Fit a regression model with the data set Auto from the ISLR2 package. The goal is to explore the
relationship between gas mileage and the other variables in the data set. I highly recommend
reading the documentation for this data set to understand what each variable is measuring. (a)
Are there any columns that should be removed from this dataframe when considering fitting a
multiple linear regression? (b) How many predictors are there in this data set? How many of
these are quantitative? How many of these are categorical? Are there some predictors where this
distinction may not be obvious? Briefly explain. (c) Use the factor () function to convert the
variable origin to be viewed appropriately as a categorical variable. Why do we have to do this?
Also give appropriate descriptive names for each of the levels. (d) A classmate recommends
treating the other variables (cylinders and year) in this analysis as quantitative. What are some
possible reasons for doing this for the discrete variables? For the remainder of this question,
follow the classmate's recommendation. (e) Fit a multiple linear regression model with mpg as
the response variable and all the other variables (except name) as predictors (with no
interactions). Create the necessary plots to assess if the model assumptions are met. Are there
transformations you should try? (f) Your classmate suggests transforming the response by
y=y0.5. Do you agree? Briefly explain? (g) Transform the response variable with y=y0.5, and re-
fit the regression model. Create the necessary plots to assess if the model assumptions are met.
For this model, answer the following: i. How do we interpret the result of the F statistic of 392.8
?
ii. Based on the t tests, what predictor(s) will you consider dropping from the model in order to
simplify the model? iii. Drop the(se) predictor(s), re-fit the model, and compare this model with
the one from part 2g using an appropriate hypothesis test. Which of these two models should you
use? Perform pairwise comparisons (using Tukey's method) to see if the response variable differs
by the origin of the car, while controlling for the other predictors. How do American, European,
and Japanese cars differ in their fuel efficiency? Which origin is the most fuel efficient, and
which is least (while controlling for other predictors)?.
Fit regression model explore relationship between gas mileage variables Auto data
1. Fit a regression model with the data set Auto from the ISLR2 package. The goal is to explore the
relationship between gas mileage and the other variables in the data set. I highly recommend
reading the documentation for this data set to understand what each variable is measuring. (a)
Are there any columns that should be removed from this dataframe when considering fitting a
multiple linear regression? (b) How many predictors are there in this data set? How many of
these are quantitative? How many of these are categorical? Are there some predictors where this
distinction may not be obvious? Briefly explain. (c) Use the factor () function to convert the
variable origin to be viewed appropriately as a categorical variable. Why do we have to do this?
Also give appropriate descriptive names for each of the levels. (d) A classmate recommends
treating the other variables (cylinders and year) in this analysis as quantitative. What are some
possible reasons for doing this for the discrete variables? For the remainder of this question,
follow the classmate's recommendation. (e) Fit a multiple linear regression model with mpg as
the response variable and all the other variables (except name) as predictors (with no
interactions). Create the necessary plots to assess if the model assumptions are met. Are there
transformations you should try? (f) Your classmate suggests transforming the response by
y=y0.5. Do you agree? Briefly explain? (g) Transform the response variable with y=y0.5, and re-
fit the regression model. Create the necessary plots to assess if the model assumptions are met.
For this model, answer the following: i. How do we interpret the result of the F statistic of 392.8
?
ii. Based on the t tests, what predictor(s) will you consider dropping from the model in order to
simplify the model? iii. Drop the(se) predictor(s), re-fit the model, and compare this model with
the one from part 2g using an appropriate hypothesis test. Which of these two models should you
use? Perform pairwise comparisons (using Tukey's method) to see if the response variable differs
by the origin of the car, while controlling for the other predictors. How do American, European,
and Japanese cars differ in their fuel efficiency? Which origin is the most fuel efficient, and
which is least (while controlling for other predictors)?
