Creating a Multiple Regression

Discussion: Creating a Multiple Regression
Model
SW
Use the link in the Jupyter Notebook activity to access your Python script.
Once you have made your calculations, complete this discussion. The
script will output answers to the questions given below. You must attach
your Python script output as an HTML file and respond to the questions
below.
In this discussion, you will apply the statistical concepts and techniques
covered in this week’s reading about multiple regression. Last week’s
discussion involved a car rental company that wanted to evaluate the
premise that heavier cars are less fuel efficient than lighter cars. The
company expected fuel efficiency (miles per gallon) and weight of the car
(often measured in thousands of pounds) to be correlated. The company
also expects cars with higher horsepower to be less fuel efficient than
cars with lower horsepower. They would like you to consider this new
variable in your analysis.
In this discussion, you will work with a cars data set that includes the
three variables used in this discussion:
Miles per gallon (coded as mpg in the data set)
Weight of the car (coded as wt in the data set)
Horsepower (coded as hp in the data set)
The random sample will be drawn from a CSV file. This data will be unique
“# Listen !

to you, and therefore your answers will be unique as well. Run Step 1 in
the Python script to generate your unique sample data.
In your initial post, address the following items:
1. Check to be sure your scatterplots of miles per gallon against
horsepower and weight of the car were included in your
attachment. Do the plots show any trend? If yes, is the trend what
you expected? Why or why not? See Steps 2 and 3 in the Python
script.
2. What are the coefficients of correlation between miles per gallon
and horsepower? Between miles per gallon and the weight of the
car? What are the directions and strengths of these coefficients?
Do the coefficients of correlation indicate a strong correlation,
weak correlation, or no correlation between these variables? See
Step 4 in the Python script.
3. Write the multiple regression equation for miles per gallon as the
response variable. Use weight and horsepower as predictor
variables. See Step 5 in the Python script. How might the car rental
company use this model?
In your follow-up posts to other students, review your peers’ results and
provide some analysis and interpretation:
1. Review your peer’s multiple regression model (#3 in their initial
post). What is the predicted value of miles per gallon for a car that
has 2.78 (2,780 lbs) weight and 225 horsepower? Suppose that this
car achieves 18 miles per gallon, what is the residual based on this
actual value and the value that is predicted using the regression
equation?
2. How do the plots and correlation coefficients of your peers
compare with yours?
3. Would you recommend this regression model to the car rental
company? Why or why not?