Case Study of Store24 (a): Managing Employee Retention
Guidance for Store24 Case A
The data required for these cases is in the file Store24A.xls.
Data Quality:
It is important to make sure the data you use is valid. An outlier data point can dramatically reduce the fit of a model, so it is critical that bad data points be moved. In the case of the Store24 data, we will assume that all managers have some experience, so remove any data points where the manager experience is zero.
Regression Analysis
First, you should 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).
First you should determine if all variables contribute to our understanding of the model. Use the p-value for each coefficient to decide (a value of 0.05 is typically used to decide whether a variable should be included). If any variables are not significant, copy the worksheet, remove the variable and run the regression again. In your report you should explain how well the model fits (e.g. describes the factors that impact profit).
It is not commonly understood how to evaluate the “impact” of the independent variables. The variables have to have a p-value that is significant (otherwise we can’t say there is a relationship), but how small the p-value is does not tell us how important the variable is. A good way to understand the impact of the variable is to find the range of values it can take, and then multiply that range by the value of the coefficient. That tells you the maximum impact that the variable can have on the problem.
Next, you must address Tom Hart’s hypothesis that manager tenure does not have a linear impact on profitability—that is, that there are diminishing returns to
The data required for these cases is in the file Store24A.xls.
Data Quality:
It is important to make sure the data you use is valid. An outlier data point can dramatically reduce the fit of a model, so it is critical that bad data points be moved. In the case of the Store24 data, we will assume that all managers have some experience, so remove any data points where the manager experience is zero.
Regression Analysis
First, you should 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).
First you should determine if all variables contribute to our understanding of the model. Use the p-value for each coefficient to decide (a value of 0.05 is typically used to decide whether a variable should be included). If any variables are not significant, copy the worksheet, remove the variable and run the regression again. In your report you should explain how well the model fits (e.g. describes the factors that impact profit).
It is not commonly understood how to evaluate the “impact” of the independent variables. The variables have to have a p-value that is significant (otherwise we can’t say there is a relationship), but how small the p-value is does not tell us how important the variable is. A good way to understand the impact of the variable is to find the range of values it can take, and then multiply that range by the value of the coefficient. That tells you the maximum impact that the variable can have on the problem.
Next, you must address Tom Hart’s hypothesis that manager tenure does not have a linear impact on profitability—that is, that there are diminishing returns to