopt Plan IV Min-max regret means, from the opportunity loss table, the minimum of the maximum is actually chosen. The opportunity loss table. Opportunity loss or regret = max payoff for a given number of households less the payoff of a given number of household and given plan. E.g. Plan III for 40,000 household, = 0.26 - 1 = 1.6 million shillings No. of householdsPlan10,00020,00030,00040,00050,00060,000MaxI0.91.82.73.64.55.45.4II0.61.21.82.433.63.6III0.40.81.21.622.42.4IV0000000(Adopt Given the probabilities, the payoff table will change to be as follows Payoff = Payoff as determined from part of a) multiplied by the given probability under the respective pricing plans. e.g. for Plan II for 3,000 household = -1.6 ( 0.2 = -0.32 Expected payoff = sum of all the payoffs for a given plan. No. of householdsPlan10,00020,00030,00040,00050,00060,000ExpectedI0-0.2-0.125-0.40.150.4-0.175II-0.26-0.34-0.320.060.40.570.11(AdoptIII-0.5-0.6-0.20.20.60.50IV-0.92-0.550.050.390.50.37-0.16 The pricing plan to follow is Plan II, which gives a higher expected payoff of sh110, 000. The approach used in part (b) is that of decision making under uncertainty. Probabilities of occurrence as much as the outcomes are not known with certainty. The approach in (c) on the other hand is decision making under risk. Probabilities of occurrence of an event is known with given amount. This gives expected payoff for any decision undertaken. Question 6 Decision making under risk is when decisions are made using already known probabilities for states of nature or outcomes. The probabilities can come from previous data. Decision making under certainty is when a decision is made where there is no prior probabilities for states of nature or outcomes. Decision tree is a diagrammatic representation of decisions given different states of nature. Nodes and branches are used to represent the decisions and outcomes from given decisions. Probability tree is a diagrammatic representation of the sequence of outcomes given certain probabilities. Minimax criterion-involves choosing the alternative with minimum regret from choice of maximum regrets from given events. Maximax criterion- involves choosing the alternative with maximum payoff from choice of maximum payoffs from given events. Pure strategy in a game is where each player knows exactly what the other player is going to do. The same rule is still followed each time. Mixed strategy is where there is a combination of the rules followed. Each player does not know what the other player is going to do. In this case probabilities are used to find what each player will do. The main aim is to maximize expected gains or to minimize losses. Games represent a competitive situation where players aim to gain from each other. Games with more than two persons represent real life situation where there are more than two persons as players. Each person seeks to gain from the others. Non-zero sum games represent situation where it is not necessarily that what one losses is gained by another. Question 7 Dominance is a principle where superior strategies of a player are said to dominate his inferior ones. This is because there is no incentive to use inferior strategies Saddle point is said to exist when the maximum of row minimum coincides with the minimum of the column maxima in a payoff matrix Mixed strategy is when players in a game use a combination of strategies and each player is always kept guessing as to which course of action is to be selected by the other player at a particular occasion. Players do not play the same strategy each time Value of a game is the payoff of play when all the players of the game follow their optimal strategies. The payoff table is as follows Player B strategiesRow minimum1234Player A Strategies1223-1-1243262(Column maximum4336(( It is not possible to determine the value of the game because player As stategy does not result to player Bs strategy. There is no saddle point .In this case a mixed strategy is adopted to determine the value of the game.             The value of the game is determined as the highest point of the shaded area (shown on the drawing by an arrow) This represents the highest loss on average that player A can have given player Bs winnings. This point can be determined by extracting a 2(2 matrix made up of the lines that intersect to make the point. In this case the matrix is as follows. Player By1-yPlayer Ax3-11-x26The expected value of the game E(x,y)=3xy-(1-y)x+2y(1-x)+6(1-x)(1-y) Differentiating partially with respect to x and y and equating to zero gives the following.  EMBED Equation.3   EMBED Equation.3  So the optimal mixed strategy for the game is Player A  EMBED Equation.3  Player B  EMBED Equation.3  Value of the game is obtained by substituting the mixed strategy in the expression for expected value of the game as follows.  EMBED Equation.3  Notes The player with only two strategies available is drawn first represented by the two lines 1 and 2 (in this case player A). The two lines 1 and 2 are divided into equal number of expected winnings of player B as shown. Player Bs strategy can then be drawn. This is drawn by a straight line joining the winning of Player B given player As strategy. For example for player Bs first strategy, a line is drawn from 2 (if player A plays strategy 1) to 4 (if player A plays strategy 2). This is done for every strategy of player B. TOPIC 8 Question 1 Final network diagram is as follows.   EET-Earliest event time (from forward pass) LET-Latest event time (determined from backward pass) Slack being the delay that an activity can have without delaying the overall time for the project is calculated as follows. The slack for activity A is determined as follows: Slack sA=LET2-EET1-dA =11-0-3=8. Where: LET2 - LET of end event EET1 - EET of start event dA - Duration of activity A LET of the end event less the EET of the start event less the duration of the activity. This is done for all activities as follows:  EMBED Excel.Sheet.8  Critical path is formed of activities that are having zero slack. Delaying any of these activities will lead to delay of completion of the project. From the table, the activities having zero slack are C-E-F-G-H. These activities have been marked on the diagram with The time chart is drawn as follows:  The scheduling flexibilities can be clearly seen from the time chart showing the slack, duration and the latest start for the activities Activities CEFG and H cannot be delayed at all since they form the critical path Activity A can start as late as 8th week Activity B can start as late as 11th week Activity D can start as late as 11th week Question 2 Calculation of estimated duration dij and standard deviation of duration (ij from the data of time estimates for the various activities is as follows: dij =  EMBED Equation.3  and (ij2 = EMBED Equation.3  Where: aij- optimistic time bij- pessimistic time mij- most likely time  EMBED Excel.Sheet.8   The slacks in this situation are all more than in the situation where optimistic/pessimistic times are not included. The critical path remained the same being C-E-F-G-H. The variance for the whole project is as follows (2=(A2+(B2+(C2+(D2+(E2+(F2+(G2+(H2 (2=0.25+0.03+0.69+0+1.78+0.03+0.11+0.44 (2=3.6 The expected time of completion is T=23.5 weeks. The probability of completion of project within t=22 weeks is as follows: P(t EMBED Equation.3 )=P EMBED Equation.3  =P EMBED Equation.3  = P EMBED Equation.3  From normal distribution table at z=-0.79, the required probability is (0.5-0.2967)=0.2033 So the probability of completing the project in 22 weeks is 0.2033. Expected time of completion is T = 23.5 weeks. So the probability of finishing the project within the earliest expected completion date is  EMBED Equation.3  From normal distribution tables at z=0 the probability =0.5. So the probability of finishing the project within the earliest expected completion date is 50% The probability of the project taking more than 30 days to complete  EMBED Equation.3  From normal distribution tables at z=3.56 the probability =0. So the probability of the project being completed after 30 weeks =0. Question 3 Linear slope R for the activities is determined as R= EMBED Equation.3  ActivityR $/weekA3.25B1.6C4.5D3.3E10F1.6G5H25 Crashing all the activities will give the following network.  The critical path is still C-E-F-G-H with completion time being 13.5 weeks The schedule of cost is as follows CommentTime in weeksDirect cost (000)Indirect cost (000)Opportunity cost (000)Total cost (000)Normal22.0140.9279177.9Crash F21.5141.726.57177.2Compress C20.5146.225.55178.7Crash C19.5150.724.53180.2Crash G18.5155.723.51182.2Compress E17.5165.722.50190.2Compress E16.5175.721.50197.2Crash E15.5185.720.50206.2Compress H14.5210.719.50230.2Crash H13.5235.718.50254.2The minimum cost occurs at 21.5 weeks indicated on the table. Notes: The linear slope R indicates the crash cost per week crashed. If any activity is to be crashed, the one with lowest crash cost per week and in the critical path is chosen first until it is exhausted. To find the minimum cost schedule, a table is drawn as shown. Direct cost is made up of normal cost and any crash cost. Indirect cost will have the fixed and variable cost while opportunity cost will be there for any period exceeding 17 weeks. Normal time cost is made up of: Direct cost of $140,900 determined by addition of all direct the costs of activities. Indirect cost is equal to $5,000 + elapsed time ( 1000 = $27,000 Opportunity cost=(elapsed time-17) weeks(2000=(22-17)(2000=$10,000 Total normal cost =140,000+27,000+10,000=$177,900 With the aim of reducing costs and time, activities are crashed and compressed depending on which activity is in the critical path and has the lowest crash cost per week at that time. This is done until the project crash time is reached. So to start, activity F is crashed because it is in the critical path and has the lowest crash cost per week. This adds onto direct cost the amount of $1,600(0.5 weeks and reduces indirect cost by $1,000(0.5 weeks and opportunity costs by $2,000(0.5 to give the total cost of $177,200. The next activity in the critical path with lowest crash cost per week is C. This is first compressed by one week before crashing it. This process continues with costs determined on the table. Care has to be taken as to the maximum possible crashing of activity. Activities not in the critical path are not crashed because they add cost without reducing time. Question 4 To get the project completion time and critical path the network is drawn as follows.  Doing a forward pass on the activities, the earliest event times are determined as shown on the network diagram. The completion time is determined to be 58 hours. On doing a backward pass the latest event times are determined. From these event times the critical path can be identified as those that are between events where both earliest and latest event times are equal. From the network diagram the critical path marked with Critical path is A - D - K. Activities G and E are not part of critical path. Activity Gs earliest start is 11 hours of which activity E can start too since its latest start is 16 hours (LET2 - dE = 23 - 7 = 16). So the activities G and E can be performed at the same time without delaying the project. One person can perform activities A, G and I without delaying the overall completion of the project. This is because activities G and I are not part of critical path. G can start after activity A since its earliest start time is 11 hours which is well over the latest end time for activity A. Activity I can start after G and still be completed without delaying project time. Activities G and L are not in the critical path. Activity G can be delayed for 7 hours and activity L can be delayed for 4 hours without delaying the project time. Delaying activity G by 3 hours does not make the activity critical. Furthermore, delaying the activity L by 4 hours just makes this activity critical. Therefore the overall effect is that there is no delay in the project. NOTES: This question could be answered using a table of scheduled times and a Gantt chart. The table of activity times is as follows.  EMBED Excel.Sheet.8  To be able to see the scheduling flexibilities, a Gantt chart can be drawn as follows.    Question 5 Network planning is the arrangement of activities and events in a diagram to create a logical relationship from start to end making up a project. Activities are those actions that take up resources and time. Events indicate start or completion of activities Critical path is a combination of activities that, if any of them is delayed, then the projects duration will also delay. That is, they are critical to the projects duration. They are activities with zero floats. Float is the amount of time an activity can be delayed without delaying the overall time of the project. The activity on node network can be changed to event on node for ease of seeing the critical path. This also reduces the number of nodes encountered.  