Case 1: Case Study In this case you will study the effect that various factors might have on the average automobile insurance rate in a state. Data were obtained from the Statistical Abstract of the United States for each of the 50 states on the response variable, average automobile insurance rate, and five predictor variables: population density, automobile theft rate, automobile death rate per 100 million miles driven, average drive time to work, and average cost of a day's stay in a hospital.

1. Draw a scatterplot matrix of the data for the six variables. What do these scatter plots tell you about the relationship among the variables?

2. Does a multiple linear regression equation relating insurance rate to the five predictor variables seem appropriate for these data? Explain your answer.

3. Find the multiple linear regression equation relating the response variable of insurance rate to the five predictor variables.

4. Interpret the sample regression coefficients.

5. Determine the proportion of variation in the observed insurance rates that can be accounted for by the multiple linear regression equation in the five predictor variables.

6. Should all of the predictors remain in the regression equation?

7. Construct residual plots and assess the appropriateness of the multiple linear regression equation.

8. Construct Residual plot to assess the assumptions of constant conditional standard deviation and normality.

9. Do you think these predictor variables do a good job of predicting the response?

the data attached

Case 2: Consumer Research, Inc. Consumer Research, Inc., is an independent agency that conducts research on consumer attitudes and behaviors for a variety of firms. In one study, a client asked for an investigation of consumer characteristics that can be used to predict the amount charged by credit card users. Data were collected on annual income, household size, and annual credit card charges for a sample of 50 consumers.

Attachment:- Case-Automobile-Insurance-Rate-Data.xlsx