4. Statistical Model to Explain Insurance Premium: * Utilize multiple linear regression to explore the relationship between insurance premium and all other variables (age. sex. BMI, children, smoking habits, and region). * Interpret the coefficients of the regression model to identify which variables significantly affect insurance premiums and gquantify their impact. * Consider assessing multicollinearity among independent variables and address it if necessary to ensure the model's validity. * Validate the predictive accuracy of the model by assessing the residuals. 5. Predicting Insurance Premium for New Customers: * Use the coefficients from the regression model to predict insurance premiums for new customers based on their characteristics. How much will the insurance cost for a new customer who is 25 years old, female, has a bmi of 30, has no children, doesn't smoke, and lives in California? How much the same woman would pay if she was a smoker? * Provide predictions for different scenarios of choice, such as varying age, BMI, smoking habits, etc., and discuss how these factors influence INsurance premiumes