Consider a regression model predicting the Sales per visit in dollars for customers visiting a store using the customer's age in years, number of children at home, number of visits to the store per week, their salary in dollars, and the distance they live from the store, in miles. Here is the regression output, estimated on a random sample of customers. SUMMARY OUTPUT DV: Sales per visit Regression Statistics Multiple R 0.9522 R Square 0.9066 Adjusted R Square 0.8820 Standard Error 14.1424 Observations 25 ANOVA Significance df 5S MS F F Regression 5 36888.28 7377.657 36.887 3.835E-09 Residual 19 3800.141 200.0074 Total 24 40688.43 Standard Upper Coefficients Error t Stat P-value Lower 95% 95% Intercept 3.9437 27.46422 0.143595 0.88733 53.5395 61.427 age -0.03316 0.286263 -0.11583 0.90900 -0.6323 0.566 children at home 4.908574 1.330455 3.689396 0.001557 2.1239 7.693 visits per week -1.85384 1.441121 -1.28639 0.21376 -4.8701 1.162 salary 2.90106 0.864368 3.356281 0.003316 1.09192 4.710 distance 0.222762 0.69111 0.322325 0.750723 -1.2238 1.669