Store 24

Topics: Regression analysis, Statistics, Linear regression Pages: 6 (1743 words) Published: August 23, 2011
Case Study: Managing Employee Retention
Relationship Between Employee and Manager Tenure and Store Performance

One of the first steps in analysis of the data is to make a comparison of the 10 most profitable stores and the 10 least profitable stores. Hart claimed that the manager and crew tenure in the most profitable stores was almost four times the level of that in the least profitable stores. This analysis is however based solely on the summary statistics for those ten stores in each category. Taking a closer look at the results for the individual stores would suggest that the relationship is not so simple. For example looking at store 47, which is at the bottom of the ten most profitable list, both the crew and manager tenure are very low in comparison to the other stores in the list. This means that it would not be expected that store 47 would be so profitable if the manager and crew tenure were the only influencing factor on profitability. In fact, the levels of tenure in this store are lower the average of those from the ten lowest profit stores, which would indicate that very low levels of profit would be expected from the store. A more in-depth analysis is therefore required.

There is further evidence that neither manager tenure nor employee tenure alone significantly influences the profitability of each store. This may seen in the scatter-plots which are included below as Figure 1 and Figure 2. It appears clear from Figure 1 that most managers have been at their store for less than 50 months, and the mean which is given for manager tenure is 45.3. This mean may however be slightly higher than the median would be given that there are several exceedingly high values which would influence the calculation of the mean. A similar pattern may be seen in Figure 2, where it is clear that most employees have lower than 20 months retention, with the mean given as 13.9 months.

What is also apparent from these plots is that neither variable may significantly explain variability in the profitability of a store. This is evident in the r-squared value, which indicates that only 19.6% of variation in profitability may be explained by manager tenure alone. Similarly, only 6.7% of this variation may be explained by employee tenure alone. It therefore is apparent that there are multiple variables which may influence profitability.

In order to assess whether a manager and employee tenure combine to influence profitability a multiple regression model may be formed using these two variables. The results of this regression may be seen in Table 1.

[pic]
Figure 1: Correlation between manager tenure and store profitability [pic]
Figure 2: Correlation between employee tenure and store profitability

From Table 1 it may be seen that when considering both manager and employee tenure there is still only 21.7% of variation in profitability which these variables may explain. This therefore indicates that there must be other factors which exert an influence. It would therefore be suitable to construct a multiple regression model which takes into account other variables for which data is available. Although it was originally believed that the relationship may be non-linear, this still does not significantly increase the r-squared value.

Table 1: Regression model in which manager tenure and employee tenure are included |Regression Statistics |
|Multiple R |0.465617551 |
|R Square |0.216799704 |
|Adjusted R Square |0.19504414 |
|Standard Error |80212.7404 |
|Observations |75 |

Multiple Regression Model
The first multiple regression model which is included is that which includes all of the variables for which data are available. These variables are:
Y: Profitability
X1: Manager tenure
X2: Employee tenure
X3: Population near store
X4:...

Bibliography: Berenson, M.L., Levine, D.M. & Krehbiel, T.C. (2008) Basic Business Statistics. 11th Edition. Philadelphia, PA: Prentice Hall.
Kazmier, L.J. (2003) Schaum’s Outline of Business Statistics. New York: McGraw-Hill.
Levine, D.M., Stephan, D.F., Krehbiel, T.C. & Berenson, M.L. (2007) Statistics for Managers Using Microsoft Excel. Philadelphia, PA: Prentice Hall.
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