Question: solve OLS Regression Results Dep. Variable: final R-squared: 0. 131 Model: OLS Adj. R-squared: 0. 129 Method : Least Squares F-statistic: 101.8 Date: Sun, 16

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OLS Regression Results Dep. Variable: final R-squared: 0. 131 Model: OLS Adj. R-squared: 0. 129 Method : Least Squares F-statistic: 101.8 Date: Sun, 16 Jun 2024 Prob (F-statistic) : 2. 17e-22 Time : 12 : 46: 26 Log-Likelihood: -1970.6 No. Observations : 680 AIC: 3945. Df Residuals : 678 BIC: 3954. Df Model: 1 Covariance Type: nonrobust coef std err t P |t| [0. 025 0. 975] Intercept 14. 9195 1. 101 13.556 0. 000 12. 758 17. 081 ACT 0. 4874 0. 048 10. 088 0. 000 0. 393 0. 582 Omnibus : 1.040 Durbin-Watson: 2. 226 Prob (Omnibus ) : 0.594 Jarque-Bera (JB) : 1. 131 Skew: -0. 074 Prob ( JB) : 0. 568 Kurtosis : 2. 866 Cond. No. 149. uppose you have pandas dataframe that includes data columns final, the final exam score ut of 40, and ACT, the ACT score for the student. You use statsmodels to create a results bject the estimates the following module using OLS. final = Bo + B1ACT + E he results are in the table. uppose you want to test the hypothesis that B1

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