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Statsmodels Statsmodels Deepwiki

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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: statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. The documentation for the latest release is at The documentation for the development version is at

Recent improvements are highlighted in the release notes https://www.statsmodels.org/stable/release/ statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license.

The online documentation is hosted at statsmodels.org. statsmodels supports specifying models using R-style formulas and pandas DataFrames. Here is a simple example using ordinary least squares: You can also use numpy arrays instead of formulas: Have a look at dir(results) to see available results. Attributes are described in results.__doc__ and results methods have their own docstrings.

Please use following citation to cite statsmodels in scientific publications: This document explains how to install, set up a development environment, and contribute to the Statsmodels project. It covers installation methods, development setup, building and testing procedures, and documentation generation. For information about using the installed package, see the corresponding user documentation. Statsmodels can be installed in several ways depending on your needs: Sources: docs/source/install.rst1-97 INSTALL.txt69-107

Sources: docs/source/install.rst128-165 INSTALL.txt1-67 requirements.txt1-8 setup.py46-71 The build process is managed by setuptools with custom enhancements for Cython extensions. The build configuration is defined in: Python ecosystem is equipped with many tools and libraries which primarily focus on prediction or machine learning. For example, scikit-learn focuses on predictive modeling and machine learning and does not provide statistical summaries (like p-values, confidence intervals, R² adj.).

SciPy.statsfocuses on Individual statistical tests and distributions but has no modeling framework (like OLS or GLM). Other libraries like linearmodels , PyMC / Bambi , Pingouin have their own limitations. Statsmodels was developed to fill the gap created by these existing tools. This page covers the statistical tests and diagnostics available in the statsmodels library. These tests help you validate model assumptions, detect specification issues, and evaluate goodness-of-fit. For information about model specification and fitting, see Regression and Discrete Choice Models.

Regression diagnostics are tests and procedures used to evaluate the assumptions underlying regression models. Heteroskedasticity tests check if the variance of the errors is constant across observations. Autocorrelation tests check if the residuals are correlated with their own lagged values. Tests for normality of residuals or data distribution. statsmodels is a Python package that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and exploring statistical data. Compare nested models with F-tests or likelihood ratio tests:

Focus on effect sizes, not just p-values: Use robust standard errors when necessary: Using p-values alone for variable selection: statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct.

The package is released under the open source Modified BSD (3-clause) license. The online documentation is hosted at statsmodels.org. statsmodels supports specifying models using R-style formulas and pandas DataFrames. Here is a simple example using ordinary least squares: You can also use numpy arrays instead of formulas: Have a look at dir(results) to see available results.

Attributes are described in results.__doc__ and results methods have their own docstrings. Please use following citation to cite statsmodels in scientific publications: 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.

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Statsmodels Is A Python Library For Statistical Models And Quantitative

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 f...

At The Highest Level, Models Inherit From The Base Model

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 arc...

Recent Improvements Are Highlighted In The Release Notes Https://www.statsmodels.org/stable/release/ Statsmodels

Recent improvements are highlighted in the release notes https://www.statsmodels.org/stable/release/ statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested ...

The Online Documentation Is Hosted At Statsmodels.org. Statsmodels Supports Specifying

The online documentation is hosted at statsmodels.org. statsmodels supports specifying models using R-style formulas and pandas DataFrames. Here is a simple example using ordinary least squares: You can also use numpy arrays instead of formulas: Have a look at dir(results) to see available results. Attributes are described in results.__doc__ and results methods have their own docstrings.

Please Use Following Citation To Cite Statsmodels In Scientific Publications:

Please use following citation to cite statsmodels in scientific publications: This document explains how to install, set up a development environment, and contribute to the Statsmodels project. It covers installation methods, development setup, building and testing procedures, and documentation generation. For information about using the installed package, see the corresponding user documentation....