The project will take 22 weeks to complete. Activity D cannot be delayed without delaying the entire project, since it is an activity in the critical path A-D-F-H. Activity E can start as early as after 3 weeks and can be delayed for one week without delaying the project time. NOTES: The question could be approached in a different way by not changing the activity on node diagram. Instead a chart of float and arrangement of activities will be used. A chart of duration and float is as follows From the on node network the preceding activities can be read from the network shown by arrows entering the node (activity). ActivityPreceding ActivitiesA-B-CADAEBFD,FGD,EHC,F  EMBED Excel.Sheet.8  Question 6 The shortest time to finish the project is determined by crashing all the activities. The network diagram drawn using crash times is drawn as follows. (Note the normal duration for activities is in brackets and event times are above and below the events)  From the network diagram, the critical paths are A-C-F and B-D-F. The project crash duration is 8 days. The crash cost for the project is (70+50+15+55+30+17.5) EMBED Equation.3 1,000=KSh. 237,500 The cost schedule table is as follows.  EMBED Excel.Sheet.8  The linear crashing cost per day R= EMBED Equation.3  ActivityRA10B10C5D5E10F7.5It is economical to do the project within the normal duration of 14 days without crashing any activity. Notes: When normal activity durations are used, there is only one critical path B-D-F. So the activity with the lowest crash cost per day is D. It is compressed first by one day then again compressed and finally crashed. The opportunity cost decreased to zero while the additional crash cost increased progressively by Sh 5,000. At this point there are two critical paths A-C-F and B-D-F. Activity F is chosen to be crashed although it has a higher cost per day than activity C. This is because to crash C, activity B has to be compressed to be able to reduce the project duration by one day. Activity C can then be crashed although this does not reduce the duration because of the other critical path B-D-F. Compressing activity B after C is crashed results to reduction in project time of one day. Crashing activity B will require that activity A is crashed too to come to the minimum project duration of 8 days. At this point crashing terminates. b) The different approaches to displaying project information are Gantt chart, project evaluation and review technique PERT, critical path analysis CPA and resource schedule charts. Gantt chart involves displaying the activities on a graph against time. A line shows start, duration, end and float of activity. CPA involves displaying project activities on network. The logical relationship between activities is shown together with activity durations. From this network, the critical activity can be determined. PERT involves displaying project activities on a network like in CPA. The times for the duration used here are uncertain. So the expected time is used instead. Resource schedule chart involves presenting project activity resources required and what is available on chart. For every resource, in a project, a resource chart is drawn. Question 7 The four attributes that make the Beta distribution be chosen as representing distribution of times for PERT analysis are: It is uni-modal It has finite limits Can assume flexible shapes There is goodness of estimates of expected duration and variance b)  Critical path is B-E-G-H with shortest project duration being 14 weeks. The workers schedule merged with the Gantt chart for the project is as follows:   Lillian Wambugu can engage six workers through out the duration of the project and she will finish the project within the 14 weeks . Notes: Notice how activity C and F have been pushed to their ends of slack times to ensure a smooth resource engagement. Part III: Comprehensive Mock Examinations Questions - Mocks PAPER 1 Time allowed: 3 hours Answer any THREE questions in SECTION I and TWO questions in SECTION II. Marks allocated to each question are shown at the end of the question. Show all your workings. SECTION I QUESTION ONE Write down short notes on: Final demand. (2marks) Technical coefficients. (2marks) Closed model versus open model. (3marks) Three industries, packaging P, Bakery B, and Flour F are related with the intermediate demand matrix below. Output industry Input Industry EMBED Equation.3  The final demand for P, B and F are: D =  EMBED Equation.3  Required: Determine the total output for products of industries P, B and F. (10 marks) Comment on the total output for industries. (3 marks) QUESTION TWO Define the following terms as used in measures of dispersion: Platykurtic versus leptokurtic and mesokurtic. (6marks) Coefficient of skewness. (2marks) Quartile deviation. (2marks) b) A survey by Marketing Society here in Kenya found out the following buying habits of household air conditioners considered a luxury. Gross income per monthNumber of consumers who buy Air conditioner Up to 5,000  2 5,001 10,000 15 10,001 15,000 25 15,001 20,000 28 20,001 25,000 33 25,001 30,000 35 30,001 35,000 38 35,001 and above. 40 Required: Calculate the arithmetic mean, median and mode. (5 marks) Does the survey show that the luxury item is consumed by high-income earners? Show your answer by calculating appropriate measure of skewness. (5 marks) QUESTION THREE In connection with probability, define the following terms Compound event (2 marks) Mutually exclusive events (2 marks) Collective exhaustive events (2 marks) Equally likely events (2 marks) Conditional probability (2 marks) Given the following distribution of wages for 500 technicians in motor companies in industrial area. Wages in KSh3000-40004000-50005000-60006000-70007000-80008000-9000No. of technicians30402501006020 A technician is chosen from the above group. What is the probability that his wages are? Under KSh 5,000 (4 marks) Above KSh 6,000 (4 marks) Between KSh 5,000 and KSh 6,000 (2 marks) QUESTION FOUR Define the goodness of fit test. How is it applied in accounting? (5 marks) A research studying the role of stress and its implication on personal life in respect of job change over by low cadre staff, came up with the following data. It relates to 30 firms over 3-year period No. of people changing jobs in a year0123456789Observed frequency818192016128432 By fitting a Poisson distribution to get expected frequency, test its goodness of fit. (15 marks) QUESTION FIVE With reference to linear regression define the following terms: Scatter diagram. Bivariate distribution. Positive correlation. Confidence interval. Auto correlation. (10 marks) The following data relates business turnover and staff of fast moving consumer goods company EAI Ltd: Year19931994199519961997199819992000Business turnover in Millions of shillings4550607580110150170Staff2,6003,0003,1003,5303,8504,3005,8707,150 Required: Fit an appropriate regression equation. (8 marks) Estimate the staff requirement when business turnover reaches Sh.200 Million. (2 marks) SECTION II QUESTION SIX What are the underlying assumptions in linear programming? (5 marks) Zadock ltd. manufactures various kinds of furniture namely ordinary chairs, baby cots, and executive chairs. All the products use raw material (wood), machine time (lathe) and manual labour (technicians). The requirements for the furniture are summarised as below. wood in Cubic metersLathe time in hoursTechnician time in hoursOrdinary chair1032Baby cots1253Executive chairs2065The estimated profits for each kind of furniture are Sh. 26, Sh. 35 and Sh. 50 respectively. There is a limitation of capacity, in that only 300 cubic meters of wood, 118 hours of lathe time and 90 hours of technician time are available. This problem was solved using a spreadsheet and the following was obtained. Target Cell (Max)NameFinal ValueZ856X16X220X30ConstraintsNameCell ValueStatusSlackwood300Binding0lathe118Binding0technician72Not Binding18X16Not Binding6X220Not Binding20X30Binding0VariablesFinalReducedNameValueGradientX160X2200X30-2 ConstraintsFinalDualNameValuePricewood3001.78571428lathe1182.71428571technician720CoefficientsAdjustableLowerTargetUpperTargetNameValueLimitResultLimitResultX16-3.5972E-147006856X220015620856X3008560856 Required: Formulate the linear programming problem into standard form for input to the computer spreadsheet (5 marks) What are the units of each kind of furniture to be produced to maximise profits. What is the maximum profit? (5 marks) Is there any resource that is not used up? For the used up resource if any, how much would you pay for one additional unit. (5 marks) QUESTION SEVEN Define the following as used in game theory: N person game. (1mark) Saddle point. (2marks) Strategy. (2marks) Rules of dominance. (3marks) Peter and Ngomongo are second hand dealers of electronic equipment and shoes along Baricho road. Due to hard economic times and restriction by City Council, sales have been decreasing. Each of them came up with strategies to expand into the other business (they have monopoly now). Each of them knows what the other is considering thus influences each others decision. Peter had the following matrix of profit per day. NgomongoExpandDont expandPeterExpand10000Dont expand-500500 Required: Interpret the matrix. (4marks) Solve the game to determine the average winning (loss) each cousin would have. (8marks) QUESTION EIGHT Vick Press Ltd is planning to expanding into offering security systems in peoples homes scared of security. The following are activities and associated costs for the expansion:  Normal Crash ActivityPredecessor Time weeks Cost Sh. Time weeks Cost Sh. A - 10 10,000 7 12,000 B A 35 50,000 33 52,000 C A 4 7,000 3 8,000 D C 25 20,000 25 26,000 E B,D 5 5,000 4 4,500 F C 2 4,000 2 4,000 G F 4 30,000 4 30,000 H G 2 15,000 1 25,000 I E,H,L 1 4,000 1 4,000 J - 8 12,000 4 24,000 K - 12 24,000 10 20,000 L J,K 4 6,000 2 2,000 M J 8 10,000 6 8,000 Required: Determine the critical path. (8marks) Determine the minimum time and minimum cost for the networks. (7marks) Given that, for every delay beyond 40th week, there is share of Sh.1,000 per week loss of profit. Is it advisable to crash the project from 51 to 45 weeks? Why? (5marks) PAPER 2 Time Allowed: 3 hours Answer any THREE questions in SECTION ONE and TWO questions in SECTION II. Marks allocated to each question are shown at the end of the question. Show all your workings. SECTION I QUESTION ONE Kiko Manufacturing Ltd. wants to take a decision of introducing a new soap Kikope. The cost for introducing Kikope (Initial advertising, promotion and fixed cost for one year of production) is estimated at Ksh.30,000. The variable cost per bar of soap is Ksh.30 and the expected selling price is Ksh.50. Required: Draw the cost, revenue and profit functions and from it: Determine the break-even level of production. (3 marks) Determine the profit on the sale of 2,500 soap bars. (2 marks) Caterpillar Company Ltd. produces generators and drilling engines. The revenue function that describes total revenue for sales in a particular quarter of these engines is given by: R = 8x + 5y + 2xy x2 2y2 + 20 Where: x number of generator engines in thousands. y number of drilling engines in thousands. Required: At what quantities of x and y is revenue maximised in a particular quarter. (5 marks) Define the terms systematic, stratified, multistage, cluster and quota sampling as used in statistical inference. QUESTION TWO Define the following: Markov process. (2 marks) Cyclic chain. (2 marks) Absorbing state. (2 marks) State transition matrix. (2 marks) Steady state. (2 marks) In brand switching between different toothpastes named Closed-up CU, Cols-gate CG and Aquas-fresh AF, the state transition matrix below is obtained for a particular month. From To  EMBED Equation.3  Required: If the market share for the different toothpastes were 0.4, 0.3 and 0.3 for CU, CG and AF for January 2002, what will be the market share in February 2002 and March 2002? (6marks) What is the market share equilibrium situation? (4marks) QUESTION THREE Write short notes on the following: Price relatives. (2marks) Fixed base method. (2marks) Chain base method. (2marks) Weighted index number. (2marks) Quantity index number. (2marks) The price index for 2001 with base 1985=100 for the listed goods is. GoodsPrice IndexX Y Z100 130 180 Find the Arithmetic mean and geometric mean of these three indices. (5marks) In a base period, salaries comprise 50% of selling price of product, materials and overheads 40%, and profits 10%. If salary structure rose by 20% and materials and overhead costs rose by 10% from base period. What must have been the percentage increase in selling price if profits remained 10% of selling price? (5marks) QUESTION FOUR What is a Venn diagram? How can it be used to determine probability of a given event? (4 marks) Zinco International Corporation had 1,500 employees. In the year 2001, 300 employees got salary increment, 100 were promoted and 50 got both increment and promotion. Required: How many employees got only a promotion? What is that probability? (2 marks) How many employees got neither increment nor promotion? Give that probability (2 marks) Explain the meaning of a random variable (2 marks) Zebro Match Manufacturers finds that 5% of the matches in a box are defective. Determine the probability that out of a box containing 50 match sticks: None will be defective (4 marks) If the guarantee is not more than 4 will be defective, what is the probability that the box will meet guarantee quality? (6 marks) QUESTION FIVE Forecasting is the attempt to predict the future by using qualitative or quantitative means. Discuss the qualitative and quantitative techniques available for managers in predicting the future. (6marks) What are the steps in time-series decomposition? (3marks) Given the following data of production of vehicles by a local car assembly KMA Ltd. Determine the deseasonalised data and using the forecasting relationship forecast the production in May 2003. Production in 000 YearJan.Feb.MarchAprilMayJune JulyAug.Sept.Oct.Nov.Dec.20017.927.817.917.037.257.175.013.96.647.036.886.1420024.864.485.265.486.426.824.982.454.516.387.557.59 (11marks) SECTION II QUESTION SIX Define a dual problem in relation to the primal problem. (3 marks) A fruit/juice kiosk along Lusaka road, VegFresh kiosk has come up with the following formulae of making natural juice for its industrial area customers. 