GEORGETOWN UNIVERSITY

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1 McDONOUGH SCHOOL OF BUSINESS GEORGETOWN UNIVERSITY Fall Semester 1999 DSCI 555: QUANTITATIVE METHODS IN BUSINESS, II Professor Office Hours: And by appointment. Case studies (cases 2&3) Take-Home Final Exam Due on December 2 nd at 5:00 p.m. Due on December 10 th at 5:00 p.m. 1

2 QUANTITATIVE METHODS II Management Science focuses on the application of mathematical and statistical models to business decision making. The terms management science and Operations Research are often used interchangeably, although OR has a somewhat more engineering flavor. However, the two fields share a largely common methodology and the same major professional organization, INFORMS (the Institute for Operations Research and Management Science). Management Science methods are applied to planning and management problems, both in traditional areas such as production and operations, inventory management, and scheduling and increasingly in marketing, finance and management. Powerful, often computer-intensive MS techniques are available to solve a wide range of problems ranging from real-time control of specific business, industrial, agricultural, and administrative operations to long-term planning models for corporations and public sector agencies. Management Science is more than a set of mathematical methods. A fundamental part of MS/OR is the "systems approach" to problem solving. This approach recognizes that the context of organizational problems is as important as the stated technical problem. Defining a problem, collecting data, consulting with people involved in the solution, and implementing change are all fundamental aspects of the Management Science approach. Applied Regression Analysis: As it was indicated in quantitative I, the first step in the construction of a regression model is to hypothesize the form of the deterministic portion of the probabilistic model. This model building or model construction is the key to the success or failure of the regression analysis. If the regression model does not reflect, at least approximately, the true nature of the relationship between the mean response E(y) and the independent variables the modeling efforts will usually go unrewarded. In this part of the course, we further develop model-building and diagnostic skills. Statistical Process Control: Companies use SPC to develop and maintain their ability to produce high-quality products and services. Quantitative techniques for quality improvement will be considered in this chapter. We will study particular processes to determine at what point they are producing goods that do not conform to specifications. The objective is to adjust production and services processes before they create defective outputs. Forecasting: A forecast is a prediction of what will occur in the future. Managers are constantly trying to predict the future regarding a number of factors, in order to make decisions that will ensure the continued success of their firms. Often managers use judgment, opinion, or past experiences to forecast what will occur. However, statistical procedures often produce at least as effective forecasts, at a much lower cost in time and executive effort. 2

3 Waiting Lines and Queuing Theory: Waiting lines are an everyday occurrence, affecting people shopping groceries, buying gasoline, making bank deposits, etc. Queues may also take the form of machines waiting to be repaired, trucks in the line to be unloaded, or airplanes lined up on a runway waiting for permission to take off. We will examine the basic structure of waiting line processes and consider simple computer simulations of such schemes. GRADES DIVISIONS: Class Participation and Minicases 30% (Cases 2&3 team projects) In-class Hourly Exam 35% Comprehensive Final Exam -take-home 35%. Materials to be used with this class: I) Textbooks Required: 1) Chapters 11 through 14 from: McClave, J., Benson, P.G. and Sincich, T., Statistics for Business and Economics, 7th edition, Prentice-Hall 2) (Photocopies) Chapter 14: Waiting Lines and Queuing Theory Quantitative Analysis for Management, th Edition, Barry Render and Ralph Stair, Jr. II) Computer notes located at the following directory s:\dsci555 The following information is already in place: 1) All the minicases 2) The Syllabus 3) PowerPoint Presentation Recommended: Study Guide to the required course text by McClave et al. THE INTERNET CONNECTION: The Internet connection icon in the text directs the reader to additional materials and resources on the World Wide Web of the Internet dealing with topics and organizations discussed in each chapter. You can link to the Prentice Hall s homepage for this book via the following address: 3

4 Tentative Schedule of Classes DATE: TOPICS COVERED NOTES October 26 Review of Quantitative Techniques Chapter 11 October 28 Review of Quantitative Techniques Chapter 11-Case 1 November 2 Model Building Chapter November 4 Model Building Chapter , 12.9 November 9 Model Building Chapter 12-Case 2 November 11 Statistical Quality Control Chapter November 16 Statistical Quality Control Chapter 13.8-Case 3 November 18 IN CLASS HOURLY EXAM CHAPTERS 11,12,13 November 23 Forecasting and Times Series Chapter November 30 Forecasting and Times Series Chapter Autoregresssion-HAND-OUT December 2 Forecasting and Times Series/ Waiting Lines and Queuing Theory Chapter 14-Case 4 December 7 Waiting Lines and Queuing Theory Chapter 14-HAND-OUTS December 9 Waiting Lines and Queuing Theory Chapter 14-HAND-OUTS 4

5 Discussion Minicases Quantitative Methods Part Two Prepared by Professor Jose Luis Guerrero Cusumano Based on cases discussed in Statistics for Management and Economics G. Keller, B. Warrack and H.Bartel 3 rd Edition, 1994, Ed. Duxbury 5

