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Statsmodels Examples Statsmodels V0 10 1 Documentation

Leo Migdal
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statsmodels examples statsmodels v0 10 1 documentation

Examples are invaluable for new users who hope to get up and running quickly with statsmodels, and they are extremely useful to those who wish to explore new features of statsmodels. We hope to provide documentation and tutorials for as many models and use-cases as possible! Please consider submitting an example with any PR that introduces new functionality. User-contributed examples/tutorials/recipes can be placed on the statsmodels examples wiki page That wiki page is freely editable. Please post your cool tricks, examples, and recipes on there! If you would rather have your example file officially accepted to the statsmodels distribution and posted on this website, you will need to go through the normal patch submission process and follow the instructions...

Examples are best contributed as IPython notebooks. Save your notebook with all output cells cleared in examples/notebooks. From the notebook save the pure Python output to examples/python. The first line of the Notebook must be a header cell that contains a title for the notebook, if you want the notebook to be included in the documentation. We have a gallery of example notebooks available here. If you would like your example to show up in this gallery, add a link to the notebook in docs/source/examples/landing.json.

For the thumbnail, take a screenshot of what you think is the best “hook” for the notebook. The image will be displayed at 360 x 225 (W x H). It’s best to save the image as a PNG with a resolution that is some multiple of 360 x 225 (720 x 450 is preferred). 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. Since version 0.5.0 of statsmodels, you can use R-style formulas together with pandas data frames to fit your models. 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. When using statsmodels in scientific publication, please consider using the following citation: 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/ This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository. We also encourage users to submit their own examples, tutorials or cool statsmodels trick to the Examples wiki page SARIMAX: Frequently Asked Questions (FAQ) State space modeling: Local Linear Trends

Fixed / constrained parameters in state space models State space modeling: Local Linear Trends © 2009–2012 Statsmodels Developers© 2006–2008 Scipy Developers© 2006 Jonathan E. TaylorLicensed under the 3-clause BSD License. http://www.statsmodels.org/stable/examples/index.html Robust linear models with support for the M-estimators listed under Norms.

See Module Reference for commands and arguments. PJ Huber. ‘Robust Statistics’ John Wiley and Sons, Inc., New York. 1981. PJ Huber. 1973, ‘The 1972 Wald Memorial Lectures: Robust Regression: Asymptotics, Conjectures, and Monte Carlo.’ The Annals of Statistics, 1.5, 799-821.

R Venables, B Ripley. ‘Modern Applied Statistics in S’ Springer, New York, Statsmodels Python modules are providing classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. This package contains HTML documentation and example scripts for python-statsmodels.

State space modeling: Local Linear Trends State space models: concentrating out the scale statsmodels is a Python package that provides a collection of widely-used statistical models. While statsmodels historically has an econometrics-heavy user base, the package is designed to be useful to a large variety of statistical use cases. In comparison with other Python-based modelling tools, statsmodels focuses more heavily on the statistics and diagnostics underlying the models than having the most cutting-edge or predictive models. These are shorthand for endogenous and exogenous variables.

You might be more comfortable with the common y and X notation in linear models. Sometimes the endogenous variable y is called a dependent variable. Likewise, sometimes the exogenous variables X are called the independent variables. You can read about this in greater detail at endog, exog, what’s that? Missing data can be handled via the missing keyword argument. Every model takes this keyword.

You can find more information in the docstring of statsmodels.base.Model. The appropriate dependencies (numpy, pandas, scipy, Cython) installed Please review our installation instructions for details.

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Examples Are Invaluable For New Users Who Hope To Get

Examples are invaluable for new users who hope to get up and running quickly with statsmodels, and they are extremely useful to those who wish to explore new features of statsmodels. We hope to provide documentation and tutorials for as many models and use-cases as possible! Please consider submitting an example with any PR that introduces new functionality. User-contributed examples/tutorials/rec...

Examples Are Best Contributed As IPython Notebooks. Save Your Notebook

Examples are best contributed as IPython notebooks. Save your notebook with all output cells cleared in examples/notebooks. From the notebook save the pure Python output to examples/python. The first line of the Notebook must be a header cell that contains a title for the notebook, if you want the notebook to be included in the documentation. We have a gallery of example notebooks available here. ...

For The Thumbnail, Take A Screenshot Of What You Think

For the thumbnail, take a screenshot of what you think is the best “hook” for the notebook. The image will be displayed at 360 x 225 (W x H). It’s best to save the image as a PNG with a resolution that is some multiple of 360 x 225 (720 x 450 is preferred). statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for con...

The Package Is Released Under The Open Source Modified BSD

The package is released under the open source Modified BSD (3-clause) license. The online documentation is hosted at statsmodels.org. Since version 0.5.0 of statsmodels, you can use R-style formulas together with pandas data frames to fit your models. 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 availab...

Attributes Are Described In Results.__doc__ And Results Methods Have Their

Attributes are described in results.__doc__ and results methods have their own docstrings. When using statsmodels in scientific publication, please consider using the following citation: 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 late...