5 litres lemonade: 2 dozen lemon, 2 kg of sugar, 2 ounces of citric acid and water 5 litres grapefruit: 11/2 kg grape fruit, 11/2 kg of sugar, 1 ounces of citric acid and water 5 litres orangeade: 11/2 dozen of oranges, 11/2 kg of sugar, 1 ounces of citric acid and water The selling prices of the fruit juices per every 5 litre are: Lemonade: Sh. 37.50 Grapefruit: Sh. 40.00 Orangeade: Sh. 42.50 During the cold weather season (May 2002) VegFresh kiosk had in stock 2,500 dozen lemons,2,000 kg grape fruit, 750 dozen oranges, 5,000 kg of sugar and 3,000 ounces of citric acid Required: Formulate the linear programming problem (5 marks) Formulate the dual of this primal (5 marks) The computer solution of the dual was determined to be as follows (Minimise)NameFinal Valueturnover83958.3333NameFinal ValueLemons0Grapefruit7.91666667Oranges15.8333333Sugar0Citric acid18.75ConstraintsNameCell ValueStatusSlacklemonade37.5Binding0grapefruit40Binding0orangeade42.5Binding0Lemons0Binding0Grapefruit7.91666667Not Binding7.916667Oranges15.8333333Not Binding15.83333Sugar0Binding0Citric acid18.75Not Binding18.75Sensitivity ReportFinalReducedNameValueGradientLemons01999.99976Grapefruit7.916666670Oranges15.83333330Sugar01750Citric acid18.750 ConstraintsFinalDualNameValuePricelemonade37.5250grapefruit401333.33333orangeade42.5500Limits ReportTargetNameValueturnover83958.3333AdjustableLowerTargetNameValueLimitResultY10083958.33Y27.916666677.91666783958.33Y315.833333315.8333383958.33Y40083958.33Y518.7518.7583958.33 What are the manufacturing quantities in May 2002 that maximises the turnover? And what is the maximum turnover? (4 marks) Which raw materials are not used up? What are the amounts? (3 marks) QUESTION SEVEN What is Laplace criterion? How does it differ from Hurwicz criterion? (4marks) Sikoka health club specializes in provision of sports/exercise and medical/dietary advice to clients. The service is provided on residential basis and clients reside for whatever number of days that suit their needs. Budget estimates for the year ending 30 June 2003 is as follows: Maximum capacity of center = 50 clients per day for 350 days in the year. Clients will be invoiced at a fee per day. The budgeted occupancy level will vary with the client fee level per day and is estimated at different percentages of maximum capacity as follows: Client fee per day in Sh.Occupancy levelOccupancy % of maximum capacity3,600High904,000Most likely754,400Low60 Variable costs are estimated at one of the three levels High 1,900 Most likely 1,700 Low 1,400 The range cost levels reflect only the possible effect of the prulus price of goods and services Required: Summary of budgeted contribution to be earned by Sikoka Health Club for the year ended 30 June 2003 for each of the nine possible outcomes. (10marks) State the client fee strategy that will result from the use of the maximax, maximin and minimax regret decision criteria. (6marks) QUESTION EIGHT MMK Ltd. plans to conduct a survey. The following table shows the tasks involved, the immediately preceding tasks and for each activity duration the most likely estimate (L), the optimistic estimate (O) and the pessimistic estimate (P). Number of daysPrecedingMost likelyOptimisticPessimisticActivityactivity (L) (O) (P) A - 6 4 8 B - 24 20 40 C A 10 8 24 D B 8 4 12 E D 6 6 6 F B 8 6 10 GC,E,F 20 16 36 H G 6 4 8 I G 4 4 4 J H 10 8 12 K I,J 8 4 24Using the project evaluation and review technique (PERT) the mean time M and standard deviation (, for the duration of each task are estimated from the most likely (L), optimistic (O), pessimistic (P) estimates by using the formulae M=0.08333(4L+O+P) (=0.08333(P-O) Required: Compute the mean duration and standard deviation for each task (9marks) The project is budgeted to cost Sh.500,000. Actual costs per day are Sh.10,000. By first identifying the critical path from drawing a network diagram can the project be implemented within the budget. (9 marks) What is the probability of finishing the project 4 days earlier than the expected duration? (2 marks) PAPER 3 Time Allowed: 3 hours Answer any THREE questions in SECTION I and TWO questions in SECTION II. Marks allocated to each question are shown at the end of the question. Show all your workings. SECTION I QUESTION ONE How does differential calculus assist managers in their optimization problems when faced with single variables? (4marks) The total cost function and demand function of Migoya Construction company (MCC) are as follows: C = x2 + 16x + 39 Cost in million shillings. P = x2 24x + 117 Price per building in million shillings. x number of buildings Required: Write down the expression of average cost per unit and graph it for x values between x=0 and x=8. (3marks) Write the expression for total revenue. (2marks) If total revenue is to be maximized, what is the price to be charged? (3marks) Elasticity of demand =  EMBED Equation.3 . Determine the elasticity of demand for the quantity that maximizes total revenue. (5marks) What is the price that maximizes profit? (3marks) QUESTION TWO A Markov process describes the movement among the different states of a system as a function of time. Give the various steps in using a Markov process for determining a state at (t + 2) and steady state two periods after given state. (6 marks) A major bank BKR Ltd calculates the credit ratings of its credit card customers on a monthly basis. The ratings are poor, good and excellent depending on the payment history. The following matrix shows how the customers change from one category to the other in one month. To From  EMBED Equation.3  Required: Interpret the elements 0.84, 0.2, 0.18 and 0.16. (4 marks) Given that in August 2003, from customer base of 100,000 the accounts were classified as  EMBED Equation.3  What is expected in October? (10 marks) QUESTION THREE Write short notes on the following: Uniform distribution Poisson distribution Normal distribution Beta distribution t-distribution (10 marks) A performance test was done before and after training on 12 persons appointed in clerical positions in a cement factory warehouse in Athi River. Marks were awarded on a 10-point scale as follows, EmployeeABcDEFGHIJKLBefore training4537865910643After training5468759910654 Did the training improve performance? (10 marks) QUESTION FOUR A sample should reduce as much as possible elements of bias since they deprive statistical result of its representativeness. Define a bias and describe at least four examples of ways that bias is introduced in a sample. (6 marks) A researcher finds that 65% of a random sample of 100 technicians can be classified as highly dissatisfied with their job. With the scale used to measure job satisfaction, the percentage of highly dissatisfied people in all types of occupation has averaged 45%. Can the researcher conclude that a larger proportion of technicians is dissatisfied than is true for all occupations. (Use 5%level of significance) (4 marks) In testing records generated by two sub departments in a large firm, the following were the incorrect records obtained. Number in sampleMeanStandard deviationProduction10457Sales5556 Is the difference in number of incorrect records significant? (10 marks) QUESTION FIVE What is the estimated standard error of a regression equation? (2marks) A land resale firm, Shamba Nyingi Limited, sells land around the environs of Nairobi. The size and price data for a given year are as shown. Size (000 of square meters)Price in Sh. Millions2.34.22.44.53.04.82.64.92.65.33.06.22.77.53.68.52.03.5 Required: Determine the linear-regression equation of price on size. (8marks) Describe the relationship between size and price. (2marks) What do the equation coefficients represent? (3marks) What is the estimated standard error of the regression equation? (2marks) Determine the prize when the size of land is 2,150 square meters. (3marks) SECTION II QUESTION SIX What is sensitivity analysis on the objective function coefficients? Differentiate this with sensitivity analysis on the right hand side (RHS) constants (4 marks) Differentiate linear programming from assignment and transportation problems. (4 marks) In a linear programming problem the objective function and constraints were determined to be as follows. Maximise Z= -x1-x2+3x3-2x4 Profit Subject to :  EMBED Equation.3  Resource A  EMBED Equation.3  Resource B  EMBED Equation.3  Resource C Where xj EMBED Equation.3 0 , j=1,2,3,4 Required: By simplex method find the final table. (7 marks) Give the optimal solution and the value of the corresponding objective function (2 marks) For the variable x2, give an interval for their objective function coefficient such that the present basic solution remains optimal. (3 marks) QUESTION SEVEN The marketing department of EAA Ltd developed a sales forecasting function for its washing powder and those of competitor Hankol Ltd. EAA Ltd has 3 strategies and Hankol Ltd has 4 strategies. The increase and decrease in quarterly sales revenue for the different combinations of strategies of EAA Ltd and Hankol Ltd are as shown in the payoff matrix: Hankol Ltd 123415,000-2,00012,000-5,00026,0002,0007,0006,0003- 2,0000- 4007,000 EAA Ltd Required: Using graphical or otherwise determine the strategy available for EAA to pursue. (5marks) A milk processing plant Oldonyo Kesses Ltd in Rift-Valley desires to determine how many kilograms of butter to produce per day to meet demand. Past records indicate the following patterns of demand: Quantity in KgsNumber of days which demand occurred 15 4 20 16 25 20 30 80 35 40 40 30 45 10 Stock levels are restricted to the range 15 45 kilograms (in multiples of 5 kg.) and butter left is disposed by giving the employees working in the factory at the end of the day. Cost of production is Sh.44 and selling price is Sh.50 per Kg. Required: Construct a payoff table. (10marks) Determine the production alternative that maximises expected profit. (5marks) QUESTION EIGHT In relation to project analysis define the terms: Latest start time. (1mark) Earliest start time. (1mark) Resource leveling. (3marks) A project has 11 activities with the activities preceding constraints, time estimates and work team requirements as shown: ActivityPreceding activity DaysWork team A - 10 5 B A 8 3 C A 5 5 D B 6 4 E D 8 1 F C 7 2 G E,F 4 2 H F 2 1 I F 3 3 J H,I 3 1 K J,G 2 5 Required: Prepare a network and indicate critical path. (6marks) Prepare a time chart and work team requirement. (6marks) Can the work team resource be levelled further? Why? (3marks) Answers - Mocks SUGGESTED ANSWERS TO MOCK EXAMS SOLUTIONS FOR PAPER 1 Question 1 Final demand is the amount of production that is consumed by those outside the interconnected industry. It is an additional production for a closed input-output system. Technical coefficients are the fractions of total units produced by industries that are consumed by interrelated industries. They form the internal demand to make up the input-output matrix. Closed model is the input-output model that have entire production being consumed by those participating in production. Open model on the other hand is one in which some of the production is consumed by external bodies. There is external demand in this case. The Leontief open model is Mx+d=x So rearranging the equation (I-M)x=d x=(I-M)-1d Where M-matrix of technical coefficients x-required production d-the external demand I-Identity matrix Given that M= EMBED Equation.3  d= EMBED Equation.3 Then (I-M)= EMBED Equation.3   EMBED Equation.3  Determinant (I-M) = EMBED Equation.3  = 0.06-0.006+0=0.054 Ad joint (I-M)=Transpose of the co-factors of (I-M) Co-factors of (I-M)=  EMBED Equation.3  So adjoint (I-M)=  EMBED Equation.3  And (I-M)-1= EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  The total output for packaging industry is less than the final demand. The total output of bakery is even negative showing that it requires more input to be able to produce final demand. The total output for flour is much more than the final