6 Case 1 Determinants of Success in University Accounting Courses In the past decade, business school enrollment has increased significantly. This has resulted in a large number of students taking bookkeeping and accounting courses. There has also be increased interest in accounting in high school. One issue of concern to accounting professors is to what extent high-school accounting courses affect performance in university accounting courses. This issue is part of the more general question of what factors determine the degree of success in university accounting courses. This problem was addressed in a study reported in the Accounting Review (January 1988). A multiple regression model was proposed to investigate the variables that affect performance in a first-year accounting course. The model is y = Beta 0 + Beta 1 x 1 + Beta 2 x 2 + Beta 3 x 3 + Beta 4 x 4 + Beta 5 x 5 + Beta 6 x 6 + Beta 7 x 7 + Epsilon where y = Total mark earned in four examinations x 1 = Sum of the math and verbal scores from the Scholastic Aptitude Test (SAT), divided by 10 x 2 = Sum of high-school English and math grades, where A = 9 and D = 0 x 3 = Last cumulative grade point, where A = 6 and F = 2 x 4 = Number of quizzes taken (this is supposed to measure motivation and effort) x 5 = 1, if the student has had high-school bookkeeping or accounting = 0, otherwise x 6 = Number of semester hours of completed university-level statistics and mathematics (this is supposed to measure related experience) x 7 = Number of semester hours of completed university-level courses A total of 352 students who successfully completed the accounting course were included in the study. The Minitab system was used to estimate the model and produce the correlation matrix. These results are shown in the accompanying computer output. The regression equation is Y = X1+4.18X2+8.00X3+4.08X X5+1.08X6+.06X7 Predictor Coef Stdev t-ratio Constant X X X X X X X

7 Correlation Matrix x 2 x 3 x 4 x 5 x 6 x 7 y x X x x x x x Discuss what factors are the most important determinants of success in university accounting courses. 7

8 Case 2: Risk Assessment from Financial Reports Investors are interested in assessing the riskiness of a company's common stock, as well as its expected rate of return. It is therefore desirable to potential investors that a company's financial reports provide information to help them assess the company's risk. Farrelly, Ferris, and Reichenstein conducted an investigation into the relationship between seven accounting-determined measures of risk and the average risk assessment of financial analysts. The seven accounting-determined measures of risk (all of which could be computed from a company's financial reports) and their definitions are as follows: Dividend payout = (Cash dividends)/(earnings) Current ratio = (Current assets)/(current liabilities) Asset size = Log (Total assets) Asset growth = Average growth rate in asset size for the years Leverage = (Total senior debt)/(total assets) Variability in earnings = Standard deviation of the price-earnings ratio for the years Covariability in earnings = Strength of the relationship between a firm's price-earnings ratio and the average price-earnings ratio of the market overall These seven measures were computer for 25 well-known stocks, based on data from the companies' annual reports from 1977 to The results are listed in the accompanying table and are stored in columns 1-8 of file case 15-1 of the data disk. The names of the 25 stocks were then sent to a random sample of 500 financial analysts, who "were requested to assess the risk of each of the 25 companies on a scale of 1 (low to 9 (high), assuming that the stock was to be added to a diversified portfolio." The mean rating assigned by the 209 financial analysts who responded is recorded for each of the 25 stocks. This measure of the financial analysts' risk perception was taken to be a reasonable surrogate for the (market) risk of each stock. Based on the data given in the accompanying table, what would you conclude concerning the usefulness of the information provided by financial reports for the purpose of assessing a company's risk? 8

9 Name of Stock Dividend Payout Current Ratio Asset Size Asset Growth Leverage Variability Earnings Covariability Earnings Mean Risk Assessment American Telephone Procter & Gamble IBM General Electric Exxon Commonwealth Edison Dow Jones & Co McDonald's Sears Roebuck DuPont Safeway Citicorp Dr. Pepper General Motors Xerox American Broadcasting Holiday Inns Tandy Litton Ind RCA Georgia Pacific Emery Air Freight E.F. Hutton U.S. Homes International Harvester

10 Case 3: Chapter 14 The Gasket Manufacturing Case Please read the description of this case on pages of the McClave et al. book. The data are contained in the Minitab file Gasket.MTW on the S-Drive DSCI

11 Case 4: Business Failures and Key Economic Indicators Many people have attempted to get an overall picture of business failures and bankruptcies - both by studying the incidence of business failure on a national or industry-wide scale and by analyzing individual firms one by one - with varying degrees of success. In the latter instance, promising results have been achieved by using various accounting and financial ratios to predict bankruptcies. In looking at the overall picture, some analysts have been tempted by dramatic swings in the number of business failures from year to year to try to relate these swings to overall economic conditions. The annual percentage change in certain key economic indicators is reproduced in the accompanying table. Undertake the analysis necessary to enable you to discuss changes in the number of business failures. Annual Percentage Changes in Business Failures and in Key Economic Indicators in Canada Year Business Failures Real Domestic Product Wages & Salaries Unemployment Rate Retail Sales Housing Starts C.P.I

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