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

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
Problem 1PE
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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
Transcribed Image Text: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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