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?

Unnamed: 0 mpg cyl disp hp drat wt qsec vs am gear carb 11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 20 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1

Step 2

Step 3:

Step 4 mpg 1.000000 -0.863567 -0.770635 wt -0.863567 1.000000 0.652029 hp -0.770635 0.652029 1.000000

Step 5: Multiple regression model to predict miles per gallon using weight and horsepower OLS Regression Results ============================================================================== Dep. Variable: mpg R-squared: 0.821 Model: OLS Adj. R-squared: 0.807 Method: Least Squares F-statistic: 61.79 Date: Mon, 07 Feb 2022 Prob (F-statistic): 8.39e-11 Time: 14:02:39 Log-Likelihood: -70.647 No. Observations: 30 AIC: 147.3 Df Residuals: 27 BIC: 151.5 Df Model: 2 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ Intercept 37.2281 1.710 21.767 0.000 33.719 40.737 wt -3.8819 0.664 -5.844 0.000 -5.245 -2.519 hp -0.0317 0.009 -3.359 0.002 -0.051 -0.012 ============================================================================== Omnibus: 4.534 Durbin-Watson: 1.655 Prob(Omnibus): 0.104 Jarque-Bera (JB): 3.485 Skew: 0.832 Prob(JB): 0.175 Kurtosis: 3.129 Cond. No. 598. ==============================================================================

Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.