demand since it supplies a lot of input to the bakery and packaging. Question 2 These terms relate to kurtosis, which is the degree of flatness or peakedness of a frequency curve. Platykurtic means that a frequency curve is less peaked. The kurtosis is less than 3. Leptokurtic on the other hand means that the frequency curve is more peaked. The kurtosis is more than 3 Mesokurtic means that there is intermediate peakedness. This represents the normal curve. Kurtosis is equal to 3. Coeficient of skewness is a measure of skewness (lack of symmetry of a frequency distribution) Coefficient of skewness= EMBED Equation.3  Quatile deviation is a measure of dispersion expressed as follows Quartile deviation= EMBED Equation.3  A table to assist in determination of mean, median, mode and standard deviation is as follows. Gross incomemidpointNumber cumulativexi/1000 f fxifrequency f(xi-x)2 0.5- 5000.5 2.5005 2 5.0010 2 1037.6543 5000.5- 10000.5 7.5005 15 112.5075 17 4740.7407 10000.5- 15000.5 12.5005 25 312.5125 42 4081.7901 15000.5- 20000.5 17.5005 28 490.0140 70 1693.8272 20000.5- 25000.5 22.5005 33 742.5165 103 254.6296 25000.5- 30000.5 27.5005 35 962.5175 138 172.8395 30000.5- 35000.5 32.5005 38 1235.0190 176 1982.0988 35000.5- 45000.5 40.0005 40 1600.0200 216 8669.7531 Total 162.504 216 5460.1080 5838.6120 The arithmetic mean AM EMBED Equation.3  Median determination Q2 frequency= EMBED Equation.3 So Q2 class interval is 25000.5-30000.5 with its frequency being 33. (seen from the above table where Q2 = 108.1 is located). Median= EMBED Equation.3 Shillings Where  EMBED Equation.3 -lower boundary of Q2 class  EMBED Equation.3 -Q2 frequency  EMBED Equation.3 - Frequency of class after Q2 class f-Cumulative frequency at the start of Q2 class  EMBED Equation.3 -class interval of the Q2 class Mode determination The highest frequency is 40, so modal class is 35,000.5-45,000.5 Mode= EMBED Equation.3 Shs. Where  EMBED Equation.3 -lower boundary of modal class  EMBED Equation.3 -frequency of modal class  EMBED Equation.3 - Frequency of class just before modal class f2- Frequency of class just after modal class  EMBED Equation.3 -Interval of the modal class Coefficient of skewness= EMBED Equation.3  Standard deviation = EMBED Equation.3  Since the coefficient is negative it shows the distribution is skewed to higher income earners. Meaning that air-conditioners are usually bought by high income earners. Question 3 Compound event is the simultaneous occurrence of two or more events in connection with each other. It is an aggregate of simple events. The probability that the two events will occur is the joint probability of the events Mutually exclusive events are events that cannot occur simultaneously. Occurrence of one event precludes the occurrence of the other event. Two events A and B are mutually exclusive if they do not have any elementary outcome in common. Collectively exhaustive events exist when no other outcome is possible for a given experiment. That is, they include all possible outcomes. The sum of probabilities is equal to one. Equally likely events are events whereby each outcome is likely to occur the same way the other is likely to occur. That is, one event does not occur more than the other. Equal probability is assigned to each outcome. Conditional probability is the probability of occurrence of an event given that another event has occurred. Number of technicians with salaries under Sh. 5,000 is 30 + 40=70. Total number of technicians is 500. So the probability of wages being below Sh 5,000 is  EMBED Equation.3  Number of technicians with salaries above Sh. 6,000 is 100+60+20=180 So probability of technicians having salaries above Sh 6,000 is  EMBED Equation.3  Number of technicians with salaries between Sh. 5,000 and Sh. 6,000 is 250 So probability of technicians having salaries between Sh. 5,000 and Sh. 6,000 is  EMBED Equation.3  Question 4 Goodness of fit test is a test on how well empirical distribution(obtained from sample data) can fit theoretical distribution (like normal, Poisson or binomial distributions) using the (2 test. Accountants can use it to determine whether a given age-debtors distribution can be approximated by a given function. Also while forecasting past data or surveyed data can be compared with assumed distribution to come up with a conclusion that the distribution function represents the forecast Accountants can also come up with appropriate wage/salary given that a certain distribution exists between staff turnover and salary/wages A table to aid in calculation of distribution and x2 is as follows: No. of peopleObserved Poison  EMBED Equation.3  changing valuesdistribution x O f0 f0x fe 0 8 0.073 0.000 0.039 0.030 1 18 0.164 0.164 0.126 0.012 2 19 0.173 0.345 0.204 0.005 3 20 0.182 0.545 0.222 0.007 4 16 0.145 0.582 0.180 0.007 5 12 0.109 0.545 0.117 0.001 6 8 0.073 0.436 0.064 0.001 7 4 0.036 0.255 0.030 0.002 8 3 0.027 0.218 0.012 0.019 9 2 0.018 0.164 0.004 0.044 Total 110 1 3.255 0.127 Poisson distribution  EMBED Equation.3 and fo =  EMBED Equation.3  Mean  EMBED Equation.3   EMBED Equation.3  so the Poisson distribution fits well for the data. Question 5 Scatter diagram is a plot of a distribution in its ungrouped form on a graph Bivariate distribution is a distribution of two variables Positive correlation occurs when movement of one variable in one direction causes the other variable to move in the same direction Confidence interval is the limit at which a parameter or the linear regression itself is taken to represent a given distribution Autocorrelation occurs when a series errors or disturbance covariance is not equal to zero so the least squares estimated are not the best linear unbiased estimates. A table to aid in calculation of the regression line equation is a s follows YearBusiness turnover Sh millions Staff requirement xy x2 x y1993 45  2,600  117,000  2,025 1994 50  3,000  150,000  2,500 1995 60  3,100  186,000  3,600 1996 75  3,530  264,750  5,625 1997 80  3,850  308,000  6,400 1998 110  4,300  473,000  12,100 1999 150  5,870  880,500  22,500 2000 170  7,150  1,215,500  28,900 Totals 740  33,400  3,594,750  83,650   EMBED Equation.3  Where x-Sh million n- Number of years considered  EMBED Equation.3   EMBED Equation.3  So  EMBED Equation.3  Staff requirement when business turnover is x = Sh 200 million is as follows  EMBED Equation.3  Question 6 The underlying assumptions are: Proportionality/linearity-variables forming the basis of the problem vary in direct proportion with the level of activity. Certainty/deterministic-coefficients in the objective and constraints are known with certainty Divisibility- fractional levels for variables in objective and constraints are allowed. Additive- the total contribution of all activities are identical to the sum of the contribution of each activity taken individually Time factor is ignored b) Let x1, x2 and x3 be the quantities of ordinary chair, baby cot and executive chairs. Z-profit from sale of furniture Objective function  EMBED Equation.3  Constraints:  EMBED Equation.3  cubic meters of wood  EMBED Equation.3  Hours lathe time  EMBED Equation.3  Hours technicians labour  EMBED Equation.3  Non-negativity of variables From the computer solution the units required to maximise profit are: x1= 6 ordinary chairs x2= 20 baby cots x3= 0 executive chairs The maximum profit is Shs. 856 Yes, the time required for technicians is not completely used up. There are 18 hours remaining as slack. For used up resources, one can pay: Wood ( Sh. 1.80 for any additional cubic meter Lathe ( Sh. 2.70 for any additional hour Question 7 N-person game is a game involving n persons Saddle point is that position in the payoff matrix where maximum of row minima is equal to the minimum of column maxima A strategy is the number of competitive actions, choices or alternatives that are available for a player. Rules of dominance are the guidelines that assist to identify an inferior strategy from others. The rule is that if all elements in a column (or row) are greater than/equal to corresponding element in another column (or row) then that column (or row) is eliminated. If Peter expands and Ngomongo expands too, Peter will have additional profit per day of Sh1000, otherwise if Ngomongo does not expand then there will be no additional profit to Peter. In case Peter does not expand and Ngomongo does, then he will loose Sh500 per day, otherwise if Ngomongo does not expand too then he will be getting Sh500 profit. The payoff table is as follows: Ngomongo ExpandDo not expand Row minimay1-yExpandx100000(MaximumDo not expand1-x-500500-500Column minima1000500( MinimumThere is no saddle point since the minimum of colum maxima is not the maximum of row minimum. So mixed strategies will be used to find the optimum solution. The value of the game can be determined as follows x-probability of Peter expanding y-probability of Ngomongo expanding Expected value of the game E(x,y) =1000xy+0(x(1-y)-500(1-x)y+500(1-x)(1-y) =1000xy-500(1-x)y+500(1-x)(1-y) differentiating E(x,y) with respect to x and equating to zero gives  EMBED Equation.3  differentiating E(x,y) with respect to y and equating to zero gives  EMBED Equation.3  The probability of Peter expanding x=0.5 and for not expanding 1-x=0.5 The probability of Ngomongo expanding y=0.25 and not expanding 1-y=0.75 The value of the game when Peter and Ngomongo play the strategies as shown is obtained as follows. Solving the values for x and y in the expected value equation E(x,y) =1000(0.5(0.25-500((1-0.5)(0.25+500((1-0.5)(1-0.25) = Sh 375 Question 8 The network diagram is as follows  The activity durations in brackets are the crash times. The event times on top and below the events are the event times obtained from crash times. Critical path is A-B-E-I The minimum time is 45 weeks. The minimum cost is determined by crashing all the activities that there is a cost reduction in the crashing. These are identified as activity E,K,L, and M. Total cost for the project without crashing is Sh 185,000. Crashing E,K,L, and M means that the cost will reduce from 185000 to (185000-(1(500+2(1000+2(2000+1000(2)=176500 For the activities the ratio  EMBED Equation.3   EMBED Equation.3 is given as Activity Ratio r A 666.7 B 1000 C 1000 D 0 E 500 F 0 G 0 H 10000 I 0 J 3000 K -1000 L -2000 M -1000 The cost schedule for the activities is as follows CommentDurationDirect costOpportunity costTotal costNormal time 51 185000 11000 196000Crash K,L,M 51 177000 11000 188000Crash E 50 176500 10000 186500Compress A 49 177166.7 9000 186166.7Compress A 48 177833.4 8000 185833.4Crash A 47 178500.1 7000 185500.1Compress B 46 179500.1 6000 185500.1Crash B 45 180500.1 5000 185500.1 Yes it is advisable to crash the project to 45 weeks since the project will be running at minimum cost of Sh.185500.1 in addition to the reduced time. Paper 2 Question 1 Cost function=30x+3000 Revenue function=50x Profit=revenue-cost=50x-30x-30000=20x-30000 Where x - number of bar soaps.  EMBED Excel.Chart.8  Break-even point is where profit is equal to zero or where cost function line crosses the revenue function line. From the graph the break-even point is x=1500 bar soaps. From the graph, selling 2500 bar soaps the profit will be KSh 20,000 Revenue is maximized when the first partial derivatives of revenue are equated to zero  EMBED Equation.3  (1)  EMBED Equation.3   EMBED Equation.3  (2)  EMBED Equation.3  Adding equation (1) and (2) -2y=-13(y=6.5 000 and x=2.5 000  EMBED Equation.3  Sampling is a process of examining a representative number of items out of a whole population. Systematic sampling involves selecting every nth item after selecting the first item randomly. Stratified sampling involves taking random samples from within a group in the population that each group bears to the population as a whole. Multi-stage sampling is similar to stratified sampling except that the groups are geographically based. Cluster sampling involves selecting a few areas at random and every single item in the area is interviewed. Quota sampling involves choosing on the spot up to a given quota. An interviewer selects interviewee from given categories up to a given quota. Question 2 Markov process is a sequence of events in which the probability of occurrence for one event depends upon the preceding event. It is time-based process. For example a patients state today depends on previous days state Cyclic chain is one that repeats itself in a deterministic manner. The transition matrix has ones in two or more rows that form a closed path among cycle states. Example is a machine operation that repeats itself Absorbing state is one that cannot be left once entered. It has a transition probability of one to itself and zero to other states. Example includes the payment of a bill, sale of a capital asset or termination of an employee State transition matrix is a rectangular array that summarises the transition probabilities for a given Markov process. Transition probabilities are the probabilities of occurrence of each event depending on the state of the generator Steady state is the condition that in the long run period of time a system settles or stabilizes to. February 2002 market share = January 2002 market share(Transition matrix  EMBED Equation.3  EMBED Equation.3   EMBED Equation.3  (CU CG AF)  EMBED Equation.3  For March 2002 market share = February market share( Transition matrix  EMBED Equation.3  EMBED Equation.3   EMBED Equation.3  (CU CG AF)  EMBED Equation.3  Steady state Situation At steady state XT=X Taking CU - x, CG - y and AF - Z  EMBED Equation.3  And x+y+z=1( x=1-y-z (1) From the matrix multiplication 0.5x+0.2y+0.1z=x (2) 0.4x+0.7y+0.22z=x (3) Substituting equation (1) into equations (2) and (3) 0.5-0.5y-0.5z+0.2y+0.1z=1-y-z 0.7y+0.6z=0.5 (4) 0.4-0.4y-0.4z+0.7y+0.2z=y 0.7y+0.2z=0.4 (5) Solving equations (4) and (5) simultaneously 0.4z=0.1(z=0.25  EMBED Equation.3  x=1-0.5.025=0.25 Steady state  EMBED Equation.3  Question 3 Price relative is an index number that compares prices of a given commodity for a given period in relation to its price in another period. Fixed base method compares prices of a commodity for a given period in relation to price of the commodity in a base period. In chain base method, the base is the price of the previous period. It is not fixed to a particular period Weighted index number is one that appropriate weights have been added to reflect relative importance of commodities. This makes index numbers be reliable. Quantity index number is one that shows the change from one period to the other of quantities involved. The arithmetic mean of price is = EMBED Equation.3  Geometric mean = EMBED Equation.3  Let P1- selling price in period 1 P0- selling price in base period Then  EMBED Equation.3  And  EMBED Equation.3   EMBED Equation.3  So price change =  EMBED Equation.3  The percentage increase in selling price is 15.6% Question 4 A Venn diagram is a simple diagrammatic representation of a well defined list, collection or class of objects. (Set) It can be used to determine probability of a given event when a sub set is taken out of a set. The ratio of the subset to the set gives probability of the given subset i) Let A- the set employees with salary increment =300 B-the set employees promoted =100 U-universal set =1,500 Given that  EMBED Equation.3  Then  EMBED Equation.3  Read as B intersection not A = B minus A Intersection B( The employees who were only promoted. So probability = EMBED Equation.3  ii) And  EMBED Equation.3  The employees who did not get promoted or increment. So probability= EMBED Equation.3  A random variable is a numerically valued function defining a set of all possible outcomes of an experiment that depends on chance a) This is a case of binomial distribution Probability of defective q=0.05 Probability of no defective p=1-0.05=0.95 So probability of no defective given that n=50 is as follows P0= EMBED Equation.3 where i=0  EMBED Equation.3  b) Probability of not more than 4 being defective= Probability of at least having 4 defectives P4 =(P0+P1+P2+P3+P4) = ( EMBED Equation.3 ) = EMBED Equation.3  = EMBED Equation.3  = EMBED Equation.3  =0.896 = probability of meeting guaranteed quality Question 5 Quantitative techniques are the mathematical techniques used to analyse past data to come up with past patterns, so as to guide about the future. These include moving average, exponential smoothing and time series decomposition. Moving averages method removes cyclical movements by getting averages of past data. The average can be weighted or un-weighted. The averages are changed with additional data. Exponential smoothing on the other hand uses weights that decrease exponentially with time. The new forecast uses the previous forecast and a proportion of the forecast error (difference between current observation and previous forecast. The proportion is the smoothing constant. Time series decomposition involves deseasonalising data by use of moving averages then fixing a relationship that can be used to predict the future Qualitative techniques use other techniques like judgment, intuition, experience and flare. It is used where past data is not available and where long term forecast is required. These methods include Delphi method, market research and historical analogy. Delphi method is a technique designed to obtain expert consensus for a particular forecast. Experts independently answer a sequence of questionnaires in which responses of one questionnaire is used to produce the next questionnaire. So information is passed to other experts whereby judgments are refined with more information and experience. Market research involves the surveys of opinions, market data analysis and questionnaires designed to get reaction of market to a particular product or design. This is mainly important in short term. Historical analogy involves looking at data of a similar product being studied. The similar product is analysed through its lifecycle. Time series decomposition is the breaking down of given data to come up with a given relationship that can predict the future. The various characteristics (trend, seasonal variation, cyclical variation and residual variation) are separated. The two ways of forecasting are by use of multiplicative and additive models. In multiplicative model the steps involved include the following Deseasonalise the data by separating of trend and seasonal variation (using moving averages) Calculate the trend using least squares method Estimate the dependent variables from the regression formula Calculate the percentage variation of the dependent variable in each period Average this percentage variation of dependent variable to give average seasonal variation Forecast is made by multiplying trend with the percentage variation Plotting these values on a scatter diagram gives the following  EMBED Excel.Chart.8  Because the variations from the trend are increasing with time, the multiplicative model is appropriate. First step is to deseasonalise the data using a three months moving average MonthProduction A3pt moving average TRatio A/T=S(ESeasonal SDe-season A/S=T(ETrend T=7.18-.085(xxx2xyJan-017.920.94348.407.095118.3950Feb-017.817.8800  0.9911 0.97847.987.012415.9643Mar-017.91 7.6200 1.0381 1.05197.526.9253922.5593Apr-017.037.4000  0.9500 0.99377.076.8441628.2989May-017.25 7.4467  0.9736 1.05196.896.75552534.4602Jun-017.176.7000 1.0701 1.17706.096.6763636.5519Jul-015.01 5.6100  0.8930 1.04224.816.58574933.6503Aug-013.9 6.1533  0.6338 0.62986.196.586449.5431Sep-016.64 7.1967  0.9226 0.91697.246.41598165.1771Oct-017.037.2767  0.9661 1.02866.836.331010068.3426Nov-016.88 6.9800  0.9857 1.09896.266.2451112168.8689Dec-016.14 6.3067  0.9736 1.08735.656.161214467.7633Jan-024.86 5.7533 0.8447 0.94345.156.0751316966.9694Feb-024.485.8867  0.7610 0.97844.585.991419664.1027Mar-025.26 6.2200  0.8457 1.05195.005.9051522575.0075Apr-025.48 6.6067  0.8295 0.99375.515.821625688.2378May-026.427.0533  0.9102 1.05196.105.73517289103.7512Jun-026.826.5733 1.0375 1.17705.795.6518324104.303Jul-024.98 5.1167  0.9733 1.04224.785.5651936190.7896Aug-022.454.9600  0.4940 0.62983.895.482040077.8081Sep-024.516.2700  0.7193 0.91694.925.39521441103.2952Oct-026.38 7.2833  0.8760 1.02866.205.3122484136.4519Nov-027.557.6867 0.9822 1.09896.875.22523529158.0217Dec-027.591.08736.985.1424576167.5321Total146.7230049001735.845 The seasonal component S is determined as follows JanFebMarAprMayJunJulAugSepOctNovDecTotal20010.99111.03810.95000.97361.07010.89300.63380.92260.96610.98570.973620020.84470.76100.84570.82950.91021.03750.97330.49400.71930.87600.9822Ave S0.84470.87610.94190.88970.94191.05380.93320.56390.82100.92100.98390.973610.7447AdjS0.94340.97841.05190.99371.05191.17701.04220.62980.91691.02861.09891.087312.0000 From the deseasonalised data, using least square method, the trend  EMBED Equation.3  Let x = month y = deseasonalised data Where  EMBED Equation.3   EMBED Equation.3  So that  EMBED Equation.3  in 000 The final graph raw data, deseasonalised data and trend will look as follows: NOTES: To get 3 point moving average For example for Feb `01 =  EMBED Equation.3  Mar `01 = EMBED Equation.3  A/T = S(E = seasonal factor ( error term To deseasonalise the data, the seasonal component has to be determined. From the given data, a multiplicative model is appropriate. A three-month moving average is chosen since centering is not required. The ratio A/T=S(E (A-actual data, T-trend, S-seasonal factor and E-error term) is then determined by dividing the actual data by the moving average data. The A/T ratio is then placed for each month and year as per the second table to average out the seasonal factor. Since the total of the averages does not equal to 12 (the number of months or seasons), adjustment are done by a factor of 12/ total average=12/10.7447 The seasonal factor is then used to deseasonalise the actual data by dividing the actual data A by seasonal factor S. The trend from calculations is then determined by using the deseasonalised data (which is taken to be y while x is taken to be the time in months) x2 and x(y can then be determined to assist in coming up with the trend equation. Forecast is made of the trend ( seasonal factor. So for May 2003, the trend is determined and multiplied by seasonal factor of May (1.051) to obtain the forecast. Notice: The trend values given are not required. Also the graph is not required. They have just been reproduced to show the movement from raw data to the trend values. Critical path is B-D-E-G-H-J-K  EMBED Excel.Chart.8  The production forecast of May 2003 (month=29) is:  EMBED Equation.3 Units Question 6 A dual problem is a linear programming problem of maximisation (or minimisation) that is unique to a minimisation (or maximisation) problem (which is the primal). The dual uses the same data and numerical values of objective function and gives the same solution as the primal Let x1,x2,x3 be the 5 litre quantity of lemonade, grapefruit and orangeade and z be the revenue to be maximised. Objective function  EMBED Equation.3  EMBED Equation.3  maximise turnover Constraints  EMBED Equation.3  Dozen of lemons  EMBED Equation.3  Kg of grapefruit  EMBED Equation.3  Dozen of oranges  EMBED Equation.3 Kg of sugar  EMBED Equation.3 ounces of citric acid  EMBED Equation.3  Non-negativity of variables Dual of the problem is as follows Let y1,y2,y3 y4 y5 be the quantities of lemon, grapefruit, oranges, sugar and citric acid respectively. Objective function  EMBED Equation.3  minimise usage Constraints  EMBED Equation.3  price of 5 litre lemonade  EMBED Equation.3  price of 5 litre grapefruit  EMBED Equation.3  price of 5 litre orangeade  EMBED Equation.3  Non-negativity of variables The quantities to be manufactured in May 2002 that maximise turnover are: 250 (5 litres) lemonade 1333.33 (5 litres) grapefruit 500 (5 litres) orangeade All are read from the dual prices of the dual problem. The maximum turnover is Sh. 83958.30 The resources not used up are read from the reduced gradient. It shows that there is slack in lemons of 2000 dozen and sugar of 1750 kg. Question 7 Laplace criterion is a decision rule that expected payoff is calculated where each alternative is assigned equal probabilities. The best alternative is that with the highest expected payoff. Hurwicz criterion on the other hand is a decision rule where there is a weighted average of the best and worst payoffs of each alternative. Laplace criterion is different from Hurwicz criterion because of the equal probabilities and the other uses an average depending on the decision makers view of risk Payoffs table in Million shillings for the various price strategies is follows i) Variable costHighMost likelyLow190017001400 Client fee360026.77529.92534.65400027.562530.187534.125440026.2528.3531.5The amounts of payoffs in the table are determined from the expression: Contribution per client per day(50(350(occupancy level e.g. for a charge of Sh.3,600 and variable cost of 1,700, the payoff = (3600-7700) ( 50 ( 350 ( 90% =Sh.29,925,000 Maximax decision rule- maximize the maximum payoffs Maximum payoffClient fee360034.65(Best strategy400034.125440031.5 Maximin decision rule- maximize the minimum payoff Minimum payoffClient fee360026.775400027.5625(Best strategy440026.25 Regret table is as follows in million shillings. The strategy that minimises the maximum regrets is the best. Variable costHighMost likelyLowMaximum190017001400 Client fee36000.78750.262500.78754000000.5250.525(Best strategy44001.31251.83753.153.15 For example regret for a charge of Sh.4,000 and variable cost of Sh.1,400 = 34.65 - 34.125 = 0.525 million shillings Question 8 ActivityLOPMSDSD2A6483.00.33330.1111B24204013.01.66662.77756C108246.01.33331.77764D84124.00.66660.44441E6663.000F86104.00.33330.1111G20163611.01.66662.77756H6483.00.33330.1111I4442.000J108125.00.33330.1111K84245.01.66662.77756Total10.9991  Since the project is expected to take 44 days then the actual cost is 44 days ( Sh10,000=Sh440,000 which is within the budget The probability of the project being completed 4 days before expected completion time (that is 40 days) is determined as follows  EMBED Equation.3  From normal distribution table at z=1.21 the probability is 0.5-0.3869=0.1131 11.31% Paper 3 Question 1 When faced with single variable the differentiation of a