CASES FOR MBA 501

Money Supply and Interest Rates

Sources

Case: Bryant and SmithData: Money.jmp

JMP Instructions

To estimate a simple linear regression model, select Analyze and then Fit Y by X. Enter the dependent and independent variables of interest. Then click on the red triangle above the scatterplot and select Fit Line.

Assignment

Run a regression of PRIME on M1. Repeat this exercise for all pairs of money supply and interest rate variables in the data set. Use simple linear regression only. In the technical appendix, present the estimated regression equations, t-statistics, and R2s. Label the statistically significant coefficients as follows: *** if the p-value < 0.01, ** if the p-value < .05, or * if the p-value < 0.10. Interpret the magnitudes and significance of the estimated slope coefficients and the R2s. Address the following questions in your executive summary:

$ Which interest rates and money supplies have statistically significant relationships?

$ Are some interest rates more responsive to certain measures of money supply than others?

The Gotham Giants

Sources

Case: Schleifer and BellData: Gotham.jmp

JMP Instructions

Although they are qualitative in nature, code dummy variables as continuous in JMP. Otherwise, you will obtain unconventional results.

I created several dummy variables that will be useful for your analysis. For example, I recoded the variable Month, which ranges from 4 (April) to 9 (September) as a pair of dummy variables: April-May and June-August. The former takes on the value 1 if the game was played in April or May and the value 0 otherwise. The latter takes on the value 1 if the game was played in June, July, or August and the value 0 otherwise. Collectively, the reference category for these two dummy variables is September. Other dummy variables are defined in a similar fashion. Note that there are two sets of groupings for opponents: a set of dummy variables indicating individual opponents and a set of dummy variables indicating groups of similar opponents. Do not use both sets of dummy variables in any one model. If you are unclear about a variable definition, click on the box above the column and then select Edit Formula to see the variable that generates the values in the column of interest.

To generate a correlation matrix, select Analyze and then Multivariate. Highlight the variables of interest. Then select Y and Ok.

To estimate a multiple regression model, select Analyze and then Fit Model. Enter the dependent and independent variables of interest and click on Run.

Assignment

Assume that the TV schedule is frozen before the start of the season. Thus, it is possible that the day of the week, the month, the time of day, the opponent, and special events (if frozen in advance) could influence whether a game is televised. However, the temperature, the weather, and the pitcher cannot influence whether a game is televised because these variables are not observed when the TV schedule is made. Provide an influence diagram to help you select variables. Label each variable as A, B, C, D, E, treatment, or dependent variable. Using only the methods covered by the end of the last class, estimate several regression models. Present at most two empirical specifications. Justify your selection of independent variables. Present results tables even if you use specifications provided in the case book. Conduct a cost-benefit analysis to assess the value of special events.

Address the following questions in your executive summary:

$ Does televising games affect attendance? Explain fully.

$ Station WQJY would like to televise three more games in 1980 than in 1979 (at the same $12,500 per game). Should Glasshofer agree to this deal? Explain.

$ How worthwhile are the special events? What policy would you recommend with regard to special events in 1980? Explain (refer to the technical appendix for details).

Baseball Fans: Who are the Gotham Giants? What is the real radio station? Who are the star pitchers? Who are opponents 1, 2, and 10?

Reyem Affiar

Sources

Case: Separate HBS CaseData: Reyem.jmp

JMP Instructions

As in the Gotham Giants case, I created dummy variables that will be useful for your analysis.

For the look-alike analysis, you may want to sort the data. To do so, select Tables and then Sort. Highlight the variable of interest and click on By and then Sort.

To predict the selling price of 236 Ellery Street, add one row of data by selecting Rows and then Add Rows. Add one row to the end of the data file and fill in the information corresponding to 236 Ellery Street. Since some of the cells including the cell for Sale Price will be missing, this observation will not contribute to the estimated regression model. After estimating the regression model of interest (see the above instructions for The Gotham Giants), click on the red triangle in the upper left hand corner and then select Save Columns and Predicted Values. Return to the data table and note the new column of predicted values. The predicted value of interest will appear in the last row of this new column.

Assignment

Look-alike Analysis: Identify a group of condominiums with characteristics similar to 236 Ellery Street. The size of this group will depend on your look-alike definitions. Using only this group of condominiums, guess the selling price of 236 Ellery Street.

