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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: Model: Method: R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): Log-Likelihood: 50 Exam4 0.068 0.049 3.518 0.0668 -173.00 350.0 353.8 OLS Least Squares Fri, 16 Aug 2019 10:23:53 Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: AIČ: 48 BIC: 1 nonrobust coef std err t P>|t| [0.025 0.975] Intercept Examl 57.7627 0.2266 10.052 0.121 5.746 1.876 0.000 0.067 37.552 -0.016 77.973 0.469 Omnibus: Prob(Omnibus): skew: Kurtosis: Durbin-Watson: 3.859 0.145 0.428 3.784 Cond. No. Jarque-Bera (JB): Prob(JB): 1.723 2.809 0.245 753. Exam4 = 77.973 + 0.469 Exam1, model is not statistically significant Exam4 = 77.973 + 0.469 Exam1, model is statistically significant Exam4 = 57.7627 + 0.2266 Exam1, model is not statistically significant Exam4 = 57.7627 + 0.2266 Exam1, model is statistically significant

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: Model: Method: R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): Log-Likelihood: 50 Exam4 0.068 0.049 3.518 0.0668 -173.00 350.0 353.8 OLS Least Squares Fri, 16 Aug 2019 10:23:53 Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: AIČ: 48 BIC: 1 nonrobust coef std err t P>|t| [0.025 0.975] Intercept Examl 57.7627 0.2266 10.052 0.121 5.746 1.876 0.000 0.067 37.552 -0.016 77.973 0.469 Omnibus: Prob(Omnibus): skew: Kurtosis: Durbin-Watson: 3.859 0.145 0.428 3.784 Cond. No. Jarque-Bera (JB): Prob(JB): 1.723 2.809 0.245 753. Exam4 = 77.973 + 0.469 Exam1, model is not statistically significant Exam4 = 77.973 + 0.469 Exam1, model is statistically significant Exam4 = 57.7627 + 0.2266 Exam1, model is not statistically significant Exam4 = 57.7627 + 0.2266 Exam1, model is statistically significant

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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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: Model: Method: R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): Log-Likelihood: 50 48 1 Exam4 0.068 0.049 3.518 0.0668 -173.00 350.0 353.8 OLS Least Squares Fri, 16 Aug 2019 10:23:53 Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: AIC: ВIC: nonrobust coef std err t P>|t| [0.025 0.975] Intercept Examl 57.7627 0.2266 10.052 0.121 5.746 1.876 0.000 0.067 37.552 -0.016 77.973 0.469 Omnibus: Prob(Omnibus): skew: Kurtosis: 3.859 0.145 0.428 3.784 Durbin-Watson: Jarque-Bera (JB): Prob(JB): Cond. No. 1.723 2.809 0.245 753. Exam4 = 77.973 + 0.469 Exam1, model is not statistically significant Exam4 = 77.973 + 0.469 Exam1, model is statistically significant Exam4 = 57.7627 + 0.2266 Exam1, model is not statistically significant Exam4 = 57.7627 + 0.2266 Exam1, model is statistically significant
Transcribed Image Text: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: Model: Method: R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): Log-Likelihood: 50 48 1 Exam4 0.068 0.049 3.518 0.0668 -173.00 350.0 353.8 OLS Least Squares Fri, 16 Aug 2019 10:23:53 Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: AIC: ВIC: nonrobust coef std err t P>|t| [0.025 0.975] Intercept Examl 57.7627 0.2266 10.052 0.121 5.746 1.876 0.000 0.067 37.552 -0.016 77.973 0.469 Omnibus: Prob(Omnibus): skew: Kurtosis: 3.859 0.145 0.428 3.784 Durbin-Watson: Jarque-Bera (JB): Prob(JB): Cond. No. 1.723 2.809 0.245 753. Exam4 = 77.973 + 0.469 Exam1, model is not statistically significant Exam4 = 77.973 + 0.469 Exam1, model is statistically significant Exam4 = 57.7627 + 0.2266 Exam1, model is not statistically significant Exam4 = 57.7627 + 0.2266 Exam1, model is statistically significant
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