I have included screenshots of the five questions I need to answer as well as the data (step 5 in python) it is referencing.

1. Is at least one of the two variables (weight and horsepower) significant in the model? Run the overall Ftest and provide your interpretation at 5% level of significance. See Step 5 in the Python script. Include the following in your analysis: 3. Define the null and alternative hypothesis in mathematical terms and in words. b. Report the level of significance. c. Include the test statistic and the P-value. (Hint: FStatistic and Prob (F-Statistic) in the output). d. Provide your conclusion and interpretation of the test. Should the null hypothesis be rejected? Why or why not? 2. What is the slope coefficient for the weight variable? Is this coefficient significant at 5% level of significance (alpha=0.05)? (Hint: Check the P- value, I'>l'|, for weight in Python output. Recall that this is the individual t test for the beta parameter.) See Step 5 in the Python script. 3. What is the slope coefficient for the horsepower variable? Is this coefficient significant at 5% level of significance (alpha=0.05)? (Hint: Check the P-value, P>ltl, for horsepower in Python output. Recall that this is the individual t-test for the beta parameter.) See Step 5 in the Python script. 4. What is the purpose of performing individual t-tests after carrying out the overall F-test? What are the differences in the interpretation of the two tests? 5. What is the coefficient of determination of your multiple regression model from Module Six? Provide appropriate interpretation of this statistic. from statsmodels . formula . api import ols # create the multiple regression model with mpg as the response variable; weight and horsepower as predictor variables model = ols ( 'mpg ~ wtthp', data=cars_df) . fit( ) print (model . summary () ) OLS Regression Results Dep. Variable: mpg R - squared : 0. 792 Model: OLS Adj. R-squared: 0.777 Method : Least Squares F-statistic: 51. 40 Date: Tue, 15 Jun 2021 Prob (F-statistic) : 6. 22e-10 Time: 15: 56:02 Log-Likelihood : -70.457 No. Observations : 30 AIC : 146.9 Df Residuals: 27 BIC: 151. 1 Df Model : 2 Covariance Type: nonrobust coef std err t P> | t] [0. 025 0.975] Intercept 37 . 2458 1. 876 19.851 0.000 33. 396 41. 096 wt - 3. 9414 0. 724 -5 . 441 0.000 -5.428 -2.455 hp -0. 0310 0. 009 - 3. 299 0. 003 -0. 050 -0. 012 Omnibus : 5.729 Durbin-Watson: 1.727 Prob (Omnibus ) : 0. 057 Jarque -Bera (JB) : 4.443 Skew: 0. 930 Prob ( JB) : 0. 108 Kurtosis : 3. 314 Cond. No. 651. Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified