## Simple Linear Regression

5-3 Discussion: Simple Linear Regression
SW
Use the link in the Jupyter Notebook activity to access your Python script.
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
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.
“# Listen !

Rubrics
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.
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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