A credit card company wants to understand the variation in credit card charges occurred by its customers. While the company profits from these charges, it is concerned that customers with very high charges may be at greater risk of default - a bad outcome for both the customer and the company. It was decided to perform a multiple linear regression.

A random sample of n = 5000 credit card customers was obtained, with information on the following variables:

• Annual charges, in $
• Annual income, in $000s
• Age
• Household size
• Sex (1 = female, 0 = male)

Exceeded credit limit in past 12 months? (1 = yes, 0 = no).

Selected SPSS output is provided in figure 1. Analyse the regression results. In your analysis, be sure to address at least the following:

• Write out the full regression model, including any assumptions, and the estimated model.
• Interpret the estimated coefficient of the "Exceeded credit limit in past 12 month?" variable.
• Comment on the relative importance of the predictor variables.
• Briefly discuss any changes you would recommend making to the model.

Figure 1 Model Summary" Adjusted R Sid. Error of Model R R Square Square the Estimate 1 591 349 .348 4903.278836 ANOVA" Sum of Model Squares df Mean Square F Sig 1 Regression 6.439E+10 5 1.28BE+10 535.669 000 Residual 1.201E+11 4994 24042143.34 Total 1.845E+11 4999 a. Dependent Variable: Annual charges (in $) Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta Sig (Constant) 791.790 435.050 1.820 069 Annual income (in $000s) 120.882 2.468 559 48.987 000 Age -9.256 6.470 -.016 -1.431 153 Household size 528.069 33.458 180 15.783