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-statistics) Exam4 = 68.9576 + 0.1028 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 = 76.85 + 0.206 Exam3, model is statistically significant
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
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-statistics)
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Exam4 = 68.9576 + 0.1028 Exam3, model is statistically significant |
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Exam4 = 76.85 + 0.206 Exam3, model is not statistically significant |
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Exam4 = 68.9576 + 0.1028 Exam3, model is not statistically significant |
|
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Exam4 = 76.85 + 0.206 Exam3, model is statistically significant |
![OLS Regression Results
Dep. Variable:
Model:
Method:
R-squared:
Adj. R-squared:
F-statistic:
Prob (F-statistic):
Log-Likelihood:
Exam4
0.077
0.058
4.010
0.0509
-172.76
349.5
353.3
OLS
Least Squares
Fri, 16 Aug 2019
10:29:37
Date:
Time:
No. Observations:
Df Residuals:
Df Model:
Covariance Type:
50
48
1
nonrobust
AIC:
BIC:
coef
std err
P>[t|
[0.025
0.975]
t
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
3.621
Durbin-Watson:
Jarque-Bera (JB):
Prob(JB):
Cond. No.
1.644
4.422
0.110
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