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:

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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: 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. Suppose you have pandas dataframe that includes data columns final, the final exam score out of 40, and ACT, the ACT score for the student. You use statsmodels to create a results object the estimates the following module using OLS. final = Bo + B1ACT + E The results are in the table. What can we conclude? O ACT scores are negatively related to exam performance. O at a confidence level of 90%, 31 is statistically significant, but not at a confidence level of 95%. O ACT scores do not explain variation in final exam scores. O at a confidence level of 99%, 1 is statistically significant. This means variation in ACT scores explains some of the variation in final exam scores

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