6-4 Creating a Multiple Regression Model
docx
keyboard_arrow_up
School
Southern New Hampshire University *
*We aren’t endorsed by this school
Course
MISC
Subject
Mathematics
Date
Jan 9, 2024
Type
docx
Pages
2
Uploaded by BaronKuduPerson693
6-4 Discussion: Creating a Multiple Regression Model
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.
The MPG against Weight plot shows that as the weight of the vehicle increases, the
miles per gallon the vehicle gets decreases. This is the trend I would expect because the
heavier the vehicle is, the more gas it takes to move it.
The MPG against Horsepower plot shows that as the horsepower of a vehicle
increases, the miles per gallon the vehicle gets decreases. This is the trend I would expect
because the more horsepower the vehicle gets equates to more energy being expended which
results in more fuel being needed. Essentially, vehicles with more horsepower would get less
miles per gallon.
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.
The coefficients of correlation between miles per gallon and horsepower is -0.780595.
This indicates a strong, negative correlation between the two variables. This correlation is
negative because as the horsepower of the vehicle increases, the miles per gallon the vehicle
gets decreases. This relationship is seen via the plot graph. The correlation indicates a strong
negative correlation because the value is close to -1.00.
The coefficients of correlation between miles per gallon and the weight of the car is
-0.867975. This indicates a strong, negative correlation between the two variables. This
correlation is negative because as the weight of the vehicle increases, the miles per gallon the
vehicle gets decreases. This relationship is seen via the plot graph. The correlation indicates a
strong negative correlation because the value is close to -1.00.
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?
The multiple regression equation: Ŷ = 37.2559 – 3.8409wt
– 0.0322hp
The car rental company could use this model to determine what to charge customers for
vehicle rentals. The company would be able to more accurately price their vehicles based on the
weight and horsepower and how these factors affect miles per gallon for each vehicle.
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help