Introduction to Python StatsModels

Statsmodels is a Python library that is built specifically for statistics as well as for conducting statistical tests and statistical data exploration. It is a Python module and Sklearn a Library. 

Statsmodels is built on top of NumPy, SciPy, and matplotlib,  but it also has some more advanced functions for statistical testing and modelling that is not present in any numerical library NumPy or SciPy. 

Statsmodels is a Python package that is used for exploring results, estimating statistical models, and running statistical tests.  In this package for various types of data and each estimator, a comprehensive list of descriptive statistics, statistical measures, plotting functions, and outcome statistics are available. It generally focuses on data analysis, data science, and statistics. 

It was started by Jonathan Taylor, a statistician now at Stanford, as part of SciPy under the name models. 

Its intended audience is both theoretical and applied statisticians and econometricians as well as Python users and developers across disciplines who use statistical models. Users of R, Stata, SAS, SPSS, NLOGIT, GAUSS or MATLAB for statistics, financial econometrics, or econometrics who would rather work in Python for all its benefits may find statsmodels a useful addition to their toolbox. This paper introduces statsmodels and is aimed at the researcher who has some prior experience with Python, NumPy/SciPy.

It is designed for hardcore analytics which helps us to focus on statistics in a way that no one else can do. The current main developers of statsmodels are trained as economists with a background in econometrics. As such, much of the development over the last year has focused on econometric applications.

Thus the design of the stats model follows consistent patterns which make it user-friendly and easily extensible by developers from any discipline. It makes simple models without much hassle and with fair a number of lines of code.

It moreover presents the yield in a way that's less demanding to examined and get it. We hope that statsmodels too can become an integral part of the Scientific Python community and serve as a step in the direction of Python becoming a serious open-source language for statistics.