The data required for these cases is in the file Store24AB.xls.
Summary Statistics: Definitions of the different variables in the data are provided in the case itself. Briefly discuss the summary statistics presented in Exhibit 3 in Store24A and Exhibit 2 in Store24B. Use a maximum of 2 slides for this discussion. In your team’s opinion, what do the summary statistics tell us about Store24?
Doucette wants to decide whether or not to put an employee retention program in place. But first, he wants Sarah Jenkins to check whether manager tenure and crew tenure are related to store profit. Accordingly, run the three regression models per instructions given below; data for these 3 models is in the worksheet labeled Data for Case A.
Model 1: Run a full model for profit that includes both tenure and site location related variables. Tenure related variables are MTenure and CTenure. Site location related variables are population, number of competitors, street level visibility, pedestrian access, type of neighborhood, and whether a store stays open 24 hours. These variables are also defined on page 4 of the case Store24 (A). For categorical variables, be sure to use dummy variables for this and remaining models.
Interpret the coefficients for MTenure and CTenure in model 1. Which of the two variables contributes more to profits (i.e. which one has a greater $ impact)?
A common way of determining the relative importance of two independent variables is by assessing the incremental r-squared attributable to that variable. The following two models (Models 2 and 3) allow us determine the relative importance of manager and crew tenure in predicting profits at Store24.
Model 2: Run a model for profit with all of the independent variables from model 1 other than managerial tenure. The difference between the adjusted r-squared values for Models 1 and 2 is the incremental adjusted r-squared...