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.

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)

Cars data frame (showing only the first five observations) Unnamed: 0 mpg cyl disp hp drat wt qsec vs am gear carb 30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 5 8 10 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 0 4 4 11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3OLS Regression Results === Dep. Variable: mpg R - squared : 0.736 Model : OLS Adj. R-squared : 0.726 Method : Least Squares F-statistic: 77.94 Date : Thu, 29 Sep 2022 Prob (F-statistic) : 1. 40e-09 Time : 18:37:15 Log-Likelihood : -75.445 No. Observations : 30 AIC : 154.9 Of Residuals: 28 BIC: 157.7 Df Model : 1 Covariance Type: nonrobust = = coef std err t P> t [0. 025 0.975] Intercept 36.9758 2.029 18.227 0.000 32.820 41 . 131 wt -5. 2925 0. 599 -8.828 0.000 -6.520 -4.064 Omnibus : 4. 140 Durbin-Watson : 1.885 Prob (Omnibus ) : 0. 126 Jarque-Bera (JB) : 3.222 Skew : 0. 802 Prob ( JB) : 0. 200 Kurtosis : 3.041 Cond. No. 13.1 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified