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) Dep. Variable: Model: Method: Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: Intercept Exam3 Omnibus: Prob (Omnibus): Skew: Kurtosis: E OLS Regression Results R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): Log-Likelihood: coef 68.9586 0.1028 Exam4 OLS Least Squares Fri, 16 Aug 2019 10:29:37 50 48 1 nonrobust std err 3.925 0.051 5.557 0.062 0.659 3.621 AIC: BIC: t 17.568 2.002 P>|t| 0.000 0.051 Durbin-Watson: Jarque-Bera (JB): Prob(JB): Cond. No. [0.025 61.066 -0.000 0.077 0.058 4.010 0.0509 -172.76 349.5 353.3 0.975] 76.851 0.206 1.644 4.422 0.110 271. Exam4 = 68.9576 + 0.1028 Exam3, model is not statistically significant Exam4 = 68.9576 + 0.1028 Exam3, model is statistically significant Exam4 76.85 +0.206 Exam3, model is not statistically significant Exam4 76.85 +0.206 Exam3, model is statistically significant
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) Dep. Variable: Model: Method: Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: Intercept Exam3 Omnibus: Prob (Omnibus): Skew: Kurtosis: E OLS Regression Results R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): Log-Likelihood: coef 68.9586 0.1028 Exam4 OLS Least Squares Fri, 16 Aug 2019 10:29:37 50 48 1 nonrobust std err 3.925 0.051 5.557 0.062 0.659 3.621 AIC: BIC: t 17.568 2.002 P>|t| 0.000 0.051 Durbin-Watson: Jarque-Bera (JB): Prob(JB): Cond. No. [0.025 61.066 -0.000 0.077 0.058 4.010 0.0509 -172.76 349.5 353.3 0.975] 76.851 0.206 1.644 4.422 0.110 271. Exam4 = 68.9576 + 0.1028 Exam3, model is not statistically significant Exam4 = 68.9576 + 0.1028 Exam3, model is statistically significant Exam4 76.85 +0.206 Exam3, model is not statistically significant Exam4 76.85 +0.206 Exam3, model is statistically significant
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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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)
Dep. Variable:
Model:
Method:
Date:
Time:
No. Observations:
Df Residuals:
Df Model:
Covariance Type:
Intercept
Exam3
coef
68.9586
0.1028
Omnibus:
Prob (Omnibus):
Skew:
Kurtosis:
OLS Regression Results
Exam4
OLS
R-squared:
Adj. R-squared:
F-statistic:
Prob (F-statistic):
Least Squares
Fri, 16 Aug 2019
10:29:37
50
48
1
nonrobust
std err
3.925
0.051
Log-Likelihood:
5.557
0.062
0.659
3.621
AIC:
BIC:
t
17.568
2.002
P> |t|
0.000
0.051
Durbin-Watson:
Jarque-Bera (JB):
Prob (JB):
Cond. No.
[0.025
61.066
-0.000
0.077
0.058
4.010
0.0509
-172.76
349.5
353.3
0.975]
76.851
0.206
1.644
4.422
0.110
271.
Exam4 = 68.9576 +0.1028 Exam3, model is not statistically significant
Exam4 = 68.9576 + 0.1028 Exam3, model is statistically significant
Exam4 76.85 +0.206 Exam3, model is not statistically significant
Exam4 76.85 +0.206 Exam3, model is statistically significant](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Feaeff7c2-58eb-4b4a-8eb8-831bf9d0d68f%2F770f8ddb-0708-465b-971e-ed55aca310e3%2Fnb9gpha_processed.jpeg&w=3840&q=75)
Transcribed Image Text: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)
Dep. Variable:
Model:
Method:
Date:
Time:
No. Observations:
Df Residuals:
Df Model:
Covariance Type:
Intercept
Exam3
coef
68.9586
0.1028
Omnibus:
Prob (Omnibus):
Skew:
Kurtosis:
OLS Regression Results
Exam4
OLS
R-squared:
Adj. R-squared:
F-statistic:
Prob (F-statistic):
Least Squares
Fri, 16 Aug 2019
10:29:37
50
48
1
nonrobust
std err
3.925
0.051
Log-Likelihood:
5.557
0.062
0.659
3.621
AIC:
BIC:
t
17.568
2.002
P> |t|
0.000
0.051
Durbin-Watson:
Jarque-Bera (JB):
Prob (JB):
Cond. No.
[0.025
61.066
-0.000
0.077
0.058
4.010
0.0509
-172.76
349.5
353.3
0.975]
76.851
0.206
1.644
4.422
0.110
271.
Exam4 = 68.9576 +0.1028 Exam3, model is not statistically significant
Exam4 = 68.9576 + 0.1028 Exam3, model is statistically significant
Exam4 76.85 +0.206 Exam3, model is not statistically significant
Exam4 76.85 +0.206 Exam3, model is statistically significant
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