Results And Summary Tables Statsmodels Statsmodels Deepwiki

Leo Migdal

-Dec 4, 2025, 9:49 AM

results and summary tables statsmodels statsmodels deepwiki

This page documents the components and functionality of the Results and Summary Tables system in statsmodels, which provides tools for formatting, displaying, and exporting model results in various formats. For information about creating data structures and specifying models, see Data Handling and Model Formula Interface. The results and summary system enables users to generate publication-quality output from statistical model results in text, HTML, LaTeX, and CSV formats, facilitating model interpretation across different environments (console, Jupyter notebooks, academic papers). The key components of the results and summary tables system are: Sources: statsmodels/iolib/summary2.py16-249 statsmodels/iolib/table.py124-485 statsmodels/iolib/table.py499-632 statsmodels/iolib/table.py634-759 The Summary class is the primary interface for creating formatted model result summaries.

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. Communities for your favorite technologies. Explore all Collectives

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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. In the realm of data science and machine learning, understanding statistical results is crucial for making informed decisions.

One of the most commonly used packages data scientist encountered daily is statsmodel. Its summary table is a great tool to gain insights to understanding the relationship between explanatory variable and response variable. In this blog post, we’ll dive into how to interpret a Statsmodels summary table and extract meaningful insights from it. When you fit a statistical model using Statsmodels, such as linear regression, logistic regression, or any other supported model, you typically receive a summary of the model’s results, as shown above. This summary contains various statistical metrics, including coefficients, standard errors, p-values, confidence intervals, and more, depending on the type of model you’ve fitted. The table is divided into three sections.

Number of observations: The number of data points used in the analysis. Method: least square. Find the best line by minimizing the the sum of the squared errors. Degree of freedom: number of independent variables Statsmodels is a Python library for statistical models and quantitative analysis. It provides a comprehensive suite of tools for model estimation, statistical tests, and data exploration.

The library emphasizes statistical computation, model inspection, and rigorous statistical methods rather than machine learning or predictive modeling. This overview introduces the core architecture, main model families, and common usage patterns in statsmodels. For more specific information about individual model types, see the following wiki pages: Statsmodels is organized around several model families that share common base classes and interfaces. At the highest level, models inherit from the base Model class, with specialized models extending this foundation to implement specific statistical techniques. Sources: statsmodels/base/model.py65-188 statsmodels/regression/linear_model.py193-449), statsmodels/discrete/discrete_model.py173-931 statsmodels/genmod/generalized_linear_model.py82-292 statsmodels/tsa/base/tsa_model.py98-135

The core architecture follows several key principles: Construction does not take any parameters. Tables and text can be added with the add_ methods. Contains the list of SimpleTable instances, horizontally concatenated tables are not saved separately. extra lines that are added to the text output, used for warnings and explanations. add additional text that will be added at the end in text format

add_table_2cols(res[, title, gleft, gright, ...]) This document describes the Formula API in statsmodels, which provides an R-style formula interface for specifying statistical models. The Formula API allows users to express model specifications using a concise, string-based syntax rather than directly managing design matrices. This approach simplifies model creation and enhances readability by allowing users to focus on the statistical relationships rather than data manipulation details. For information about direct data management without formulas, see Data Management. The Formula API provides a consistent interface for specifying models using R-like formulas.

It leverages the patsy library for formula parsing and design matrix creation, which then feeds into statsmodels' model classes. Sources: statsmodels/formula/api.py12-32 The Formula API provides formula-based constructors for many statsmodels model classes. Each of these constructors is a convenience function that calls the from_formula method of the corresponding model class. The name of the endog variable in the tables. The default is y.

The names for the exogenous variables, default is “var_xx”. 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. Class that holds the summary tables and text, which can be printed or converted to various output formats.

Linear regression is a popular statistical technique used to model the relationship between a dependent variable and one or more independent variables. In this article, we will explore the use of the Statsmodels API to create a linear regression model and understand the detailed report it provides. Linear regression models are widely used in data analysis and predictive modeling. They can help us understand the relationship between variables and make predictions Based on the available data. The Statsmodels API offers a comprehensive report that provides valuable insights into the statistical properties used to create the linear regression model. The Statsmodels API report provides various statistical properties and tests that help us assess the goodness of fit of our linear regression model.

These properties include the target variable, multicollinearity, statistical significance, R-squared value, adjusted R-squared value, F-statistic, and the relevance of each feature. Before delving into the report, it is essential to understand the statistical properties used in the linear regression model. These properties include the target variable, multicollinearity, and other fine details that contribute to the understanding of how linear regression works. The target variable, also known as the dependent variable, is the variable we aim to predict or explain using independent variables. In linear regression, we analyze the relationship between the target variable and the independent variables to fit the model.

Results And Summary Tables Statsmodels Statsmodels Deepwiki

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When Building A Regression Model Using Python’s Statsmodels Library, A