Question: 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

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?

 OLS Regression Results

==============================================================================

Dep. Variable:mpgR-squared:0.840

Model:OLSAdj. R-squared:0.828

Method:Least SquaresF-statistic:70.64

Date:Tue, 06 Oct 2020Prob (F-statistic):1.87e-11

Time:21:43:59Log-Likelihood:-69.404

No. Observations:30AIC:144.8

Df Residuals:27BIC:149.0

Df Model:2

Covariance Type:nonrobust

==============================================================================

coefstd errtP>|t|[0.0250.975]

------------------------------------------------------------------------------

Intercept37.61811.61823.2570.00034.29940.937

wt-3.87090.634-6.1020.000-5.173-2.569

hp-0.03340.009-3.6840.001-0.052-0.015

==============================================================================

Omnibus:4.325Durbin-Watson:2.598

Prob(Omnibus):0.115Jarque-Bera (JB):3.132

Skew:0.782Prob(JB):0.209

Kurtosis:3.246Cond. No.588.

==============================================================================

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?   OLS Regression Results ============================================================================== Dep. Variable: mpg R-squared: 0.840 Model: OLS Adj. R-squared: 0.828 Method: Least Squares F-statistic: 70.64 Date: Tue, 06 Oct 2020 Prob (F-statistic): 1.87e-11 Time: 21:43:59 Log-Likelihood: -69.404 No. Observations: 30 AIC: 144.8 Df Residuals: 27 BIC: 149.0 Df Model: 2  Covariance Type: nonrobust ==============================================================================  coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ Intercept 37.6181 1.618 23.257 0.000 34.299 40.937 wt -3.8709 0.634 -6.102 0.000 -5.173 -2.569 hp -0.0334 0.009 -3.684 0.001 -0.052 -0.015 ============================================================================== Omnibus: 4.325 Durbin-Watson: 2.598 Prob(Omnibus): 0.115 Jarque-Bera (JB): 3.132 Skew: 0.782 Prob(JB): 0.209 Kurtosis: 3.246 Cond. No. 588. ==============================================================================

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