A researcher investigating the determinants of hamburger sales in a particular restaurant in the US fits the following model:

Sales_? = ?_? + ?_?Price_? + ?_?Advert_? + e_?

The variables are defined as below.

Sales	Monthly sales revenue of hamburgers sales in thousands of dollars
Price	Price of hamburgers in dollars
Advert	Advertising expenditure in dollars

summarize sales price advert Variable | Obs Mean Std. Dev. Min Max sales 75 77.37467 6. 488537 62 . 4 91.2 price 75 5. 6872 . 518432 4. 83 6.49 advert | 75 1. 844 8316769 .5 3.1 regress sales price advert Source SS df MS Number of obs 75 F (2, 72) 29 .25 Model | 1396.53893 2 698.269465 Prob > F 0 . 0000 Residual 1718 . 94294 72 23. 8742075 R-squared 0. 4483 Adj R-squared 0. 4329 Total | 3115. 48187 74 42. 1011063 Root MSE 4 . 8861 sales Coef. Std. Err. t P>ItI [95% Conf. Interval] price | -7. 907854 1. 095993 -7.22 0. 000 -10.09268 -5. 723032 advert | 1. 862584 . 6831955 2.73 0 . 008 500659 3. 22451 cons 118 .9136 6.351638 18.72 0 . 000 106 .2519 131. 5754 predict u, residuals sktest u Skewness/Kurtosis tests for Normality ---- joint Variable | Obs Pr (Skewness) Pr (Kurtosis) adj chi2 (2) Prob>chi2 -+- u 75 0. 7845 0. 4974 0 . 55 0. 7614 estat vif Variable | VIF 1/VIF advert 1.00 0 . 999305 price 1.00 0 . 999305 Mean VIF | 1.00