Create a Managerial Report summarizing and analyzing the data set.
Question Description
Chapter 12 Case Problem Simple Linear Regression Modeling
Manufacturers are continually assessing how much shelf space to buy in retail stores to best maximize their sales. The manufactures believe that more shelf space equates to more sales. Data was collected over a several year period to test if there is in fact a relationship between shelf space (in feet) and sales (in millions). Below is a sample of the data from the data set. The variable ‘Display Feet’ represents the average number of feet of shelf space that the manufacturer bought in each retail store. The variable ‘Sales’ is the amount of sales in millions that were generated from all retail stores in which shelf space was bought.
Display Feet |
Sales (in millions) |
1 |
150.8 |
6 |
358.41 |
7 |
382.3 |
4 |
279.9 |
6 |
300.4 |
7 |
398.3 |
5 |
279.9 |
2 |
204.9 |
5 |
284.8 |
7 |
357.8 |
… |
… |
… |
… |
… |
… |
5 |
259.7 |
Download the complete data set from the course website.
Create a Managerial Report summarizing and analyzing the data set.
- Construct a descriptive summary for each variable in the data set.
- Is this data set a good data set for simple linear regression? Explain.
- Use regression analysis to develop an estimated regression equation that could be used to predict the amount of sales based on the amount of display feet bought.
- What conclusions and recommendations can you derive from your analysis using an alpha of 0.05?
- How much in sales can a manufacturer expect to earn if they buy 6.8 feet of shelf space?
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