OLS Regression Results Dep. Variable: Model: R-squared: Adj. R-squared: F-statistic: total_wins 0.837 OLS 0.837 Method: Least Squares Sat, 27 Feb 2021 1580. Prob (F-statistic): Log-Likelihood: Date: 4.41e-243 Time: 18:17:53 -1904.6 No. Observations: 618 AIC: 3815. Df Residuals: 615 BIC: 3829. Df Model: Covariance Type: 2 nonrobust coef std err t P>|t| [0.025 0.975] Intercept avg_pts avg_elo_n -152.5736 4.500 -33.903 0.000 -161.411 -143.736 0.3497 0.048 7.297 0.000 0.256 0.444 0.1055 Θ.002 47.952 0.000 0.101 0.110 Omnibus: 89.087 Durbin-Watson: 1.203 Prob(Omnibus): Skew: Jarque-Bera (JB): Prob(JB): Cond. No. θ.000 160.540 -0.869 1.38e-35 Kurtosis: 4.793 3.19e+04
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.
How do I determined the coefficient of determination from this:
![OLS Regression Results
Dep. Variable:
Model:
total_wins
R-squared:
Adj. R-squared:
0.837
OLS
0.837
Least Squares
Sat, 27 Feb 2021
Method:
F-statistic:
1580.
Prob (F-statistic):
Log-Likelihood:
AIC:
Date:
4.41e-243
Time:
18:17:53
-1904.6
No. Observations:
618
3815.
Df Residuals:
615
BIC:
3829.
Df Model:
2
Covariance Type:
nonrobust
coef
std err
P>|t|
[0.025
0.975]
Intercept
avg_pts
avg_elo_n
-152.5736
4.500
-33.903
0.000
-161.411
-143.736
0.3497
0.048
7.297
Θ. 000
0.256
0.444
0.1055
Θ. 002
47.952
0.000
0.101
0.110
Omnibus:
89.087
Durbin-Watson:
1.203
Prob(Omnibus):
Θ. 000
Jarque-Bera (JB):
Prob(JB):
Cond. No.
160.540
Skew:
-0.869
1.38e-35
Kurtosis:
4.793
3.19e+04](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa04351ea-a0da-4310-b4a2-31804867261c%2F126a64b9-c3da-4e60-b57b-867023d95e23%2F7d7g0l4_processed.png&w=3840&q=75)
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