Regression Analysis: Using only the methods covered by the end of the last class, develop and estimate regression models of selling price. Present at most two empirical specifications. Justify your selection of independent variables. Briefly discuss which variables significantly influence the selling price. Indicate the signs of these coefficients and whether the signs are intuitive. Based on one of these models, predict the selling price of 236 Ellery Street.

Address the following questions in your executive summary:

$ Discuss the relative merits of look-alike analysis and regression analysis in the context of real estate appraisals in general and in this case in particular.

$ What other variables not included in the data set might improve the regression model=s predictive power?

$ What price should Reyem offer? Explain fully (refer to the results in the technical appendix). What information about Reyem might help you address this question?

Barbara J. Key Vs. The Gillette Co. (A and B)

Sources

Case: Schleifer and BellData: None

Assignment

The plaintiff=s expert presents results based on two regression models that differ with respect to the dependent variable -- SALARY or Log(SALARY). The defense expert presents results based on four regression models that differ with respect to the dependent variable -- again SALARY or Log(SALARY) -- and the set of independent variables -- inclusion or exclusion of job grade dummy variables.

Based on the evidence provided in the case, can you conclude that women suffered salary discrimination at Gillette in the 1972-75 time frame? If so, what do the results suggest about the extent of this discrimination? Before addressing these questions, think about which models are the most appropriate.

In your executive summary, summarize your conclusions with regard to whether and to what extent women suffered salary discrimination at Gillette between 1972 and 1975.

In your technical appendix, include a description of the data set (including the sample sizes), all relevant variable definitions, and an assessment of the various models. Present an influence diagram if it facilitates your analysis. Also explain how to interpret the coefficient on the sex dummy in models where the dependent variable is SALARY and the multiplicative effect in models where the dependent variable is Log(Salary). Illustrate by interpreting at least one coefficient and at least one multiplicative effect. Also indicate how the interpretation of the coefficient on SEX differs across models, depending on which explanatory variables are included.

Harmon Foods

Sources

Case: Schleifer and BellData: Harmon.jmp

JMP Instructions

Ignore the Invalid Row Number message when reading in the data.

If you use dummy variables, remember to code them as continuous in JMP.

Note that lagged values of consumer packs and dealer allowances have already been created.

See the instructions for The Gotham Giants and for Reyem Affiar on estimation and prediction.

To obtain the results of a Durbin Watson test, click on the red triangle in the upper left hand corner and then select Row Diagnostics and Durbin Watson Test.

Assignment

For January 1988, Harmon Foods has budgeted for 250,000 cases of consumer packs and $100,000 in dealer allowances. What is your sales forecast for the month, assuming the budgeted promotion figures prove correct?

To answer this question, develop a multiple regression model of sales. In addition to consumer packs and dealer allowances, consider including a trend, seasonal effects, and lags in your model. In your analysis, be sure to describe the data set including the sample size, clearly define all variables used in your model, and explain why you selected these variables. Present a specific sales forecast based on your regression model, interpret relevant coefficients, indicate which variables are statistically significant at conventional levels of significance (10%, 5%, and 1%), and discuss the fit of your model. Present up to three sets of results in your technical appendix but discuss only one set of results in your executive summary.

Advertising Strategy

Sources

Case: Anderson, Sweeney, and Williams, Management Science Supplement, Chapter 2Data: None

Assignment

See the instructions for the managerial report. In addition to providing the relevant background and motivation, address parts #1-7 in your executive summary. Include the linear programming model and graphical solution in your technical appendix. Clearly indicate the decision variables, objective function, and constraints. On your graphical solution, clearly indicate the constraints, feasible set, and optimal solution.

Production Strategy

Sources

Case: Anderson, Sweeney, and Williams, Management Science Supplement, Chapter 2Data: None

Assignment

See the instructions for the managerial report. In addition to providing the relevant background and motivation, address parts #1-3 in your executive summary. Also discuss the potential implications of the 30% dealer discount on the optimal product mix. Include the linear programming model and graphical solution in your technical appendix. Clearly indicate the decision variables, objective function, and constraints. On your graphical solution, clearly indicate the constraints, feasible set, and optimal solution.