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OLS Regression Results Dep. Variable: final R-squared : 0. 134 Model: OLS Adj. R-squared: 0. 133 Method: Least Squares F-statistic: 104.8 Date: Sun, 16 Jun 2024 Prob (F-statistic): 5. 67e-23 Time : 12 : 46:26 Log-Likelihood: -1969.3 No. Observations: 680 AIC: 3943. Of Residuals: 678 BIC: 3952. Df Model: 1 Covariance Type: nonrobust coef std err t PIt| [0. 025 0. 975] Intercept 17. 7067 0. 817 21. 675 0. 000 16. 103 19. 311 priGPA 3. 1640 0. 309 10.238 0. 000 2. 557 3.771 Omnibus : 0. 075 Durbin-Watson: 2.240 Prob (Omnibus ) : 0. 963 Jarque-Bera (JB) : 0. 045 Skew: -0. 020 Prob ( JB) : 0. 978 Kurtosis: 3. 006 Cond. No. 14.6 Suppose you have pandas dataframe that includes data columns final, the final exam score out of 40, and priGPA, the prior term GPA for the student. You use statsmodels to create a results object the estimates the following module using OLS. final = Bo + BipriGPA +e The results are in the table. What can we conclude? O With 95% confidence, the value for B1 is between 2.557 and 3.771. O With 95% confidence, the value for 31 is between 2.557 and 16.103. With 95% confidence, the value for B1 is equal to 3.164 O With 95% confidence, the value for 1 is between 16.103 and 19.311