I need to answer the follow questions

1. 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.
2. 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.
3. Using the below data

In [5] : from statsmodels . formula. api import ols # create the simple Linear regression model with mpg as the response variable and weight as the predictor variable model = ols('mpg ~ wt', data=cars_df) . fit() #print the model summary print (model . summary () ) OLS Regression Results ====== Dep. Variable: mpg R- squared: 0.765 Model : OLS Adj. R-squared : 0. 757 Method : Least Squares F-statistic: 91. 21 Date: Mon, 30 May 2022 Prob (F-statistic): 2. 63e-10 Time : 16:07:18 Log-Likelihood : -71.953 No. Observations : 30 AIC : 147.9 Of Residuals: 28 BIC : 150.7 Of Model: Covariance Type: nonrobust coef std err t P> | t [0. 025 0.975] Intercept 35 . 8143 1. 782 20.096 3.000 32.164 39.465 wt -5. 0020 0. 524 -9.550 0.090 -6.075 -3.929 Omnibus : 4.780 Durbin-Watson: 2.000 Prob (Omnibus ) : 0. 092 Jarque-Bera (JB) : 3.207 Skew : 0. 741 Prob (JB ) : 0.201 Kurtosis: 3.607 Cond. No. 13.0 === Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified