Target Corporation is testing a multiple linear regression model to predict the average number of times any particular customer will visit a Target store in any given month. The model is based on data from the following variables: Variable Description Age Customer's age Gender 1 if customer is female, 0 if male Income Annual household income of customer's household (in $1,000s) Distance Distance customer lives from closest Target store (in miles) Visits Customer's average number of monthly visits to Target over the past twelve months Sample Avg 48.93 0.73 56.02 3.14 8.05 Consider the Excel output shown on this and the following page: Age Gender Income Distance Visits Age 1 Gender -0.053 1 Income 0.000 -0.002 1 Distance 0.018 0.004 0.005 1 Visits 0.400 0.373 0.203 -0.065 1 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.60 0.36 0.36 3.33 1000 Intercept Age Gender Income Distance Coefficients Standard Error -0.93 0.49 0.10 0.01 3.70 0.24 0.04 0.00 -0.20 0.07 t Stat -1.90 16.64 15.64 8.05 P-value 0.06 0.00 0.00 0.00 Lower 95% Upper 95% -1.89 0.03 0.09 0.11 3.23 4.16 0.03 0.05 -0.33 -0.07 -2.95 0.00 Scatter Plot of Residuals on Predicted Visits 15 10 5 Residual 0 15 20 -5 -10 Predicted Visit Histogram of Residuals 250 200 150 Frequency "T 100 50 2-6- -5--3 -3-1 com -1-1 1-3 5.7 7-9 -7--5 9-11 11-13 13-15 (a) What is the sample size? (b) Which independent variable has the weakest correlation with Visits? (c) Use a single sentence to describe the base case of the regression model. [Hint: It does not correspond to the situation when all variables are 0.]