A) Insurance premium (charge) for applicants can be modeled using a multiple regression model with five independent variables. In the dataset, in the case of sex, 1 indicates female, and 0 indicates male. Similarly, for smoking 1 indicates smoker while o indicates non-smoker. The children variable indicates the number of children, the applicant has. Based on the output provided below:

I. Please write the regression equation for a person, who is smoker and male.

II. Please write the regression equation for a person, who is non-smoker and female.

III. Which of the independent variable is most important in deciding the premium?

IV. Does the model suffer from multi-collinearity problem?

Descriptive Statistics Std. Mean Deviation N charges 13270.4223 12110.01124 1338 age 39.21 14.050 1338 sex .49 500 1338 bmi 30.6634 6.09819 1338 children 1.09 1.205 1338 smoker .20 404 1338 Model Summary Adjusted R Std. Error of Model R R Square Square the Estimate 866 .750 749 6069.72525 a. Predictors: (Constant), smoker, bmi, children, sex, age b. Dependent Variable: chargesANOVA Sum of Model Squares df Mean Square F Sig Regression 1.470E+11 5 2.940E+10 798.019 .000 Residual 4.907E+10 1332 36841564.60 Total 1.9618+11 1337 a. Dependent Variable: charges b. Predictors: (Constant), smoker, bmi, children, sex, age Coefficients Standardized Unstandardized Coefficients Coefficients Collinearity Statistics Model B Std. Error Beta Sig Tolerance VIF 1 (Constant) -12181.102 963.902 -12.637 000 age 257.735 11.904 299 21.651 000 985 1.015 sex 128.640 333,361 005 386 700 991 1.009 bmi 322.364 27.419 .162 11.757 000 986 1.015 children 474.411 137.856 047 3.441 001 998 1.002 smoker 23823.393 412.523 .794 57.750 000 .994 1.006 a. Dependent Variable: charges