Hello,

I need help with these questions, please.

Market	Revenue	Facebook Ads	TV Ads
Allegheny	200.5	4.9	7.5
Altoona	111.4	4.1	5.1
Bloomington	198.0	7.9	6.8
Bucks	120.2	4.5	3.3
Canton	166.4	5.3	5.3
Charleston	136.5	4.9	4.1
Deerborn	74.8	5.0	2.5
Erie	137.8	4.6	5.0
Farmingdale	90.8	4.7	3.8
Harrisburg	284.4	6.0	9.4
Kalamazoo	125.0	5.4	3.7
Lancaster	56.5	4.0	4.0
Petersburg	78.8	2.9	5.4
Scranton	150.0	4.0	4.8
Terre Haute	129.9	3.7	3.9
Wheeling	86.0	3.1	3.7

5. Refer to the Excel "Rust Belt" data posted on eLearning: (18) a. Develop an estimated regression equation with the Revenue serving as the dependent variable and Facebook Ads and TV Ads serving as explanatory variables. Write out this estimated equation (use the estimate values!) to explain Revenue. Do not use generic labels like 'x1' when you can use problem-specific labels. b. Show the residual plots where residuals are plotted against each explanatory variable separately. Comment on whether you can proceed with statistical inference based on what you see in the plots. (Hint: Don't go looking for trouble!) c. Provide an interpretation for the three coefficient estimates that you calculated in part "a". (don't forget the intercept). d. What would your regression model predict the revenue amount to be in a market with 0 TV Ads and 0 Facebook Ads? Is this a meaningful prediction? Answer, in a sentence, why or why not. e. Provide a 90% confidence interval for your estimate of the Facebook Ads Coefficient. Interpret exactly what this 90% confidence interval means. f. What statistic and p-value would you use to test the specific null hypothesis that: Ho: Facebook Ads = TV Ads = 0? Do you reject or fail to reject this null hypothesis? g. If I asked you to consider a "backward selection" approach which only included predictors to the model that where you were confident at the 99% level, then would your answer to part "a" change? If so, what would it change to? h. What percentage of the variation in Revenue can be explained by the model you developed on part "a"? How much more variation does this model explain than a model which uses only TV Ads to help predict Revenue?