PLEASE TYPE RESPONSE.

NO HANDWRITTEN.

I have attached STEPS 1 - 5. They are need for responces. Thank you so much.

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.

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)

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?

Cars data frame (showing only the first five observations) Unnamed: 0 mpg cyl disp hp drat wt qsec vs am gear carb 26 Porsche 914-2 26.0 120.3 91 4.43 2.140 16.70 0 1 5 2 22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 3MPG against Weight 35 30 25 MPG 20 15 10 15 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 Weight (1000s lbs)\fmpg wt hp mpg 1. 000000 -0. 869747 -0.776206 wt -0. 869747 1. 000000 0. 649313 hp -0. 776206 0. 649313 1. 000000OLS Regression Results Dep. Variable: mpg R-squared: 0. 834 Model: OLS Adj . R-squared: 0. 821 Method : Least Squares F-statistic: 67.71 Date: Mon, 05 Apr 2021 Prob (F-statistic) : 3. 02e-11 Time : 14:00:05 Log-Likelihood: -69. 819 No. Observations: 30 AIC : 145.6 Of Residuals : 27 BIC: 149.8 Df Model: 2 Covariance Type: nonrobust coef std err t P>| t| [0. 025 0. 975] Intercept 37. 6345 1. 648 22. 840 0.000 34.254 41 . 015 wt -3. 9548 0. 645 -6. 129 0. 000 -5.279 -2. 631 hp -0. 0323 0. 009 -3. 544 0. 001 -0. 051 -0. 014 Omnibus : 4. 869 Durbin-Watson: 1. 702 Prob (Omnibus ) : 0. 088 Jarque-Bera (JB) : 3. 667 Skew: 0. 848 Prob ( JB) : 0. 160 Kurtosis: 3. 241 Cond. No. 585. Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified