Last week's discussion involved development of a multiple regression model that used miles per gallon as a response variable. Weight and horsepower were predictor variables. You performed an overall F-test to evaluate the significance of your model. This week, you will evaluate the significance of individual predictors. You will use output of Python script from Module Six to perform individual t-tests for each predictor variable. Specifically, you will look at Step 5 of the Python script to answer all questions in the discussion this week.

In your initial post, address the following items:

1. 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,, for weight in Python output. Recall that this is the individual t-test for the beta parameter.) See Step 5 in the Python script.
2. 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,, 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. Last week, you performed an overall F-test. Why do you now need to perform individual t-tests? What are the differences in the interpretation of the two tests?
4. What is the coefficient of determination of your multiple regression model from Module Six? Provide appropriate interpretation of this statistic.

Below is step 5. There are no missing information. The python script is from step 5. This is the information needed to solve the questions thank you

This is step 5 OLS Regression Results ============================================================================== Dep. Variable: mpg R-squared: 0.822 Model: OLS Adj. R-squared: 0.808 Method: Least Squares F-statistic: 62.13 Date: Fri, 14 Feb 2020 Prob (F-statistic): 7.88e-11 Time: 05:00:39 Log-Likelihood: -69.730 No. Observations: 30 AIC: 145.5 Df Residuals: 27 BIC: 149.7 Df Model: 2 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ Intercept 37.8867 1.748 21.674 0.000 34.300 41.473 wt -4.0629 0.694 -5.855 0.000 -5.487 -2.639 hp -0.0318 0.009 -3.470 0.002 -0.051 -0.013 ============================================================================== Omnibus: 5.277 Durbin-Watson: 1.919 Prob(Omnibus): 0.071 Jarque-Bera (JB): 3.980 Skew: 0.878 Prob(JB): 0.137 Kurtosis: 3.314 Cond. No. 620. ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. 

Any help would be appreciated thank you