A mail-order company collected data on 250 customers and wanted to understand current year spending amounts with multiple regression. Data were collected on the following variables: ? Log(AmountSpent) - the dependent variable, the logarithm of annual expenditure in ? Age - age of the customer, in years ? Gender - gender of the customer, male = 1 and female = 0 ? Salary - customer's household salary, in ? Married - customer's marital status, married = 1 and otherwise = 0 ? OwnHome - customer owns home outright = 1 and otherwise = 0. Selected SPSS output is provided in Figure 2 (on the next page). Analyse the regression results. In light of the regression results, propose any changes you would make to the model, including any other explanatory variables which you would consider using, or any you would remove from the existing model. Keep in mind the company's objective of understanding customer spending.

Figure 2 Model Summary" Adjusted R Std. Error of Model R R Square Square the Estimate .686 471 460 6338712849 ANOVA Sum of Model Squares df Mean Square F Sig. 1 Regression 87.153 5 17.431 43.382 000b Residual 98.037 244 402 Total 185.190 249 Coefficients Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta Sig (Constant) 5.608 149 37.657 000 Age 001 003 010 212 .832 Gender .128 083 074 1.553 122 Salary 1.610E-5 .000 553 8.265 000 Married .179 .104 .104 1.722 086 OwnHome .152 .091 088 1.677 095Histogram Dependent Variable: Log(AmountSpent) Mean = 2.64E-15 40- Std. Dev. = 0.990 N = 250 30- Frequency 20- 10- Regression Standardized Residual