Statistical Tests And Diagnostics Statsmodels Statsmodels Deepwiki

Leo Migdal

-Dec 4, 2025, 5:50 AM

statistical tests and diagnostics statsmodels statsmodels deepwiki

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. In many cases of statistical analysis, we are not sure whether our statistical model is correctly specified. For example when using ols, then linearity and homoscedasticity are assumed, some test statistics additionally assume that the errors are normally distributed or that we have a large sample. Since our results depend on these statistical assumptions, the results are only correct of our assumptions hold (at least approximately). One solution to the problem of uncertainty about the correct specification is to use robust methods, for example robust regression or robust covariance (sandwich) estimators. The second approach is to test whether our sample is consistent with these assumptions.

The following briefly summarizes specification and diagnostics tests for linear regression. For these test the null hypothesis is that all observations have the same error variance, i.e. errors are homoscedastic. The tests differ in which kind of heteroscedasticity is considered as alternative hypothesis. They also vary in the power of the test for different types of heteroscedasticity. Lagrange Multiplier Heteroscedasticity Test by Breusch-Pagan

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.

There are multiple Python libraries/packages that allow you to perform complex statistical tests and build various models. 8.5. Interactive Visualizations Using Bokeh By Jun Yan and students in STAT 5255/3255, Spring 2022 © Copyright 2021. When working with statsmodels, a Python module that provides classes and functions for estimating and testing regression models, it's crucial to understand advanced statistical tests and diagnostic checks available within this library. These tools are vital for validating the models and ensuring robust results.

In this article, we will discuss how to implement advanced statistical tests and perform diagnostic checks in statsmodels. Advanced statistical tests allow us to gain more nuanced insights into our data and models. In statsmodels, you can perform several tests which help in validating different assumptions and checking for issues such as heteroscedasticity, serial correlation, and non-normal distribution of errors. This test compares the goodness of fit of two nested models. A nested model refers to a simpler model that is a subset of a more complex model. The Wald test assesses the significance of individual model coefficients.

It checks whether the estimated parameters are significantly different from zero or some other value. This test, also known as LM test, is used to determine if adding more parameters to the model could provide a significantly better fit to the data. This document covers the various diagnostic tests provided in statsmodels for evaluating model assumptions and specification. Diagnostic tests help users assess whether a fitted model is appropriate for the data at hand by checking assumptions such as normality of residuals, absence of heteroskedasticity, absence of serial correlation, and model stability. For visualization tools related to diagnostics, see Visualization Tools. For multiple testing procedures, see Multiple Testing.

Statsmodels provides a comprehensive suite of diagnostic tests organized into the following categories: Sources: statsmodels/stats/diagnostic.py1-30 statsmodels/stats/diagnostic.py52-56 The following diagram illustrates a typical workflow for applying diagnostic tests to evaluate a model: This section collects various statistical tests and tools. Some can be used independently of any models, some are intended as extension to the models and model results. API Warning: The functions and objects in this category are spread out in various modules and might still be moved around.

We expect that in future the statistical tests will return class instances with more informative reporting instead of only the raw numbers. Calculate the medcouple robust measure of skew. Calculates the four skewness measures in Kim & White robust_kurtosis(y[, axis, ab, dg, excess]) 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. 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: This section collects various statistical tests and tools. Some can be used independently of any models, some are intended as extension to the models and model results. API Warning: The functions and objects in this category are spread out in various modules and might still be moved around. We expect that in future the statistical tests will return class instances with more informative reporting instead of only the raw numbers. See also the notes on notes on regression diagnostics

The following functions calculate covariance matrices and standard errors for the parameter estimates that are robust to heteroscedasticity and autocorrelation in the errors. Similar to the methods that are available for the LinearModelResults, these methods are designed for use with OLS. The following are standalone versions of the heteroscedasticity robust standard errors attached to LinearModelResults There was an error while loading. Please reload this page.

Statistical Tests And Diagnostics Statsmodels Statsmodels Deepwiki

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