Overview The ISLR2 library contains a dataset called Auto. This dataset contains a list of American cars, with variables for the mpg, horsepower, weight, and acceleration. If you are unfamiliar with car terminology. MPG is a measure of fuel efficiency (more mpg, more efficient) and horsepower is a measurement of how much "power" an engine produces (more horsepower, more power). The car weights are measured in pounds. This second lab will require a new variable. The "origin" variable is a categorical variable for where the car was made. 1 Is American, 2 is European, 3 is Japanese. To complete this lab: create a Word document (or similar document) with results for all questions in the Lab Steps section. Include photos of plots. Set-up 1. Load the Auto dataset Lab Steps 1. Plot a "boxplot" with the y = mpg and the x = origin 2. Which origin seems to have the more fuel efficient automobiles? 3. Plot a "boxplot" with the y = weight and the x = origin 4. What do you notice about the weight of the cars for different origins? 5. Create a linear model with response = mpg, and the single explanatory variable origin 6. How fuel efficient do you predict a Japanese car will be on average, based on this model? 7. Create a linear model with response = mpg, and explanatory variables origin and weight 8. Predict the average mpg for two cars: a Japanese car that weighs 3000 and an American car that weights 3000 9. How much more fuel efficient are Japanese cars? Is the result what you expect