The ols() method in statsmodels is used to fit a simple linear regression model using "Exam4" as the response variable and "Exam3" 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 10% level of significance (alpha = 0.10)? Select one.

(Hint: Review results of F-statistic)

OLS Regression Results Dep. Variable: Exam4 R-squared: 0. 077 Model: OLS Adj. R-squared: 0. 058 Method: Least Squares F-statistic: 4. 010 Date: Fri, 16 Aug 2019 Prob (F-statistic) : 0. 0509 Time: 10: 29:37 Log-Likelihood: -172.76 No. Observations : 50 AIC: 349.5 Of Residuals: 48 BIC: 353. 3 of Model: Covariance Type: nonrobust coef std err t P>It| [O . 025 0.975] Intercept 68. 9586 3.925 17. 568 0. 000 61. 066 76. 851 Exam3 0. 1028 0. 051 2. 002 0.051 -0. 000 0. 206 Omnibus : 5.557 Durbin-Watson: 1. 644 Prob (Omni bus) : 0. 062 Jarque-Bera (JB) : 4. 422 Skew: 0. 659 Prob (JB) : 0. 110 Kurtosis : 3. 621 Cond. No. 271. Exam4 = 76.85 + 0.206 Exam3, model is not statistically significant Exam4 = 76.85 + 0.206 Exam3, model is statistically significant Exam4 = 68.9576 + 0.1028 Exam3, model is not statistically significant Exam4 = 68.9576 + 0.1028 Exam3, model is statistically significant