Can you help with this Statistics problem?

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 (below) from Module Six to perform individual t-tests for each predictor variable.

1. 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?
2. What is the coefficient of determination of your multiple regression model? Provide appropriate interpretation of this statistic.

Cars data frame (showing only the first five observations) Unnamed: 0 mpg cyl disp hp drat wt qsec vs am gear cart 30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 8 18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 2 15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 O 3 A 16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 3 4 13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 MPG against Weight 35 100 MPG 15 10 . . 15 20 25 3.0 3.5 4.0 4.5 5.0 5.5 Weight (1000s lbs) MPG against Horsepower MPG 20 . .. 15 10 50 100 150 200 250 300 Horsepower mpg wt hp mpg 1.000000 -0.869022 -0.778709 wt -0. 869022 1.030000 0.655075 hp -0. 778709 0.655075 1.000000 OLS Regression Results Dep. Variable: mpg R-squared: 8.832 Model: OLS Adj. R-squared: 0.820 Method : Least Squares F-statistic: 66.87 Date : Tue, 04 Oct 2022 Prob (F-statistic): 3.47e-11 Time : 05:29:14 Log-Likelihood: -70.077 No. Observations: 30 AIC: 146.2 Of Residuals: 27 BIC: 150.4 Of Model: Covariance Type: nonrobust roef std err P>t [0.025 0.975] Intercept 37.5745 1.663 22.596 0.0ee 34.162 40.986 wt -3.9162 0. 650 6.023 0.000 -5.250 2.582 hp -0.0325 0.009 -3.514 0.002 -0.052 -0.014 Omnibus : 3.946 Durbin-Watson: 1.927 Prob (Omnibus ) : 0. 139 Jarque-Bera (JB) : 2.898 Skew: 0.758 Prob(JB) : 0.235 Kurtosis : 3.146 Cond. No. 592. Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified