Math Problem Statement
OLS Regression Results
Dep. Variable: total_wins R-squared: 0.823
Model: OLS Adj. R-squared: 0.823
Method: Least Squares F-statistic: 2865.
Date: Thu, 12 Dec 2024 Prob (F-statistic): 8.06e-234
Time: 17:16:20 Log-Likelihood: -1930.3
No. Observations: 618 AIC: 3865.
Df Residuals: 616 BIC: 3873.
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
Intercept -128.2475 3.149 -40.731 0.000 -134.431 -122.064 avg_elo_n 0.1121 0.002 53.523 0.000 0.108 0.116
Omnibus: 152.822 Durbin-Watson: 1.098 Prob(Omnibus): 0.000 Jarque-Bera (JB): 393.223 Skew: -1.247 Prob(JB): 4.10e-86 Kurtosis: 6.009 Cond. No. 2.14e+04
Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 2.14e+04. This might indicate that there are strong multicollinearity or other numerical problems.
Solution
Ask a new question for Free
By Image
Drop file here or Click Here to upload
Math Problem Analysis
Mathematical Concepts
Regression Analysis
Ordinary Least Squares (OLS)
Statistical Significance
Multicollinearity
Formulas
Linear Regression Formula: Y = β0 + β1X + ε
R-squared = 1 - (SS_res / SS_tot)
t-statistic = (Coefficient - Hypothesized Value) / Standard Error
Theorems
Gauss-Markov Theorem
Central Limit Theorem (assumption for normality of residuals)
Suitable Grade Level
Undergraduate Statistics/Graduate Level
Related Recommendation
Regression Analysis: Interpreting Slope, R-Squared, and P-Value
Analyzing Regression Results: Statistical Significance and Model Fit
Understanding Regression Output: Regression Statistics, ANOVA, and Coefficients
Analysis of OLS Regression Results: Understanding the Impact of Variables on days_all_x_label
Choosing the Best Regression Model: Understanding R-squared and Correlation Coefficients