function of cost, revenue or profit will give optimum conditions when equated to zero. Depending on what is to be optimized a manager can be able to obtain the quantity of variable that will optimize a given function. For example, a fund manager can check behaviour of a given stock and determine the optimum time to purchase the stock, by differential calculus. Average cost AC= EMBED Equation.3  The graph of cost vs. units is as follows  EMBED Excel.Chart.8  Total revenue TR =Px =(x2-24x+117)x =x3-24x2+117x  EMBED Equation.3  Solving for x  EMBED Equation.3  When x=13 TR=133-24(132+117(13=-338 When x=3 TR=33-24(32+117(3=162 So the quantity that maximizes revenue is x=3. The price to be charged then is P=32-24(3+117=54 million shillings At x=3  EMBED Equation.3   EMBED Equation.3  So elasticity of demand = EMBED Equation.3  Profit =TR-TC =x3-24x2+17x-(x2+16x+39) =x3-25x2+101x-39 To maximize profit the derivative of profit with respect to x is equal to zero  EMBED Equation.3  Solving for x  EMBED Equation.3  When x=14.31 Profit=14.313-25(14.312+101(14.31-39=-782.7 million shillings When x=2.35 Profit=2.353-25(2.352+101(2.35-39=73.27 million shillings So the price that maximizes profit is found by substituting x=2.35 into the price equation P=2.352-24(2.35+117=66.1225 million shillings. Question 2 First probabilities associated with retentions, gains and losses are determined. From this a state transition matrix is drawn. Secondly using the state transition matrix obtained and the current state, the future state in one period (t+1)is obtained as follows State(t+1)=State t((State transition matrix) Thirdly using the state(t+1) and state transition matrix the state(t+2)is obtained as follows State(t+2)=State(t+1)((State transition matrix) Lastly to obtain the steady state the following is solved for the unknowns in the matrix algebra as follows X((transition matrix)=X (1) Sum of the vector elements=1 (2) Where x is the starting state. The elements mean the following 0.84- of those debtors who were rated as excellent, 84% remained exclent the following month 0.2- of those debtors who were rated as good, 20% were rated s poor the following month 0.18-of those debtors who were rated as poor, 18% were rated as good the following month 0.16-of those who had been rated as excellent 16% were rated as good the following month September make-up=August make-up(transition matrix  EMBED Equation.3  EMBED Equation.3  EMBED Equation.3  October make-up=September make-up(transition matrix  EMBED Equation.3  EMBED Equation.3  EMBED Equation.3  So the ratings are Poor-36420 Good-43879 Excelent-25821 Question 3 Probability distributions are approximations of observed frequency distributions Uniform distribution- here a random variable takes any value in a continuous interval, say a and b, in such a way that no value is more likely than the other. Example- a contractor footing from Kibera takes 20 to 30 minutes to walk to work in industrial area.  Mean EMBED Equation.3  Varaince= EMBED Equation.3  Probability P(c0.45 Larger proportion of technicians is dissatisfied than is true for the whole population The test statistic is the standardized normal distribution  EMBED Equation.3  Where:  EMBED Equation.3  - Sample proportion of dissatisfied P - Population proportion of dissatisfied n - number of sample q - population proportion of satisfied = 1-P Since calculated z=4.02> z5%=1.645 from normal distribution tables the null hypothesis is rejected and the conclusion is that a larger proportion of technicians is dissatisfied than is true for the whole population. H0- EMBED Equation.3  There is a no significant difference in number of incorrect records H1- EMBED Equation.3  There is a significant difference in number of incorrect records (1-production mean of incorrect records (2-sales mean of incorrect records First it is necessary to know whether the population variances are equal H0- EMBED Equation.3  The population variances are equal H1- EMBED Equation.3  The population variances are not equal  EMBED Equation.3  Given that n1=10 n2=5 s1=7 s2=6 Since calculated F=1.2099< F5%,9,4=5.999 from F-distribution tables, then the null hypothesis is accepted that the variance are equal. 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    PAGE iv   PAGE iii   Strathmore University  Quantitative Techniques Powered By:  HYPERLINK "http://WWW.MANYAMFRANCHISE.COM" Www.Manyamfranchise.Com PAGE vi General Examination Techniques  Introduction  PAGE ix  PAGE viii Syllabus  PAGE x Topical guide to Past Paper Questions  PAGE 4 Questions Past Papers  Revision Questions and Answers  PAGE 5  PAGE 110 Answers Past Papers  PAGE 124 Questions - Mocks  PAGE 153 Comprehensive Mock Examinations   Quantitative Techniques PAGE 154 Answers - Mocks  PAGE 156 Normal Distribution  PAGE 161 Statistical Tables  PAGE 160 F Distribution  20 14 40 40 45 45 39 12 46 16 43 16 48 20 2 (1) 5 (4) 4 (4) 25 (25) 35 (33) 2 (2) Start  EMBED Equation.3  H C D B E F G 42 1/4 G 1/2 BBG 1/32 ( ( B BGB ( ( 1/2 3/4 3/32 G B 1/4 1/4 B G BGG Finish H G F D E B A where: aij - optimistic time bij - pessimistic time mij - most likely time 10 46 12 44 44 50 50 39 4 I 43 8 0 0 0 0 45 45 51 51 4 (2) 12 (10) 10 (7) 8 (6) 8 (4) L M K GBB 9/32 ( ( B 3/4 3/4 G 1/2 G 1/4 B Group1 GBG 1/2 GGB G 3/4 B Group2 1/4 G Group3 GGG  EMBED Equation.3  J A Peter B 3/4 BBB t(p,df) J A f(x) km ( x Required area Macharia Kungu Thuo Waithera Mwanzia Simiyu Barasa Juki Universal set D C A f(x) hours ( x Required area Diagram 0.85 N 0.10 S 0.05 L 0.9 T 0.1  EMBED Equation.3  0.2 T 0.5 T 0.04 0.05 0.05 0.01 0.085 0.765 0.5  EMBED Equation.3  0.8  EMBED Equation.3  f(x) ( Z(1.65 H1 area Diagram 1.96 H1 area f(x) ( 1.96 H1 area I III II IV 1 2 3 4 5 6 7 500 400 400 800 500 300 100 400 700 800 75 75 -40 -60 -50 Demand Supply Trans-shipment A B C D E F 0 4 0 5 0 3 0 1 0 2 3 1 2 4 5 6 6 5 10 5 8 9 2 4 100 150 -125 -125 -250 Demand Supply 200 Key Node Thika Naivasha Machakos Nairobi Mombasa Kisumu b d c a e Maximization line parallel to bc x2 x1 x2 x1 x2 x1 ( 0 5 5 0 10 10 1 2 A C D B E F G H 5 6 1 1 3 1 2 8 0 0 3 11 5 5 11 11 12 12 14 14 22 22 A 3 0 0 3 11 EET LET EET LET Activity Duration Event 1 Event 2 Key  Activity duration Activity latest start Activity slack Key 4 8 16 12 24 20 C E F G H A B D Duration in weeks 0 Activities Without affecting project completion. C A C D B E F G H 5.5 6.7 1.3 1.2 2.8 1 2 8 0 0 2.8 11.2 13.5 13.5 15.5 11.2 23.5 23.5 12.2 12.2 5.5 5.5 0.69 1.78 0.03 0.11 0.44 0.03 0 0.25 A C D B E F G H 3 3 0.5 0.5 1 0.7 2 6 0 0 1 5.8 3 3 6 6 6.5 6.5 7.5 7.5 13.5 13.5 4 B C F A D E I H 7 30 7 4 10 12 25 11 0 0 11 14 7 7 37 37 23 26 29 33 58 58 G 15 J 6 L 25 K 21  Activity latest start 8 16 32 24 48 40 A D K B C E Duration in weeks 0 F G H I J L 56 Activity duration U Activity Slack Key A B D C F E G H 3 3 7 7 5 6 10 8 0 0 5 5 3 4 11 11 14 14 22 22 A B C D E F 3 (5) 4 (7) 3 (4) 5 (6) 2 (3) 1 (2) 8 8 14 14 0 0 0 0 3 3 5 5 5 5 6 6 7 7 12 12 A B D C F E G H 7 2 2 3 4 3 10 3 0 0 4 6 7 7 9 9 14 14 11 11 Activity duration Activity latest start Key 2 4 8 6 12 10 B E H A Duration in weeks 0 G C F D 14 A and B B and D E F and G H and C 6 workers Workers schedule Activities Activity slack 10 15 10 10 7 7 I 1 (1) C 4 (3) 20 a b 30 1/10 Frequency Time Area=1 f(x) x Bell shaped f(x) x a m b f(x) x 0 df1 df2 ( Critical path A-B-D-E-G-K 8 2 4 2 3 3 10 5 7 E C F B D K G J H 38 38 36 36 33 25 33 25 32 32 30 22 18 18 23 15 10 10 0 0 8 I A 6  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3  0 z U A F 5 5 2 11 3 I K J H G 44 44 39 39 Critical path B-D-E-G-H-J-K 13 3 6 E A C B D 34 34 31 31 20 20 13 13 14 3 0 0 3 6 11 17 24 24 HPX`hpx\kd΢ $$If7064abFf $$Ifa$X_`ghopwxŔƔ͔ΔՔ֔ݔޔ %&-.56=>EFGIJQRYZabijqryzh*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJPJQJ^Jh*OJQJOƔΔ֔ޔ&.6>FGJRZbjrzFf $$Ifa$•ɕʕѕҕٕڕ&'./67>?FGNOVW^_fgnovw~h*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJQJh*OJPJQJ^JO•ʕҕڕ'/7?GOW_Ff $$Ifa$_gowĖ̖Ԗܖ Ff Ff $$Ifa$ÖĖ˖̖ӖԖۖܖ   69:DELMNOPQRSTUVWXYZ[\]^_Ƽ⛌h*5OJPJQJ\^Jh*5OJQJ\*jh*CJOJQJUmHnHsH uh*CJOJQJh*CJOJQJaJh*B* CJOJQJaJphh*CJOJQJh*OJPJQJ^Jh*OJQJ79;<=>?@ABCDEMOQSUWY[]_behk $$Ifa$$a$_abdeghjkmnpqstvwz{~ɗʗҗӗۗܗ #$,-./078?@GHOPWX_`ghoh*OJPJQJ^Jh*OJQJh*CJOJQJh*5OJPJQJ\^Jh*5OJQJ\Oknqtw{ʗӗܗ $-Ff $$Ifa$-.08@HPX`hpxȘɘ˘ӘژFf $$Ifa$Ff opwxǘȘɘʘ˘ҘӘ٘ژ   &'-.45;<BCIJPQRSTZ[abhiopvw}~h*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJQJh*OJPJQJ^JO  '.5<CJQRT[bipw~Ff $$Ifa$~Ùęʙ˙љҙؙٙڙۙܙ  !"()/067=>DEKLRSYZ`abcdeflmstz{h*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJPJQJ^Jh*OJQJOę˙ҙٙڙܙ ")07>ELSZFf $$Ifa$Zabcdfmt{\kd{ $$If7064abFf $$Ifa$ǚȚΚϚ՚֚ܚݚ  %&,-34:;ABHIOPVW]^deklrstuv|}h*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJQJh*OJPJQJ^JOȚϚ֚ݚ &-4;BIPW^elsFf7 $$Ifa$stv}Ûʛћ؛ߛ Ff= $$Ifa$Ff: ›ÛɛʛЛћכ؛ޛߛ   !'(./56<=CDJKQRXY_`fgmntu{|h*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJPJQJ^Jh*OJQJO!(/6=DKRY`gnu|Ɯ͜ԜFf@ $$Ifa$ŜƜ̜͜ӜԜڜۜ    &'-.45;<BCIJPQWX^_eflmstz{h*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJQJh*OJPJQJ^JOԜۜ   '.5\kd $$If7064abFfC $$Ifa$5<CJQX_fmt{ŝ̝ӝڝFf $$Ifa$ĝŝ˝̝ҝӝٝڝ   !#$*+1289?@FGMNTU[\bcijpqwx~h*OJQJh*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJPJQJ^JO  !$+29@GNU\cjqxFf $$Ifa$žȞɞϞО֞מݞޞ#$*+12356<=CDJKQRXY_`fgmntu{|h*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJPJQJ^Jh*OJQJOžɞОמޞ$+236=DKRFf $$Ifa$Ff RY`gnu|Ff $$Ifa$ǟȟΟϟ՟֟ܟݟ "#)*0178>?EFGIJPQWX^_eflmstz{h*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJQJh*OJPJQJ^JOȟϟ֟ݟ#* $$Ifa$\kd $$If7064ab*18?FGJQX_fmt{ȠϠРӠFfv Ffs $$Ifa$ǠȠΠϠРҠӠ٠ڠ   &'-.45;<BCIJPQWXY[\bcijpqwx~h*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJPJQJ^Jh*OJQJOӠڠ  '.5<CJQXY\cjqxFfy $$Ifa$š̡ӡڡ$+29@GFf| $$Ifa$ġšˡ̡ҡӡ١ڡ#$*+1289?@FGMNTU[\bcijklmopvw}~h*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJQJh*OJPJQJ^JOGNU\cjklmpw~\kdU" $$If7064abFf $$Ifa$ĢˢҢ٢#*18?FMT[bFf$ $$Ifa$âĢʢˢѢҢآ٢ߢ "#)*0178>?EFLMSTZ[abhiopvw}~h*5OJPJQJ\^Jh*5OJQJ\h*CJOJQJh*OJPJQJ^Jh*OJQJObipw~ȣϣ֣ݣ Ff. 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Equation.39q|8mI4yI P=) 932Equation Native |_1086544222WFȼFOle CompObjVXfObjInfoYEquation Native _1086544558Pd\FFFOle  +) 332 +) 132 =) 1332 FMicrosoft Equation 3.0 DS Equation Equation.39qԠ8mI4yI PAB()PA()=) 750 CompObj[]fObjInfo^Equation Native _1086544783aFFF) 1250 =) 712 FMicrosoft Equation 3.0 DS Equation Equation.39qԨII PAB()PA()=) 1250 ) 2050 =) 1220Ole CompObj`bfObjInfocEquation Native I FMicrosoft Equation 3.0 DS Equation Equation.39q`8mI4yI PAB()=PA()PB/A()_1086544930_ifFFOle CompObjegfObjInfohEquation Native |_1086545039kFOle CompObjjlf FMicrosoft Equation 3.0 DS Equation Equation.39qDI4I PB/A()=PB() FMicrosoft Equation 3.0 DS Equation Equation.39qObjInfomEquation Native `_1086545090 pF>Ole CompObjoqfObjInforEquation Native _1086546260x}uF>>Ԝ,IsI PAB()=PA()PB()=) 824 0.8=0.267 FMicrosoft Equation 3.0 DS Equation Equation.39qOle CompObjtvfObjInfowEquation Native dII PG()=PGH()+PG2H() FMicrosoft Equation 3.0 DS Equation Equation.39qԐIdI =PH()PG/H()+P2H()_1086546249zF> eOle CompObjy{fObjInfo|Equation Native _1086546272F e eOle CompObj~fPG/2H() FMicrosoft Equation 3.0 DS Equation Equation.39qhII =0.50.3+0.5) 34 =0.525ObjInfoEquation Native _1101543741FF e0Ole  FMicrosoft Equation 3.0 DS Equation Equation.39q3IP}I H FMicrosoft Equation 3.0 DS Equation Equation.39qCompObjfObjInfoEquation Native 0_1086546312sF00Ole CompObjfObjInfoEquation Native ԬI`I PH/G()=PGH()PG()=0.30.525=0.571 FMicrosoft Equation 3.0 DS Equation Equation.39q_1086546557F0@Ole CompObjfObjInfo\8mI4yI PG/D()=P(GDPD() FMicrosoft Equation 3.0 DS Equation Equation.39qtInI PD()=Equation Native x_1086546656F@@Ole CompObjfObjInfoEquation Native 4_1086546805F@PڽOle     #&)*-0369<?BCDGJMRUVY\_dilmpstwz{|}PE()PD/E()+PF()PD/F()+PG()PD/G()+PH()PD/H() FMicrosoft Equation 3.0 DS Equation Equation.39qCompObjfObjInfoEquation Native _1086546929FPڽPڽԜ IlI =0.30.05+0.250.1+0.350.15+0.10.2x FMicrosoft Equation 3.0 DS Equation Equation.39qd II PGD()Ole  CompObj fObjInfoEquation Native =PG()PD/G()A FMicrosoft 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P("A/"I)=P("A"I)P("I)=0.7860.8=0.9825 FMicrosoft Equation 3.0 DS Equation Equation.39q_1101548435FOle ECompObjFfObjInfoHn\4()=1"P0()+P1()+P2()+P3()+Ole CompObjfObjInfoEquation Native P4()()# FMicrosoft Equation 3.0 DS Equation Equation.39qԠ4II =1", 10 C 0 p 0 q 10 +, 10 C 1 p 1 q_1086550771FOle CompObjfObjInfoEquation Native _1086550781 FOle CompObj  f 9 +, 10 C 2 p 2 q 8 +, 10 C 3 p 3 q 7 +, 10 C 4 p 4 q 6 () FMicrosoft Equation 3.0 DS Equation Equation.39qObjInfo Equation Native _1094904368FOle pPII =1"q 10 +10!1!9!p 0 q 9 +10!2!8!p 2 q 8 +10!3!7!p 3 q 7 +10!4!6!p 4 q 6 ()CompObjfObjInfoEquation Native _1086551194F FMicrosoft Equation 3.0 DS Equation Equation.39qԐIԂI =1"0.52 10 +100.480.52 9 +450.48 2 0.52 8 +1200.48 3 0.52 7 +2100.48 4 0.52 6 () FMicrosoft Equation 3.0 DS Equation Equation.39q@xIpI =1"0.427=0.57Ole CompObjfObjInfoEquation Native \_1086551442F ֿ ֿOle CompObjfObjInfo FMicrosoft Equation 3.0 DS Equation Equation.39q48mI4yI =36500 km FMicrosoft Equation 3.0 DS 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Equation.39qԀ8mI4yI P=)1560+)760=)2260=)ObjInfo]Equation Native _1086556493"`FOle 1130 FMicrosoft Equation 3.0 DS Equation Equation.39q`II P=)1060+)760=)2760CompObj_afObjInfobEquation Native |_1086556632eFOle CompObjdf fObjInfog"Equation Native #, FMicrosoft Equation 3.0 DS Equation Equation.39q8mI4yI   FMicrosoft Equation 3.0 DS Equation Equation.39q_1086556674cmjFOle $CompObjik%fObjInfol'Equation Native (_1086556918oF5Ole +CompObjnp,fpIPI )1560)315=)360=)120 FMicrosoft Equation 3.0 DS Equation Equation.39q88mI4yI P(A/B)=ObjInfoq.Equation Native /T_1086557368htF55Ole 1P(A) FMicrosoft Equation 3.0 DS Equation Equation.39ql8mI4yI PA/B()=PAB()PB()!CompObjsu2fObjInfov4Equation Native 5_1086558340yF5\Ole 8CompObjxz9fObjInfo{;Equation Native < FMicrosoft Equation 3.0 DS Equation Equation.39qxII Z=x"=600"800160="1.25_1104571821<%~F\\Ole ?CompObj}@fObjInfoB FMicrosoft Equation 3.0 DS Equation Equation.39qtII Px<600()=0.5"0.3944=1056% FMicrosoft Equation 3.0 DS EqEquation Native C_1086558471wF\\Ole FCompObjGfuation Equation.39qLII Px<x g ()=0.01 FMicrosoft Equation 3.0 DS Equation Equation.39qObjInfoIEquation Native Jh_1086558515FЃЃOle LCompObjMfObjInfoOEquation Native P_1086558569rFЃЃԈ IĢI Z=x g "=x g "800160=2.33 FMicrosoft Equation 3.0 DS Equation Equation.39qԄ|}IHyI !x g =1Ole SCompObjTfObjInfoVEquation Native W602.33+800=1172.8H"1173 FMicrosoft Equation 3.0 DS Equation Equation.39q$,InI 2x=850_1086559024FOle ZCompObj[fObjInfo]Equation Native ^@_1086558870FOle _CompObj`f FMicrosoft Equation 3.0 DS Equation Equation.39qԴII Z=2x") n  =850"800)160 25  =1.5625ObjInfobEquation Native c_1086559119FOle gCompObjhfObjInfojEquation Native k_1086559991F FMicrosoft Equation 3.0 DS Equation Equation.39q|II P2x>850()=0.5"0.4406=0.0594 FMicrosoft Equation 3.0 DS EqOle nCompObjofObjInfoqEquation Native ruation Equation.39q԰DyIPI Punsatisfactoryd"2()=P0()+P1()+P2() FMicrosoft Equation 3.0 DS Eq_1086559655FOle vCompObjwfObjInfoyuation Equation.39qII =, 10 C 0 p 0 q 10 +, 10 C 1 p 1 q 9 +, 10 C 2 p 2 q 8 ()Equation Native z_1086559857FOle CompObjf FMicrosoft Equation 3.0 DS Equation Equation.39qII =q 10 +10!1!9!p 0 q 9 +10!2!8!p 2 q 8 ()ObjInfoEquation Native _1086559905F ¦ ¦Ole CompObjfObjInfoEquation Native _1086560224*F ¦ G¦ FMicrosoft Equation 3.0 DS Equation Equation.39q II =0.85 10 +100.150.85 9 +450.15 2 0.85 8 ()=0.82Ole CompObjfObjInfoEquation Native P FMicrosoft Equation 3.0 DS Equation Equation.39q48mI4yI 22010=4 FMicrosoft Equation 3.0 DS Equation Equation.39q_1086560339F G¦ G¦Ole CompObjfObjInfo8mI4yI Px>3()=1"e "4 +e "4 41+e "4 4 2 2() FMicrosoft Equation 3.0 DS Equation Equation.39qEquation Native _1086560546F G¦0n¦Ole CompObjfObjInfoEquation Native \_1089604009KF0n¦0n¦Ole @8mI4yI =1"0.238=0.76 FMicrosoft Equation 3.0 DS Equation Equation.39q8mI4yI )mnICompObjfObjInfoEquation Native 8_1089604070F0n¦0n¦Ole CompObjfObjInfoEquation Native p FMicrosoft Equation 3.0 DS Equation Equation.39qTIdI P(A/B)=P(AB)P(B)` FMicrosoft Equation 3.0 DS Eq_1089604579F@¦@¦Ole CompObjfObjInfouation Equation.39qϘ8mI4yI =0.05+0.040.175=0.090.175=0.514 FMicrosoft Equation 3.0 DS Equation Equation.39qEquation Native _1089604701F@¦@¦Ole CompObjfObjInfoEquation Native t_1086561104F`¦`¦Ole X8mI4yI =0.7650.825=0.927 FMicrosoft Equation 3.0 DS Equation Equation.39q8mI4yI 2xCompObjfObjInfoEquation Native 0_1086561244FP¦`¦Ole CompObjfObjInfoEquation Native  FMicrosoft Equation 3.0 DS Equation Equation.39q԰II Z=2x") n  =5500"5000)1200 144  =5_1086562062F`¦`¦Ole CompObjfObjInfo FMicrosoft Equation 3.0 DS Equation Equation.39qII Z=2x")s n"1  =16.5"15)3.5 200"1  =6.046>1.96!Equation Native _1087451561F`¦p æOle CompObjf FMicrosoft Equation 3.0 DS Equation Equation.39q2d8mI4yI 2x 1 ,s 1 ,2x 2 ,s 2 FMicrosoft Equation 3.0 DS EqObjInfoEquation Native _1087451645Fp æp æOle CompObjfObjInfoEquation Native _1087451773Fp æ1æuation Equation.39q2РxInI 2x 1 =x 1 " n 1 =1,0486=174.67 FMicrosoft Equation 3.0 DS Equation Equation.39qOle CompObjfObjInfoEquation Native 2ДlII 2x 2 =x 2 " n 2 =1,3307=190 FMicrosoft Equation 3.0 DS Equation Equation.39q_1087452458F1æ1æOle CompObjfObjInfoEquation Native h_1087452480F1æXæOle CompObjf2L8mI4yI (x 1 "2x 1 ) 2 FMicrosoft Equation 3.0 DS Equation Equation.39q2L8mI4yI (x 2 "2x 2 ) 2ObjInfoEquation Native h_1090045879FææOle 3 FMicrosoft Equation 3.0 DS Equation Equation.39qII s 1 = (x 1 "2x 1 ) 2 " n 1 = 1,811.336  = 301CompObjfObjInfoEquation Native 0_1087453494 Fææ.89  =17.37 FMicrosoft Equation 3.0 DS Equation Equation.39q28mI4yI s 2 = (x 2 "2x 2 )  " n 2 = 887Ole CompObjfObjInfoEquation Native    !"#$%&),-./2569<=@CFKPSV[^_`cfghilorux}  = 12.57  =3.55 FMicrosoft Equation 3.0 DS Equation Equation.39q20$IsI  1 = 2_10874536390 FææOle CompObj  fObjInfo Equation Native L_1087453592FæĦOle CompObj f FMicrosoft Equation 3.0 DS Equation Equation.39q20xIwI  1 `" 2 FMicrosoft Equation 3.0 DS EqObjInfo Equation Native  L_1101545413FĦĦOle CompObjfObjInfoEquation Native _1090046081"FĦĦuation 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"2E+!<`;Yw!b(`!+!<`;YwX@|x5O=KQ+wV!G!1]-N$,IW'๻|ogy A/\αV(a4>ykyk ? @ A B C D E F G H I J K L M O P Q S T U V Y \ _ b e f g h i j k l m n o p q r s t u v w x y z { | } ~  - B 2 # ActivityE'.1Garamond---'-  -Times New Roman----  2 LET89?- 2 <2------- 2 EET99?- 2 <w1-2 #: Duration DI1#'13I---------2 ) Slack = LET,''3J89?- 2 < 2- 2 6 -EET99?- 2 < 1- 2  -DI--'-    2 AE 2 11,, 2 0, 2 3, 2 8, 2 4B8 2 412,, 2 40, 2 41, 2 411,, 2 CD 2 *5, 2 0, 2 5, 2 0, 2 4DI 2 412,, 2 43, 2 41, 2 48, 2 E9 2 11,, 2 5, 2 6, 2 0, 2 4F6 2 412,, 2 411,, 2 41, 2 40, 2 GI 2 14,, 2 12,, 2 2, 2 0, 2 4HN 2 422,, 2 414,, 2 48, 2 40,-'-  -  "- "- !-BB- !B-//- !/-- !-  - ! -  - ! - - ! - - ! - - ! - - ! - - ! - - ! - - ! - - ! - - ! - - ! --'-  ' FMicrosoft Excel WorksheetBiff8Excel.Sheet.89q A@\pInformation Techinology Centre ObjInfo Workbook SummaryInformation(N DocumentSummaryInformation8R  Ba= =--Q <X@"1Arial1Arial1Arial1Arial1.Times New Roman1 Garamond1 Garamond"$"#,##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_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *  "8@ @ `Sheet1`i Activity Duration DABCDEFGHLET2EET1Slack = LET2-EET1-D   2< A@  dMbP?_*+%MHP LaserJet 4@g X@MSUDHP LaserJet 4<d "dX??U} }  }  h,,,,,,,,      &@@' @DDD (@?'&@DDD @@'DDD (@@?' @DDD &@@@'DDD (@&@?'DDD ,@(@@'DDD  6@,@ @'DDDFUUUUUUU>@[d7       &@@' @DDD (@?'&@DDD @@'DDD (@@?' @DDD &@@@'DDD (@&@?'DDD ,@(@@'DDD  6@,@ @'DDDFUUUUUUU>@7  Oh+'0@H`  DISMUS OBUBAx Information Techinology Centre Microsoft Excel@@p ՜.+,0 PXt | S.W.A.T COMMANDERsh Sheet1  Worksheets_1085720262F 릓 릓Ole W CompObjX fObjInfoZ  FMicrosoft Equation 3.0 DS Equation Equation.39qx\`mI\yI a ij +4m ij +b ij 6() FMicrosoft Equation 3.0 DS Equation Equation.39qEquation Native [ x_1085720586F0릓0릓Ole ] CompObj^ fObjInfo` Equation Native a p_1101631862 F0릓`d즓Ole c xTItI b ij- a ij 6() 2 2  -    ''  '  '  PRINTd CompObj fObjInfo Workbook 0*+Garamond- 2 #[ActivityE'.1Garamond--"System-'-  ----  2 ua'- 2 <ij------- 2 mL- 2 <ij------- 2 6b1- 2 <gij------- 2 d/- 2 <ij-2 #Slack,''32 #2 CommentD1LL'3-'-    2 AE 2 1, 2 3, 2 I4, 2 2.8,, 2 0.25,,, 2 < 8.4,,2  Not criticalN1'#'' 2 4B8 2 41, 2 41, 2 4I2, 2 41.2,, 2 40.03,,, 2 4& 12.3,,,2 4 Not criticalN1'#'' 2 CD 2 4, 2 5, 2 I9, 2 5.5,, 2 0.69,,, 2 ^ 0,2  CriticalD#'' 2 4DI 2 41, 2 41, 2 4I1, 2 41.0,, 2 40.00,,, 2 4< 9.7,,2 4 Not criticalN1'#'' 2 E9 2 4, 2 6, 2 312,, 2 6.7,, 2 1.78,,, 2 ^ 0,2  CriticalD#'' 2 4F6 2 41, 2 41, 2 4I2, 2 41.2,, 2 40.03,,, 2 4^ 0,2 4 CriticalD#'' 2 GI 2 1, 2 2, 2 I3, 2 2.0,, 2 0.11,,, 2 ^ 0,2  CriticalD#'' 2 4HN 2 46, 2 48, 2 4310,, 2 48.0,, 2 40.44,,, 2 4^ 0,2 4 CriticalD#''-'-  -  "- "- ! - - ! - - ! - - ! - - ! - - ! - - ! - - ! - - ! --'-  -'- x  -4&> "--- !---.  - -    .1 &@= & MathTypep Times New Roman-  #2 22 'ijSymbol-  2 5s & "System-  --'& --' FMicrosoft Excel WorksheetBiff8Excel.Sheet.89q.F!Sheet1!Object 1 A@\pInformation Techinology Centre Ba= =-- <X@"1Arial1Arial1Arial1Arial1 Garamond1 Garamond"$"#,##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"Yes";"Yes";"No""True";"True";"False""On";"On";"Off"                + ) , *  "8  ( "x "8@ "<@ "<@  (@   " `e Sheet1`ik"cUZ=xyU?@=7UZ=xyU!IxڅToE~$qbb8B-DP"pu럵=&q\'f]Ch!\º>TF/Z J]7Y}7}yoQP*p/0 Μ&bm*~-2`D_əOֹ{{WꜿxqQ~PDTeLhLIyWa= v+Azŗϝ( 1Tbg$Z~?x!''I-,FE /Q-tKqGOmĩ-QgDHQ3EyiYf#87[:^'D2ϬI.)OJPXW!YUE%FptidoU:9OM誄&{PN9{~j 8fwn(9ȹ=(f$~L%楛x镘,~r(~x0x3M>#t9p*@ż8lqtN~d < < ۴ؚWmǛ8ev/{QbߖCK?+:je܃⽢>OXN_Z$fap ?{ AϣKP5ڎ\gz+ 2| 6D5Sm3aV[:lH7<5;4|\k<5ұ@ȡo?ihsH5=Zր @2| {X>%s,!/Z*G[ ^ hS>m~x#԰En0!s \m9@s=:kr"=Ĺ\~>޽_-}>@ D],ZVK\t#.s'17x]2].[ot"f?l;`t7.^ütrbw~Un M B5݃4\ɋJ_С3  @@  ActivitySlackABCDEFGHComment Not criticalCriticalaijmijbijdij 77 A@  dMbP?_*+%MHP LaserJet 4@g X@MSUDHP LaserJet 4<d "dX??U} m } } }   h,,,,,,,, , ,        ?@@0@DDD+?DD~ @@  ??@0?DDD+qq?DD~ 8@  @@"@0@DDD+88?DD~   ???0?DDD+DD~ P@  @@(@0@DDD+qq?DD~   ??@0?DDD+qq?DD~   ?@@0@DDD+qq?DD~    @ @$@0 @DDD+qq?DD~     l(  v  <A ?A@?__]J` Xlh Equation.3 >@[d7 MBD0009F39BFP=즓P=즓Ole  >CompObj fEquation Native  H FMicrosoft Equation 3.0 DS Equation Equation.39q,4yI  ij  2 Oh+'0@H`  DISMUS OBUBAx Information Techinology Centre Microsoft Excel@ڭ@hSummaryInformation( DocumentSummaryInformation8 _1085722032F`d즓`d즓Ole   ՜.+,0 PXt | S.W.A.T COMMANDERsh Sheet1  Worksheets FMicrosoft Equation 3.0 DS Equation Equation.39qCompObj fObjInfo Equation Native  0_1085722219:F`d즓p즓x`mI\yI d"T: FMicrosoft Equation 3.0 DS Equation Equation.39qxH(II zd"t"T()()Ole  CompObj fObjInfo Equation Native  d_1104663261F즓즓Ole  CompObj fObjInfo  FMicrosoft Equation 3.0 DS Equation Equation.39q`II zd"22"23.5()3.3() FMicrosoft Equation 3.0 DS EqEquation Native  |_1104663334S F즓즓Ole  CompObj   fuation Equation.39q8II zd""0.826() FMicrosoft Equation 3.0 DS Equation Equation.39qObjInfo  Equation Native  T_1101565492F즓Ole  CompObj fObjInfo Equation Native  _1103632741F'nܬ8nI4zI P(td"23.5)=Pzd"23.5"23.5 3.3  ()=P(zd"0) FMicrosoft Equation 3.0 DS Equation Equation.39qOle  CompObj fObjInfo Equation Native  ϨI@yI P(te"30)=Pzd"30"23.5 3.3  ()=P(zd"3.26) FMicrosoft Equation 3.0 DS Equation Equation.39q_1101632078?F'NOle  CompObj fObjInfo Equation Native  _1101817499E~  F 9Ole  PRINT! X II crash cost"normal costnormal time"crash time()<^ @   \''  ' ['                       ! # $ % & ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N P S T U W X Y Z ] b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~   [Garamond- 2 !Activity?#-+2 EET(n)44712 @LET(n+1)3471E)2  Duration DE1#&-1E*2 ySlack=LET(n+1)-EET(n)-D-&#.E3471E)4471E2  Comment?-HH%1Garamond- 2 A? 2 "0( 2 7( 2 7( 2 Z 0(2 critical# ## 2 B3 2 "0( 2 14(( 2 10(( 2 Z 4(2 not critical.-# ## 2 \C= 2 \"7( 2 \14(( 2 \4( 2 \Z 3(2 \ not critical.-# ## 2 DB 2 "7( 2 37(( 2 30(( 2 Z 0(2  critical# ## 2 6E4 2 6"7( 2 626(( 2 67( 2 6F 12((2 6 not critical.-# ## 2 F1 2 11(( 2 26(( 2 12(( 2 Z 3(2  not critical.-# ## 2 GB 2 11(( 2 33(( 2 15(( 2 Z 7(2  not critical.-# ## 2 }HG 2 }23(( 2 }37(( 2 }11(( 2 }Z 3(2 } not critical.-# ## 2 I 2 23(( 2 58(( 2 25(( 2 F 10((2  not critical.-# ## 2 WJ 2 W23(( 2 W33(( 2 W6( 2 WZ 4(2 W not critical.-# ## 2 K< 2 37(( 2 58(( 2 21(( 2 Z 0(2  critical# ## 2 1L3 2 129(( 2 158(( 2 125(( 2 1Z 4(2 1 not critical.-# ## 2 NG@2 n&Start Node; n+1 - end node of activity(# G-+#.D(#.+.-+#-##)-"System-'- \-  "-mmY "- !Um-Y- !U-GGY- !UG-Y- !U-!!Y- !U!-Y- !U-Y- !U-hhY- !Uh-Y- !U-BBY- !UB-Y- !U-Y- !U-Y- !U--'- [' FMicrosoft Excel WorksheetBiff8Excel.Sheet.89q Oh+'0@H`  CompObj fObjInfo # WorkbookSummaryInformation("$ A@\pInformation Techinology Centre Ba= =-- <X@"1Arial1Arial1Arial1Arial1 Garamond1 Garamond1 Garamond1 Garamond1 Garamond1Arial"$"#,##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_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *        "8 !8  "8  !8  "x       `Sheet1`iActivityCommentABCDEFGHIJKL TIME IN HOURSEET(n)LET(n+1)Slack=LET(n+1)-EET(n)-D Duration DN&Start Node; n+1 - end node of activity2 _9j A@  dMbP?_*+%MHP LaserJet 4@g X@MSUDHP LaserJet 4<d "dX??U} } m } m } m}  ,               @@'DDDI4n3D critical not criticalB critical ,@$@'@DDDIHn3D critical not criticalB not critical @,@@'@DDDIdn3D critical not criticalB not critical @B@>@'DDDIn3D critical not criticalB critical @:@@'(@DDDIn3D critical not criticalB not critical &@:@(@'@DDDIn3D critical not criticalB not critical &@@@.@'@DDDIn 3D critical not criticalB not critical   7@B@&@' @ D D D I n 3D  critical not criticalB not critical   7@M@9@' $@ D D D I n 3D  critical not criticalB not critical   7@@@@' @ D D D I  n 3D  critical not criticalB not critical   B@M@5@'  D D D I <n 3D  critical not criticalB critical   =@M@9@' @ D D D I Pn3D  critical not criticalB not critical  "J ,T>@[d7  B@M@5@'  D D D I 07 3D  critical not criticalB critical   =@M@9@' @ D D D I  73D  critical not criticalB not critical  ,T>@7 DISMUS OBUBAx Information Techinology Centre Microsoft Excel@P @w=[ ՜.+,0 PXt | S.W.A.T COMMANDERsh Sheet1  WorksheetsDocumentSummaryInformation8" _1104663943' F@POle ' PRINT&)( 2( O   J ''  ' I ' I Garamond- 2 Comment?-HH%12 Activity?#-+2 Activity?#-+2 PDurationE1#&-12 EET(n)44712  LET(n+1) 3471E)2 m Float4-&Garamond-2 Start(#  2 zA? 2 5( 2 0( 2 d 5( 2 0(2 Start(#  2 B3 2 3( 2 0( 2 d 4( 2 1( 2 \{C= 2 \9A? 2 \7( 2 \5( 2 \P 14(( 2 \ 2( 2 xDB 2 9A? 2 6( 2 5( 2 P 11(( 2  0( 2 6E4 2 6?B3 2 67( 2 63( 2 6P 11(( 2 6 1( 2 F1 2 E,D4B 2 3( 2 11(( 2 P 14(( 2  0( 2 End4.+ 2 xGB 2 E,D4B 2 10(( 2 11(( 2 P 22(( 2  1( 2 }End4.