Regression Problem: 
A senior financial analyst with Ace Gadgets (AG) is attempting to get a better grasp on sales forecasting for AG’s new franchises. She has obtained various details for 27 existing franchises including (see associated spreadsheet): 

From her recollection of her undergraduate course in statistics, she thought of regression analysis as a possibility in modeling new franchise sales. She has enlisted your help in this modeling task and has provided you with this list of questions. 

1. What is the correlation between the above variables and sales? 
2. Which variable appears to have the strongest relationship with sales? Why do you suggest this 
variable? 
3. Create a scatterplot between the variable that you selected in requirement 2 and sales. 
Properly label your chart. 
4. Add a trend line to the requirement 3 chart along with the regression equation and R2. 
5. Interpret (in layman’s language) what the equation means and what the R2 means. Remember that the senior analyst (senior ˜ old) hasn’t had a course in statistics in several years and needs an interpretation that is understandable. Be sure to include all elements of the equation. 
6. Using the analysis toolpak add-in, run regression analysis using the variable that you selected in requirement 2. 
7. Using the output from requirement 6, is this variable statistically significant in predicting sales? What specifically on the output allows you to reach this conclusion1? 
8. Which variables from the above list are useful in predicting sales? Why? 
9. Using an appropriate Excel function, if a new franchise decided to carry $300,000 in inventory, what can be the expected annual sales for this franchise? Are you 100% confident in your answer? Why or why not? 

NOTE: “a general rule of thumb . . . for large samples, a t-statistic greater than about 2.00 is significant at the 95% confidence level.” I personally do not look at the t-statistic but 
rather at the p-value – this is much more useful than an absolute t-statistic value.