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Problem 2 1. Use lm to fit a linear regression model with mpg as the dependent variable and wt as the independent variable. 2. How much would you expect miles per gallon to decrease with a 2000 pound increase in weight of the vehicle? 3. What is the p-value for testing the null hypothesis that there is no linear association between wt and mpg? Problem 3 1. Which model of car is associated with the largest (positive or negative) residual? 2. What is the fitted value for this model? 3. Estimate the fuel efficiency of a car that weighs 3000 pounds. Problem 4 1. What percentage of the variation in mpg can be explained by knowing the weight of the car? 2. Plot the fitted values on the x-axis and the residuals on the y-axis. 3. Comment on how well the assumption of linearity is satisfied. Problem 5 1. Remove the Chrysler Imperial from the dataset and refit the linear regression model. 2. Comment on how the regression coefficients have changed. 3. What about the Chrysler Imperial might have accounted for this change in model coefficients? (lol stats homework)
