# SNHU Horsepower And Weight of The Car Are Significant Predictors Of MPG Analysis

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zrpunabvq

Engineering

Southern New Hampshire University

## Description

• 6-3 Jupyter Notebook (Discussion Prep)External Learning ToolThis activity will take you to the Jupyter Notebook containing the Python scripts for your Module One discussion. It is highly recommended that you read through the discussion prompt before completing your work in this notebook. When you are finished completing and running the Python scripts, begin work on your initial discussion post.Note: This task is not graded, but you will be required to attach your completed Jupyter notebook to your discussion post in the next activity.
• 6-4 Discussion: Creating a Multiple Regression ModelDiscussion Topic

Starts Jun 5, 2021 8:59 PM
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 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?
Remember to attach your Python output and respond to all questions in your initial and follow-up posts. Be sure to clearly communicate your ideas using appropriate terminology.

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Module 6 Discussion
For the discussion, analysis was carried out to evaluate whether horsepower and weight of the
car are significant predictors of miles per gallon (MPG). The obtained results are as discussed
below.
Scatterplot
The first analysis involved plotting scatterplots of miles per gallon against horsepower and miles
per gallon against the weight of car. The obtained plots are as shown below.

Figure 1: Scatterplot of Miles per Gallon (MPG) against the Weight of the car.

Figure 2: Scatterplot of Miles per Gallon (MPG) against Horsepower
The scatterplots show that there is a negative linear relationship between the weight of a car and
the MPG. There is also a negative linear relationship between horsepower and the MPG. MPG
decreases as horsepower increases, and also MPG decreases as the weight of car increases and
vice versa. The results were as expected because as the weight of a car increases, then the
amount of fuel consumed per mile is more, thereby resulting in a lower MPG. In terms of
horsepower, the results were also as expected. The more power a car needs, then the more fuel it
uses per mile, thereby resulting in a lower MPG.
Correlation coefficients
The correlation coefficient confirms the results observed from the scatterplots. The correlation
coefficient between the weight of a car and the MPG is equal to -0.868. The correlation
coefficient indicates that there is a strong negative linear relationship between the weight of a car

and the MPG. MPG therefore strongly linearly decreases as the weight of a car increases. The
correlation coefficient between horsepower and the MPG is equal to -0.786. The correlation
coefficient also indicates that there is a strong negative linear relationship between the
horsepower of a car and the ...

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