Question: The ols() method in statsmodels was used to fit a simple linear regression model using Exam4 as the response variable and Exam1 as the predictor

The ols() method in statsmodels was used to fit a simple linear regression model using "Exam4" as the response variable and "Exam1" as the predictor variable. The output is shown below. A text version is available. What is the correct regression equation based on this output? Is this model statistically significant at 5% level of significance (alpha = 0.05)? Select one.

(Hint: Review results of F-statistic)

OLS Regression Results Dep. Variable: Exam4 R-squared: 0. 068 Mode 1: OLS Adj. R-squared: 0. 049 Method: Least Squares F-statistic: 3. 518 Date: Fri, 16 Aug 2019 Prob (F-statistic) : 0. 0668 Time: 10:23:53 Log-Likelihood: -173.00 No. Observations : 50 AIC: 350.0 Df Residuals: 48 BIC: 353.8 Df Model: Covariance Type: nonrobust coef std err t P>It| [0 . 025 0. 975] Intercept 57.7627 10.052 5.746 0. 000 37. 552 77.973 Exam1 0. 2266 0. 121 1. 876 0. 067 -0. 016 0. 469 Omnibus : 3.859 Durbin-Watson: 1.723 Prob (Omnibus) : 0. 145 Jarque-Bera (JB) : 2. 809 Skew: 0. 428 Prob (JB) : 0. 245 Kurtosis : 3.784 Cond. No. 753. O Exam4 = 77.973 + 0.469 Exam1, model is not statistically significant O Exam4 = 57.7627 + 0.2266 Exam1, model is not statistically significant O Exam4 = 57.7627 + 0.2266 Exam1, model is statistically significant O Exam4 = 77.973 + 0.469 Exam1, model is statistically significant

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