The ols() method in statsmodels was used to fit a simple linear regression modelusing "Exam4" as the response variable and "Exam1" as the predictor variable. Theoutput is shown below. A text version is available. What is the correct regressionequation based on this output? Is this model statistically significant at 5% level ofsignificance (alpha= 0.05)? Select one.(Hint: Review results of F-statistic)======Dep. Variable:Model:Method:Date:Time:No. Observations:Df Residuals:Df Model:Covariance Type:InterceptExam1Omnibus:Prob (Omnibus):Skew:Kurtosis:0000coef57.76270.2266OLS Regression ResultsExam4OLSR-squared:Adj. R-squared:F-statistic:Prob (F-statistic):Least SquaresFri, 16 Aug 201910:23:535048std err10.0520.1211nonrobust3.8590.1450.4283.784Log-Likelihood:AIC:BIC:t5.7461.876P>|t|0.0000.067Durbin-Watson:Jarque-Bera (JB):Prob (JB):Cond. No.[0.02537.552-0.016Exam4 = 57.7627 +0.2266 Exam1, model is not statistically significantExam4 = 57.7627+0.2266 Exam1, model is statistically significantExam4 = 77.973 +0.469 Exam1, model is statistically significantExam4 = 77.973 +0.459 Exam1, model is not statistically significant0.0680.0493.5180.0668-173.00350.0353.80.975]77.9730.4691.7232.8090.245753.

Given that Y Intercept = 57.7627 Slope of exam1 = 0.2266

Answered: Variable and "Exam1" as the predictor…

MATLAB: An Introduction with Applications

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Author:Amos Gilat

Publisher:Amos Gilat

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