9. Which do you think is the best regression to use for predicting future sales values? Explain your choice of answer. (Note: there may be more than one right answer, depending on your explanation - be sure to justify your choice!) Your explanation should include both mathematical reasoning related to the data, and practical reasoning about the real-life situation the data describes.

10. Graph the Residuals plot for your chosen regression, and sketch the graph below or attach the image file. Can you draw any conclusions about your data from examining its residuals?

11. Suppose a new location opens in an area with a population of 144,000, an average income of $36,000, an average age of 27, and $2,000 spent on advertising in the previous month. a. Use your chosen regression to predict Gross Sales for the month at the new location. b. Suppose actual Gross Sales for the month were $420,624. Does this make sense, given your model and predicted value?

12. Using your chosen regression model, a. Identify the slope and explain what it means, in the context of the model. b. Identify the initial value or y-intercept and explain what it means, in the context of the model.

13. Take a step back, and look at the data and the different regression and correlation values you've found. Is there anything you can think of that may result in an improved model over the one you've found? Do you have any suggestions for future research that may be done, or data that may be collected, to make more accurate predictions in the future?