Yummy Lunch Restaurant needs to decide the most profitable location for their business expansion. Marketing manager plans to use a multiple regression model to achieve their target. His model considers yearly revenue as the dependent variable. He found that number of people within 2KM (People), Mean household income(income), no of competitors and price as explanatory variables of company yearly revenue.

The following is the descriptive statistics and regression output from Excel.

Revenue People Income Competitors Price Mean 343965.68 5970.26 41522.96 2.8 5.68 Standard Error 5307.89863 139.0845281 582.1376385 0. 142857 0.051030203 Median 345166.5 6032 41339.5 3 5.75 Mode #N/A 5917 #N/A 3 6 Standard Deviation 37532.51115 983.47613 4116.334718 1.010153 0.360838027 Sample Variance 1408689393 967225.2984 16944211.51 1.020408 0.130204082 Sum 17198284 298513 2076148 140 284 Count 50 50 50 50 50 SUMMARY OUTPUT Regression Statistics Multiple R 0.77 R Square A Adjusted R Square B Standard Error 25139.79 Observations 50.00 ANOVA Significance df SS MS F F Regression 40585376295 H 3.0831E-08 Residual 28440403984 G Total 69025780279 Coefficients Standard Error t Stat P-value Intercept 68363.1524 78524.7251 -0.8706 0.3886 People 6.4394 3.7051 0.0891 Income 7.2723 0.9358 0.0000 Competitors -6709.4320 3818.5426 K 0.0857 Price 15968.7648 10219.0263 L 0. 1251