Suppose Mortgage Life would like to develop a regression model that would predict a person's credit score based on their weekly income, years living in their current residence, and gender (Male = 0, Female = 1). The following data represent the values for the independent and dependent variables from a random sample of adults. Credit Score Income Residence Gender 546 $484 1 Male 601 $636 18 Male 610 $953 13 Male 829 $1,949 19 Female 643 $1,169 12 Male 652 $1,391 8 Male 787 $1,922 11 Female 669 $1,402 9 Male 775 $1,873 5 Female 688 $1,485 11 Male 740 $1,846 3 Female 690 $1,521 17 Male 710 $1,716 13 Male 720 $1,738 11 Female 725 $1,801 15 Female 690 $1,679 12 Male a. Write the estimated regression equation. b. Evaluate the statistical significance of the regression coefficients. Be sure to write the null and alternative hypotheses. Carefully interpret the results. c. Predict the credit score for a male that has lived in his current residence for 10 years and has a weekly income of $1607. d. Test for the overall significance of the model. Be sure to write the null and alternative hypothesis. e. Carefully interpret the adjusted multiple coefficient of determination.