Statsmodels Regression Linear Model Regressionresults Summary

Leo Migdal

-Dec 4, 2025, 7:13 AM

statsmodels regression linear model regressionresults summary

Name of endogenous (response) variable. The Default is y. Names for the exogenous variables. Default is var_## for ## in the number of regressors. Must match the number of parameters in the model. Title for the top table.

If not None, then this replaces the default title. The significance level for the confidence intervals. Flag indicating to produce reduced set or diagnostic information. Default is False. When building a regression model using Python’s statsmodels library, a key feature is the detailed summary table that is printed after fitting a model. This summary provides a comprehensive set of statistics that helps you assess the quality, significance, and reliability of your model.

In this article, we’ll walk through the major sections of a regression summary output in statsmodels and explain what each part means. Before you can get a summary, you need to fit a model. Here’s a basic example: Let’s now explore each section of the summary() output. The regression summary indicates that the model fits the data reasonably well, as evidenced by the R-squared and adjusted R-squared values. Significant predictors are identified by p-values less than 0.05.

The sign and magnitude of each coefficient indicate the direction and strength of the relationship. The F-statistic and its p-value confirm whether the overall model is statistically significant. If the key assumptions of linear regression are met, the model is suitable for inference and prediction. Communities for your favorite technologies. Explore all Collectives Stack Overflow for Teams is now called Stack Internal.

Bring the best of human thought and AI automation together at your work. Bring the best of human thought and AI automation together at your work. Learn more Find centralized, trusted content and collaborate around the technologies you use most. Bring the best of human thought and AI automation together at your work. Linear regression is a popular method for understanding how different factors (independent variables) affect an outcome (dependent variable.

The Ordinary Least Squares (OLS) method helps us find the best-fitting line that predicts the outcome based on the data we have. In this article we will break down the key parts of the OLS summary and how to interpret them in a way that's easy to understand. Many statistical software options, like MATLAB, Minitab, SPSS, and R, are available for regression analysis, this article focuses on using Python. The OLS summary report is a detailed output that provides various metrics and statistics to help evaluate the model's performance and interpret its results. Understanding each one can reveal valuable insights into your model's performance and accuracy. The summary table of the regression is given below for reference, providing detailed information on the model's performance, the significance of each variable, and other key statistics that help in interpreting the results.

Here are the key components of the OLS summary: Where, N = sample size(no. of observations) and K = number of variables + 1 (including the intercept). \text{Standard Error} = \sqrt{\frac{N - K}{\text{Residual Sum of Squares}}} \cdot \sqrt{\frac{1}{\sum{(X_i - \bar{X})^2}}} This formula provides a measure of how much the coefficient estimates vary from sample to sample. Daily Dose of Data Science Free Book | Deep Dives

Statsmodel provides one of the most comprehensive summaries for regression analysis. Yet, I have seen so many people struggling to interpret the critical model details mentioned in this report. Today, let me help you understand the entire summary support provided by statsmodel and why it is so important. The first column of the first section lists the model’s settings (or config). This part has nothing to do with the model’s performance. This class summarizes the fit of a linear regression model.

It handles the output of contrasts, estimates of covariance, etc. The covariance estimator used in the results. Additional keywords used in the covariance specification. Flag indicating to use the Student’s t in inference. smry – this holds the summary tables and text, which can be printed or converted to various output formats. © 2009–2012 Statsmodels Developers© 2006–2008 Scipy Developers© 2006 Jonathan E.

TaylorLicensed under the 3-clause BSD License. http://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.RegressionResults.summary.html This class summarizes the fit of a linear regression model. It handles the output of contrasts, estimates of covariance, etc. Aikake’s information criteria. For a model with a constant \(-2llf + 2(df_model + 1)\).

For a model without a constant \(-2llf + 2(df_model)\). Bayes’ information criteria For a model with a constant \(-2llf + \log(n)(df_model+1)\). For a model without a constant \(-2llf + \log(n)(df_model)\) The standard errors of the parameter estimates. Name of endogenous (response) variable. The Default is y.

Names for the exogenous variables. Default is var_## for ## in the number of regressors. Must match the number of parameters in the model. Title for the top table. If not None, then this replaces the default title. The significance level for the confidence intervals.

Flag indicating to produce reduced set or diagnostic information. Default is False. In this article, we will discuss how to use statsmodels using Linear Regression in Python. Linear regression analysis is a statistical technique for predicting the value of one variable(dependent variable) based on the value of another(independent variable). The dependent variable is the variable that we want to predict or forecast. In simple linear regression, there's one independent variable used to predict a single dependent variable.

In the case of multilinear regression, there's more than one independent variable. The independent variable is the one you're using to forecast the value of the other variable. The statsmodels.regression.linear_model.OLS method is used to perform linear regression. Linear equations are of the form: Syntax: statsmodels.regression.linear_model.OLS(endog, exog=None, missing='none', hasconst=None, **kwargs) Return: Ordinary least squares are returned.

Importing the required packages is the first step of modeling. The pandas, NumPy, and stats model packages are imported.

Statsmodels Regression Linear Model Regressionresults Summary

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