5-3 Discussion: Simple Linear Regression

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 correlation coefficient and simple

linear regression. A car rental company wants to evaluate the premise that

heavier cars are less fuel efficient than lighter cars. In other words, the

company expects that fuel efficiency (miles per gallon) and weight of the

car (often measured in thousands of pounds) are correlated. Performing

this analysis will help the company optimize its business model and charge

its customers appropriately.

In this discussion, you will work with a cars data set that includes two

variables:

Miles per gallon (coded as mpg in the data set)

Weight of the car (coded as wt 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:

“# Listen !

Rubrics

Discussion Rubric: Undergraduate

1. You created a scatterplot of miles per gallon against weight; check

to make sure it was included in your attachment. Does the graph

show any trend? If yes, is the trend what you expected? Why or

why not? See Step 2 in the Python script.

2. What is the coefficient of correlation between miles per gallon and

weight? What is the sign of the correlation coefficient? Does the

coefficient of correlation indicate a strong correlation, weak

correlation, or no correlation between the two variables? How do

you know? See Step 3 in the Python script.

3. Write the simple linear regression equation for miles per gallon as

the response variable and weight as the predictor variable. How

might the car rental company use this model? See Step 4 in the

Python script.

4. What is the slope coefficient? Is this coefficient significant at a 5%

level of significance (alpha=0.05)? (Hint: Check the P-value, ,

for weight in the Python output.) See Step 4 in the Python script.

In your follow-up posts to other students, review your peers’ calculations

and provide some analysis and interpretation:

1. How do their plots and correlation coefficients compare with

yours?

2. 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.

To complete this assignment, review the Discussion Rubric.

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