Statistics Stats Statsmodels 0 14 4

Leo Migdal

-Dec 4, 2025, 6:27 AM

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]) pip install statsmodels Copy PIP instructions Statistical computations and models for Python 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

There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. This patch release fixes an issue with recent SciPy releases (1.16+) that prevented statsmodels from importing. It also addresses some small changes that improve future compatibility.

There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. The statsmodels developers are pleased to announce the release of 0.14.4. This release contains one feature and no fixes.

Statsmodels is a Python library that enables us to estimate and analyze various statistical models. It is built on numeric and scientific libraries like NumPy and SciPy. It provides classes & functions for the estimation of many different statistical models. Before installing Statsmodels, ensure that you have: The easiest way to install Statsmodels is by using pip. Run the following command in your terminal or command prompt:

python -m venv env .\env\Scripts\activatepip install statsmodels This will automatically install Statsmodels with its dependencies including NumPy, SciPy, Pandas, and Patsy. Patsy is used for handling formulas in statistical models. Statistical computations and models for Python The statsmodels/statsmodels repo was created 13 years ago and the last code push was Yesterday. The project is extremely popular with a mindblowing 10055 github stars!

Here are some statsmodels code examples and snippets. The statsmodels package has 2835 open issues on GitHub Probabilistic modeling and statistical inference in TensorFlow statsmodels is using github to store the updated documentation. Two version are available: Development, the latest build of the main branch

API stability is not guaranteed for new features, although even in this case changes will be made in a backwards compatible way if possible. The stability of a new feature depends on how much time it was already in statsmodels main and how much usage it has already seen. If there are specific known problems or limitations, then they are mentioned in the docstrings. This release bring official Pyodide support to a statsmodel release. It is otherwise identical to the previous release. Special thanks to Agriya Khetarpal for working through Pyodide-specific issues, and improving other areas of statsmodels while doing so.

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/ Statistical computations and models for use with SciPy

To install this package, run one of the following: Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. Researchers across fields may find that statsmodels fully meets their needs for statistical computing and data analysis in Python. Statistical computations and models for use with SciPy 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:

Statistics Stats Statsmodels 0 14 4

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This Section Collects Various Statistical Tests And Tools. Some Can

Robust_kurtosis(y[, Axis, Ab, Dg, Excess]) Pip Install Statsmodels Copy PIP

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Statsmodels Is A Python Library That Enables Us To Estimate