+ 2 }vHG 2 }F,C1= 2 }8( 2 }14(( 2 }P 22(( 2 } 0(O2 0n - start node n+1 - annode of given activity.# .-+#.D(#..-+#-&)#.##)-"System-'- J -  "-mm "- ! m- - ! -GG - ! G- - ! -!! - ! !- - ! - - ! -hh - ! h- - ! --'- I ' FMicrosoft Excel WorksheetBiCompObjO fObjInfo(+Q WorkbookbSummaryInformation(*,R ff8Excel.Sheet.89q Oh+'0@H`  DISMUS OBUBAx Information Techinology Centre Microsoft Excel@L"@f/? ՜.+,0 PXt | A@\pInformation Techinology Centre Ba=  =--N <X@"1Arial1Arial1Arial1Arial1 Garamond1 Garamond1 Garamond1 Garamond1 Garamond"$"#,##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_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *    "    ( "8   (  "8    `Sheet1`iActivity PrecedingDurationFloatCommentABCDEFGHE,DF,CEET(n)StartEnd LET(n+1) Time in weeks0n - start node n+1 - annode of given activity $LMu A@  dMbP?_*+%MHP LaserJet 4@g X@MSUDHP LaserJet 4<d "dX??U} } I } $  ,               @@'DDD  @@'?DDD  @@,@'@DDD  @@&@'DDD  @@&@'?DDD   @&@,@'DDD    $@&@6@'? 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DDD     @,@6@' D D D Bbmmmmmmq>@7 DocumentSummaryInformation8V _1086294826/F``Ole [ CompObj.0\ fS.W.A.T COMMANDERsh Sheet1  Worksheets FMicrosoft Equation 3.0 DS Equation Equation.39qx`mI\yI ObjInfo1^ Equation Native _ ,_11019737194 F`#煉Ole ` 8[ a7   g ''  ' f ' f Garamond- 2 NormalG- E# 2  14(( 2  9( 2  0( 2  179(((2  Compress D=-E- #B PRINT36a CompObj fObjInfo58 Workbook  2 13(( 2 4.5(( 2 5(2 179.5((((2  Compress D=-E- #B 2 12(( 2 0( 2 10(( 2 180(((2 \Crash D= #.B 2 \ 11(( 2 \ 0( 2 \ 15(( 2 \ 185(((72  Crash F (A-C-F & B-D-F critical)= #.1?=1E3B1# ## 2  10(( 2  0( 2  22.5(((2  192.5((((2 6Crash C= #.= 2 6 10(( 2 6 0( 2 6 27.5(((2 6 197.5((((2  Compress B=-E- #3 2 49( 2  0( 2  37.5(((2  207.5((((2 Crash B= #.3 2 49( 2  0( 2  47.5(((2  217.5((((2 }Crash A= #.? 2 }48( 2 } 0( 2 } 57.5(((2 } 227.5(((("System-'- g -  "-mmd "- !` m-d - !` -GGd - !` G-d - !` -!!d - !` !-d - !` -d - !` -hhd - !` h-d - !` --'- f ' FMicrosoft Excel WorksheetBiff8Excel.Sheet.89q A@\pInformation Techinology Centre Ba=  =--- <X@"1Arial1Arial1Arial1Arial1 Garamond1 Garamond"$"#,##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_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *     (  ( "8  ( "8   `SSheet1`iCommentTimeOpportunity costAdditional crash Total costNormal Compress DCrash D Compress BCrash Bdays in Sh'000'cost in Sh'000' Crash F (A-C-F & B-D-F critical)Crash CCrash A` q A@  dMbP?_*+%MHP LaserJet 4@g X@MSUDHP LaserJet 4<d "dX??U} m} } } }                ,@"@`f@ *@@@%pf@DD (@$@%f@DD &@.@% g@DD  $@6@%h@DD $@;@%h@DD "@B@%i@ DD   "@G@% 0k@D D    @L@% pl@D D  TBF0SSSSSSSS>@[d7 %f@DD  &@.@% g@DD  $@6@%h@DD  "@@@@%Pi@DD   @@E@%j@DD^BF0SSSSS>@7 SummaryInformation(79 DocumentSummaryInformation8 _1085722715<F#煉#煉Ole   Oh+'0@H`  DISMUS OBUBAx Information Techinology Centre Microsoft Excel@.@GȨ ՜.+,0 PXt | S.W.A.T COMMANDERsh Sheet1  Worksheets FMicrosoft Equation 3.0 DS Equation Equation.39qx`mI\yI crash cost"normal costnormal time"crCompObj;= fObjInfo> Equation Native  _1101790681AFJ煉q煉ash time() FMicrosoft Equation 3.0 DS Equation Equation.39q̸II PBFP0.50.10.1B0.20.60.2F0.10.Ole  CompObj@B fObjInfoC Equation Native  20.6 FMicrosoft Equation 3.0 DS Equation Equation.39q@8mI4yI 202050()_1101790683NFF煉煉Ole  CompObjEG fObjInfoH Equation Native  \_1101790688KF0򦓜W򦓜Ole  CompObjJL f      !"#$%&'()5,-/.2143uv6789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstwyxz{|~}aXf4$$IfX!vh5555#v#v#v:V l406+5554aXf4Dd lJ  C A? 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ZB |B S Dn0z ` } C })+ ` ~ C ~ #% n  c $A n? 3"?  `B!CDEF!@C"?bB  c $D"?bB  c $D"?bB  c $D"?  `BCDEF@C"?bB  c $D"?bB  c $D"?  `BCDEF@C"? @ `BCVDEFV@C"?  `BCDEF@C"?bB  c $D"?  `BCDEF@C"?!  `B!CDEF!@C"?"bB  c $D"?#  `BCDEF@C"? bB  c $LD"?:n H   C"?'T  # Hj7 `  C   :n H   C"?(T  # Hj7 `  C   :n H   C"?&T  # Hj7 `  C   :n H   C"?%T  # Hj7 `  C   :n H   C"?$T  # Hj7 `  C    b < %#  #" ?TB  C DP | TB  C DPTB  C DDpTB  C DLx@TB  C DPTB  C DXTB  C DTTB  C D !HTB  C D"D#TB  C D 0 TB  C D@LTB  C DHTB  C DP $#TB  C D,#TB  C D#TB  C D#TB  C DtH#TB  C D#TB  C DtH!#TB  C D#<#TB  C D4 #`  C < 4 x `  C  0x#t `  C \X  `  C  p  TB  C D!$%"n E$!1  C"?Pn  * ,  #"  ' )j2  S .AShingle * ,`  C  + [, Pn  * ,  #"  [, ,.j2  S .AShingle * ,`  C  + [, Pn  * ,  #" ')j2  S .AShingle * ,`  C  + [, Pn  * ,  #" ,Y.j2  S .AShingle * ,`  C  + [, Pn  * ,  #" ]E$.&j2  S .AShingle * ,`  C  + [, Pn  * ,  #" 0*+j2  S .AShingle * ,`  C  + [, Pn  * ,  #" {/L1j2  S .AShingle * ,`  C  + [, ZB  S D ((ZB  S D (<-ZB  S D (i-ZB  S D i-i-ZB  S D%]#(ZB  S D5%l<-ZB  S D2(0*ZB  S D*Z-ZB  S D2(0ZB  S Di-NW0`  C ' @) `  C C*+ `  C Z-. `  C ~,- `  C Q)]* `  C B'( `  C S%& `  C -[,- `  C M +j, `  C *8, `  C $ #( ) `  C 3 , - `  C *0I 1 `  C [{*+ `  C j$% f  S '!- f  S #( . `  C ,.Cf0 b  d 8"  #" ?`  C  d `  C (x0 `  C \ `  C dh  ! `  C d 8" `  C D l $" b  3#  #" ?+ZB  S D 3 "ZB  S D "l"  c P:A,Light upward diagonal, TB  C DMB!`  C HPF `  C q o" `  C  `  C   `  C  %    HGRHZI+JKLMRNZ * `  C    `  C 0!y# nn  x   C"?,ZB  S DgZB   S DgkgTB  C D* lI`   C    `   C  /x TB  C D,Q nTB  C D 3 A\ TB  C D TB  C Df TB  C DRQ{ TB  C D0 TB  C Dg TB  C D bC TB  C D@ TB  C DY TB  C DZTB  C D,o TB  C D; TB  C D;rn n  ]"   C"?-ZB  S D % u!ZB   S D u!Pu!TB  C D QW!`   C     `   C   ]" TB  C D ?W!TB   C D !ZB  S D>uZB  B S DwY ZB  S Dcv ru!ZB   S D jZB  B S Dk ,VB ! C D"?Lt #?* # # C"?)Vt  * , $ # #" n(?*j2 %  S .AShingle * ,` &  C &  + [, Vt  * , ' # #" j2 (  S .AShingle * ,` )  C )  + [, Vt  * , * # #" "$j2 +  S .AShingle * ,` ,  C ,  + [, Vt  * , - # #" Lj2 .  S .AShingle * ,` /  C /  + [, Vt  * , 0 # #" "T$j2 1  S .AShingle * ,` 2  C 2  + [, Vt  * , 3 # #" jP(;!*j2 4  S .AShingle * ,` 5  C 5  + [, ZB 6 S DL $ZB 7 S D[^)ZB 8  S DU#ZB 9 S DF#.^)ZB :  S D#y)ZB ; S D1)j1)` <  C < iM ` =  C = ^#% ` >  C > 'l1) ` ?  C ? l%& ` @  C @ 0*!" ` A  C A p#&% ` B  C B  ` C  C C a&' ` D  C D 7#d$ ` E  C E  (!* ` F  C F }(") ` G  C G J""T$ ` H  C H Y"Z f I  S I !g #a& f J  S J  & ZB K  S DjZB L  S DL)` M  C M dx b N C N "?* \ O 3 O "?. b P C VP "?Y V#n R )2 Q  C"?Z` R  C WR G1!2 W"Z R )1 S  R )1ZB T  S D" ({ .ZB U S D1 i(]i(ZB V S D1 "-(ZB W  S DB"?-(ZB X  S D-/ /ZB Y S D(N/ZB Z  S DK(!(ZB [  S D $'R&'` \  C X\  }#?% X` ]  C Y]  * V, Y` ^  C Z^ #~$ Z` _  C [_ &( [` `  C \` -/ \` a  C ]a 9+, ] b  0^b ? ?"z&<( ^` c  C _c #/&%' _` d  C `d  9+1 , `` e  C ae i/1 a` f  C bf +- b` g  C cg -() c` h  C dh  ^$Z & d` i  C ei #D% e` j  C fj i( +* f` k  C gk 7$'&&) g` ` L#? & l # R&1 )pt O a.1  m # #" ` L#? &42 n  O a1 TB o  C D! ! " TB p  C D0 . ` q  C hq 3 P#" $ h` r  C ir  $ & iZ  4# s   4#pt O a.1  t # #"  #42 u  O a1 TB v  C D! ! " TB w  C D0 . ` x  C jx J o" j` y  C ky 7"4# k`B z B c $DԔ^ ?* /+`B { B c $DԔ/g 0`B |  c $DԔ*+`B } B c $DԔY'(`B ~ B c $DԔ$~'%n(n  4#   #" ?&u)pt O a.1   # #"  #42   O a1 TB   C D! ! " TB   C D0 . `   C l J o" l`   C m 7"4# mn  4#   #" *!&`$d)pt O a.1   # #"  #42   O a1 TB   C D! ! " TB   C D0 . `   C n J o" n`   C o 7"4# on  4#   #" R&v&)F)pt O a.1   # #"  #42   O a1 TB   C D! ! " TB   C D0 . `   C p J o" p`   C q 7"4# qn  4#   #" 2.!1pt O a.1   # #"  #42   O a1 TB   C D! ! " TB   C D0 . `   C r J o" r`   C s 7"4# sn  4#   #" ] .0pt O a.1   # #"  #42   O a1 TB   C D! ! " TB   C D0 . `   C t J o" t`   C u 7"4# u`   C v  +s- v`   C w  ./ w`   C x  ,- x`   C y k&!-( y`   C z F$/&&' z`   C { '( {`   C | #`% |`   C }  #-$ } n %(-   C"?[` %(,  # %(,ZB   S D }#N )ZB  S D P#0P#ZB  S D t#ZB   S D)#ZB   S Dg*g*ZB  S D}#!*ZB   S D" "ZB   S D#"%&"`   C ~  d&  ~`   C   {%z =' `   C  bQ `   C  !n# `   C  (* `   C   &'    0 ? ?"i!X A# `   C   $!%" `   C   & ' `   C  v*+ `   C  Q&@( `   C  #$ `   C  >E-! `   C  dS  `   C  P#w % `   C   $"%|$ ` ` L#? &  # %! $pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #" pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #" ? (+pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #" 7),pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #" !{!K$pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #"  !{!#K$pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #" &!(Z$      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefgijklmnopqrstuvwxyz{|}~pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & `B  B c $DԔ1 &% &`B  B c $DԔ*:*`B   c $DԔ%&`B  B c $DԔ,"#`B  B c $DԔT$e"$U#`   C  .,!- &n  *3   C"?\`   C  (F2(#3 %Z  *1    *1ZB  S D$/ZB   S Dl h) /ZB  S D #f,)ZB   S DEA#,)ZB  S Db/ 0ZB  S D)9!c.ZB  S D`" ##ZB  S D('(`   C  y|$h>& `   C  Ji(9+* `   C  $% `   C   +F- `   C  .}a0 `   C  +F-    0 ? ?"!'#J) `   C  KH+: - `   C   8, - `   C  C01 `   C  8,- `   C  (u*    0 ? ?"]%' `   C  l%' `   C  !Y)#+ `   C  *!, ` ` L#? &  #  ' *pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #" f!E$pt O a.1   # #" ` L#? &42   O a1 TB !  C D! ! " TB "  C D0 . ` #  C # 3 P#" $ ` $  C $  $ & t ` L#? & % # #"  /1pt O a.1  & # #" ` L#? &42 '  O a1 TB (  C D! ! " TB )  C D0 . ` *  C * 3 P#" $ ` +  C +  $ & t ` L#? & , # #" *!- $0pt O a.1  - # #" ` L#? &42 .  O a1 TB /  C D! ! " TB 0  C D0 . ` 1  C 1 3 P#" $ ` 2  C 2  $ & t ` L#? & 3 # #" 'c*pt O a.1  4 # #" ` L#? &42 5  O a1 TB 6  C D! ! " TB 7  C D0 . ` 8  C 8 3 P#" $ ` 9  C 9  $ & t ` L#? & : # #"  s##pt O a.1  ; # #" ` L#? &42 <  O a1 TB =  C D! ! " TB >  C D0 . ` ?  C ? 3 P#" $ ` @  C @  $ & t ` L#? & A # #" ''*r*pt O a.1  B # #" ` L#? &42 C  O a1 TB D  C D! ! " TB E  C D0 . ` F  C F 3 P#" $ ` G  C G  $ & `B H B c $DԔ >+M .,`B I B c $DԔp/0`B J  c $DԔp&+&.,B K B D1?")*0B L B ?D?"#H!(B M  B ?D?"#F#~'(B N B ?D?" $m)' / O  0O ? ?"%!" ` P  C P "$  Q  0Q ? ?"N% ' ` R  C R w %f"L'  S  0S ? ?"$"$&% ` T  C T V#$E%&  U  0U ? ?" $f+%(- ` V  C V c%V,R'.  W BW 1?#" ?]  Y HY 1?#" ?` h /(" Z # #" ?^ZB [  S DV/cB \  H ?DV?"T(T` ]  C ] 1 M R  ` ^  C ^ 4  ` _  C _ C  ` `  C ` (D4  ` a  C a !#%  ` b  C b ;%  ZB c  S Dg ! S! B d  B ?Dg ?"MB e  B ?Dg ?"kB f  B ?Dg ?"'k'B g  B ?Dg ?"""B h  B ?Dg ?"--42 i  *!qB j  Z ?D1?"  rB k  6D1M lB l  0D1M B m  Z ?D1?"  lB n  0D1B o  T ?D1?"xTDTB p  Z ?D1?" TxT` q  C q k_  ` r  C r > ` s  C s !M" ` t  C t   ` u  C u   } ` v  C v  % B2 w     B2 x   > [  y  By 1?" : " ` z  C z  R  B {  Z ?D1?" z B |  Z ?D1?" #( 42 }  dXB ~  T ?D1?"DB   Z ?D1?" `   C  -e 42   B   T ?D1?"5@B   Z ?D1?" 5`   C  <+ 42   U^B  T ?D1?"B   Z ?D1?"`   C  h 42   * K B   T ?D1?""] A(] B   Z ?D1?"] "] `   C   . 42    C B   T ?D1?" O B   Z ?D1?"  `   C  _   42   K'B   T ?D1?"!A(B   Z ?D1?"%`   C  C  B  B ?Dg ?"''`   C  &2(%  B2    KIb <17 A #" ?_H2   # h#55xB   <D1K:3:3rB   6D166   H 1?" w24    H 1?") (6}7 B   N D1?"e5 e5   H 1?"<1 2 B   B D1?"Ke5e5Rn  %&   C"?alZ  %&    %&ZB   S D b zZB  S Dd, , ZB  S D Y ZB   S D ZB  S D $ZB  S DZB   S Do#oZB   S D #`   C  I   `   C   ^ `   C  W  `   C  2 !  `   C  f ( `   C  o    0 ? ?" o `   C  ! `   C  \K `   C  \8K `   C  |> `   C  B; 1  `   C  .  `   C   8 `   C  -~!@ `   C  X! G#U ` ` L#? &  #   {pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #"   pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #" pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #" pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #"  ! pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & t ` L#? &  # #" F#%&pt O a.1   # #" ` L#? &42   O a1 TB   C D! ! " TB   C D0 . `   C  3 P#" $ `   C   $ & `B  B c $DԔ `B  B c $DԔ`B  c $DԔy`B  B c $DԔ!G"`   C  "  n 3 r%f   C"?b`   C  "f $Z 3 r%   3 r%ZB   S D k VZB  S D!  ZB   S D ZB  S D/oZB  S D;"ZB   S DN "`   C  L ;  `   C    `   C  b% Q  `   C  - `   C  }X    0 ? ?" `   C  `  g `   C    `   C   `   C  [  `   C  )Q q `   C   k y  ` )"(r%A # )"r%-t ` L#? & # #" )"#%pt O a.1  # #" ` L#? &42  O a1 TB  C D! ! " TB  C D0 . `  C  3 P#" $  `  C   $ &  `  C  #(r%  `   C   #r%A  t )"(r%A  # #" 3 }| t ` L#? &  # #" )"#%pt O a.1   # #" ` L#? &42   O a1 TB  C D! ! " TB  C D0 . `  C 3 P#" $ `  C  $ & `  C #(r% `  C #r%A t )"(r%A # #" z gt ` L#? & # #" )"#%pt O a.1  # #" ` L#? &42  O a1 TB  C D! ! " TB  C D0 . `  C 3 P#" $ `  C  $ & `  C #(r% `  C #r%A t )"(r%A # #"  U t ` L#? & # #" )"#%pt O a.1   # #" ` L#? &42 ! O a1 TB " C D! ! " TB # C D0 . ` $ C $3 P#" $ ` % C % $ & ` & C &#(r% ` ' C '#r%A t )"(r%A (# #" ~t ` L#? & )# #" )"#%pt O a.1  *# #" ` L#? &42 + O a1 TB , C D! ! " TB - C D0 . ` . C .3 P#" $ ` / C / $ & ` 0 C 0#(r% ` 1 C 1#r%A B 2B B D8c?"N  tB 3 B D8c?"\ -LB 4B B D8c?"q/ B 5 B D8c?"r)(B 6B B D8c?"Rn  %)7 7 C"?clZ  %)4 8  %)4ZB 9 S Dz -3ZB : S D&'%&h)ZB ; S D y'V-ZB < S D&',ZB = S DS/$04ZB > S Dq7-3ZB ? S D,$q/ZB @ S DS%h)p&c.` A C Ai(+* ` B C B/~1 ` C C  C(*  ` D C !D&x( !` E C "ER02 "` F C #F4.#0 # G 0$G ? ?",!E. $` H C %H6$9+%&, %` I C &I/0 &` J C 'J23 '` K C (KC021 (` L C )L() )` M C *M;)* *` N C +N)f+ +` O C ,OT.!0 ,` P C -P%+'d- -` ` L#? & Q#  + .pt O a.1  R# #" ` L#? &42 S O a1 TB T C D! ! " TB U C D0 . ` V C .V3 P#" $ .` W C /W $ & /t ` L#? & X# #" V%5(pt O a.1  Y# #" ` L#? &42 Z O a1 TB [ C D! ! " TB \ C D0 . ` ] C 0]3 P#" $ 0` ^ C 1^ $ & 1t ` L#? & _# #" .2q4pt O a.1  `# #" ` L#? &42 a O a1 TB b C D! ! " TB c C D0 . ` d C 2d3 P#" $ 2` e C 3e $ & 3t ` L#? & f# #" q+A.pt O a.1  g# #" ` L#? &42 h O a1 TB i C D! ! " TB j C D0 . ` k C 4k3 P#" $ 4` l C 5l $ & 5t ` L#? & m# #" %&')c*pt O a.1  n# #" ` L#? &42 o O a1 TB p C D! ! " TB q C D0 . ` r C 6r3 P#" $ 6` s C 7s $ & 7t ` L#? & t# #" $#.'0pt O a.1  u# #" ` L#? &42 v O a1 TB w C D! ! 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