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 ResultsDep. Variable:Model:Method:Date:Time:No. Observations:Df Residuals:Df Model:Covariance Type:Exam4OLSR-squared:Adj. R-squared:F-statistic:Prob (F-statistic):Log-Likelihood:500.0770.0584.0100.0509-172.76349.5353.3Least SquaresFri, 16 Aug 201910:29:37AIC:481BIC:nonrobustcoefstd errtP>[t|[0.0250.975]InterceptExam368.95860.10283.9250.05117.5682.0020.0000.05161.066-0.00076.8510.206Omnibus:Prob(Omnibus):Skew:Kurtosis:5.5570.0620.659 Prob(JB):3.621 Cond. No.Durbin-Watson:Jarque-Bera (JB):1.6444.4220.110271.Exam4 = 76.85 + 0.206 Exam3, model is statistically significantExam4 = 76.85 + 0.206 Exam3, model is not statistically significantExam4 = 68.9576 + 0.1028 Exam3, model is not statistically significantExam4 = 68.9576 + 0.1028 Exam3, model is statistically significant

Answered: OLS Regression Results Dep. Variable:…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

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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:
Model:
Method:
Date:
Time:
No. Observations:
Df Residuals:
Df Model:
Covariance Type:
Exam4
OLS
R-squared:
Adj. R-squared:
F-statistic:
Prob (F-statistic):
Log-Likelihood:
50
0.077
0.058
4.010
0.0509
-172.76
349.5
353.3
Least Squares
Fri, 16 Aug 2019
10:29:37
AIC:
48
1
BIC:
nonrobust
coef
std err
t
P>[t|
[0.025
0.975]
Intercept
Exam3
68.9586
0.1028
3.925
0.051
17.568
2.002
0.000
0.051
61.066
-0.000
76.851
0.206
Omnibus:
Prob(Omnibus):
Skew:
Kurtosis:
5.557
0.062
0.659 Prob(JB):
3.621 Cond. No.
Durbin-Watson:
Jarque-Bera (JB):
1.644
4.422
0.110
271.
Exam4 = 76.85 + 0.206 Exam3, model is statistically significant
Exam4 = 76.85 + 0.206 Exam3, model is not statistically significant
Exam4 = 68.9576 + 0.1028 Exam3, model is not statistically significant
Exam4 = 68.9576 + 0.1028 Exam3, model is statistically significant

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