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) Dep. Variable: Model: Method: Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: Intercept Exam1 Omnibus: Prob (Omnibus): Skew: Kurtosis: OLS Regression Results R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): coef 57.7627 0.2266 Exam4 OLS Least Squares Fri, 16 Aug 2019 10:23:53 50 48 1 nonrobust std err 10.052 0.121 3.859 0.145 0.428 3.784 Log-Likelihood: AIČ: BIC: t 5.746 1.876 P>|t| 0.000 0.067 Durbin-Watson: Jarque-Bera (JB): Prob (JB): Cond. No. [0.025 37.552 -0.016 Exam4 = 77.973 +0.469 Exam1, model is not statistically significant Exam4 = 57.7627 +0.2266 Exam1, model is statistically significant Exam4 = 77.973 +0.469 Exam1, model is statistically significant Exam4 = 57.7627+0.2266 Exam1, model is not statistically significant 0.068 0.049 3.518 0.0668 -173.00 350.0 353.8 0.975] 77.973 0.469 1.723 2.809 0.245 753.
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) Dep. Variable: Model: Method: Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: Intercept Exam1 Omnibus: Prob (Omnibus): Skew: Kurtosis: OLS Regression Results R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): coef 57.7627 0.2266 Exam4 OLS Least Squares Fri, 16 Aug 2019 10:23:53 50 48 1 nonrobust std err 10.052 0.121 3.859 0.145 0.428 3.784 Log-Likelihood: AIČ: BIC: t 5.746 1.876 P>|t| 0.000 0.067 Durbin-Watson: Jarque-Bera (JB): Prob (JB): Cond. No. [0.025 37.552 -0.016 Exam4 = 77.973 +0.469 Exam1, model is not statistically significant Exam4 = 57.7627 +0.2266 Exam1, model is statistically significant Exam4 = 77.973 +0.469 Exam1, model is statistically significant Exam4 = 57.7627+0.2266 Exam1, model is not statistically significant 0.068 0.049 3.518 0.0668 -173.00 350.0 353.8 0.975] 77.973 0.469 1.723 2.809 0.245 753.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
100%
![3
WEM
tion
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)
Dep. Variable:
Model:
Method:
Date:
Time:
No. Observations:
Df Residuals:
Df Model:
Covariance Type:
Intercept
Exami
Omnibus:
Prob (Omnibus):
skew:
Kurtosis:
OLS Regression Results
R-squared:
Exam4
OLS
Adj. R-squared:
F-statistic:
Prob (F-statistic):
Log-Likelihood:
Least Squares
Fri, 16 Aug 2019
10:23:53
coef
57.7627
0.2266
50
48
nonrobust
std err
10.052
0.121
3.859
0.145
0.428
3.784
AIČ:
BIC:
t
5.746
1.876
P>|t|
0.000
0.067
Durbin-Watson:
Jarque-Bera (JB):
Prob (JB):
Cond. No.
[0.025
37.552
-0.016
Exam4 = 77.973 +0.469 Exam1, model is not statistically significant
Exam4 = 57.7627 +0.2266 Exam1, model is statistically significant
Exam4 = 77.973 +0.469 Exam1, model is statistically significant
Exam4 = 57.7627 +0.2266 Exam1, model is not statistically significant
0.068
0.049
3.518
0.0668
-173.00
350.0
353.8
0.975]
77.973
0.469
1.723
2.809
0.245
753.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F9d62d096-051c-4718-a327-e350c164712e%2F836ca6d3-cc84-465f-9073-1d14b5448ec2%2Fo4pi793_processed.jpeg&w=3840&q=75)
Transcribed Image Text:3
WEM
tion
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)
Dep. Variable:
Model:
Method:
Date:
Time:
No. Observations:
Df Residuals:
Df Model:
Covariance Type:
Intercept
Exami
Omnibus:
Prob (Omnibus):
skew:
Kurtosis:
OLS Regression Results
R-squared:
Exam4
OLS
Adj. R-squared:
F-statistic:
Prob (F-statistic):
Log-Likelihood:
Least Squares
Fri, 16 Aug 2019
10:23:53
coef
57.7627
0.2266
50
48
nonrobust
std err
10.052
0.121
3.859
0.145
0.428
3.784
AIČ:
BIC:
t
5.746
1.876
P>|t|
0.000
0.067
Durbin-Watson:
Jarque-Bera (JB):
Prob (JB):
Cond. No.
[0.025
37.552
-0.016
Exam4 = 77.973 +0.469 Exam1, model is not statistically significant
Exam4 = 57.7627 +0.2266 Exam1, model is statistically significant
Exam4 = 77.973 +0.469 Exam1, model is statistically significant
Exam4 = 57.7627 +0.2266 Exam1, model is not statistically significant
0.068
0.049
3.518
0.0668
-173.00
350.0
353.8
0.975]
77.973
0.469
1.723
2.809
0.245
753.
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