Pam & Sue Regression Analysis
Multiple Regression Project:
Forecasting Sales for Proposed New
Sites of Pam and Susan’s Stores
Pam and Susan’s is a discount department store that currently has 250 stores, most of which are located throughout the southern United States. As the company has grown, it has become increasingly more important to identify profitable locations. Using census and existing store data, a multiple regression equation will be used to forecast potential sales, and therefore which proposed new site location will be more profitable.
The data set has 37 independent variables. This includes 7 categorical variables for competitive type and 30 numerical categories. There…show more content…
Check for trends or patterns in the forecasting errors
To verify that the forecasting errors follow a normal distribution, construct a histogram of the residuals from the final regression model. As shown below in Graph (ii), the histogram of the residuals, defined as the difference between the actual and forecasted values, resembles a normal distribution.
Checking for trends in the residuals, shown below in Graph (iii), indicates a random distribution of the forecasting errors against predicted sales. If this scatter plot had shown a general trend or pattern it might indicate that the data does not follow the general trend of the regression line.
If your technical assumptions are invalid you may still be able to use the multiple regression equation for forecasting. However, using such a model might indicate that you are using the incorrect variables. It might be wise to repeat the process until your technical assumptions are true.
Lastly, once you have completed the steps outlined above, you can interpret the results and summarize your findings. Table (ii) is a detailed summary of the final output from the regression analysis. As all p-values are less than 0.05, you can conclude that these variables are significant and should be used in the final multiple regression equations.
Final output table for multiple regression model