Python Functions Questions 4&5 assistance please

Question 4 (3 points) Listen 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 P>It [0. 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 (Omnibus) : 0.062 Jarque-Bera (JB) : 4.422 Skew: 0.659 Prob (JB) : 0. 110 Kurtosis: 3.621 Cond. 271. Exam4 = 76.85 + 0.206 Exam3, model is statistically significant Exam4 = 68.9576 + 0.1028 Exam3, model is statistically significant Exam4 = 68.9576 + 0.1028 Exam3, model is not statistically significant Exam4 = 76.85 + 0.206 Exam3, model is not statistically significant Question 5 (3 points) Listen Which of the following Python methods return the correlation coefficient? Select all that apply. pearsonr method from scipy.stats submodule corr method from pandas dataframe