A chain of small convenience food stores performs a regression analysis to ­explain variation in sales volume among 16 stores. The variables in the study are as follows:
Sales: Average daily sales volume of a store in thousands of dollars
Size: Floor space in thousands of square feet
Parking: Number of free parking spaces adjacent to the store
Income: Estimated per household income of the zip code area of the store
Output from a regression program (StataQuest) is shown here:

a. Write the regression equation. Indicate the standard errors of the coefficients.
b. Carefully interpret each coefficient.
c. Locate R2 and the residual standard deviation.
d. Is there a severe collinearity problem in this study?

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. regress Sale Size Parking Income Source df MS Number of Obs 1E Model | 27.1296056 3 9.04320188 Prob >F 0.0002 0.7912 + Adj R-Square = 0.739.。 7724 Residual 7.15923792 12 59660316 R-square Total34.288843 15 2.2859229 Root MSE coet . Std. Err. t p>IT] 1958 Cont . Interval] 2.547936 1.200827 2 1220.055-0684405 .164313 9772763 Sales size Farking Income 559840 2202793 1553877418 0 182.1182814 5893221 .1790576 3 310 0:006 2013673 cons 872716 1.94561S 0.449 0. 6623.366415 5111847 : correlate Sales size Parking Income obs-16) | Sales size Parking Income Gales 1.0000 Size 0.7415 1.0000 Parking .6568 0.6565 1.0000 Income 0.7148 0.4033 0.3241 1.0000