Statsmodels is a Python library for statistical modelling and data analysis. It provides a wide range of statistical methods and models, including linear regression, time series analysis, generalised linear models, and mixed-effects models, among others. Statsmodels is built on top of the NumPy and Pandas libraries, which are commonly used in data analysis and scientific computing.
Statsmodels provides a comprehensive set of tools for statistical analysis, including hypothesis testing, estimation of model parameters, and diagnostic tests. It also includes visualisation tools for exploratory data analysis and model diagnostics. Statsmodels is designed to be used in both academic and industry settings and is widely used in various fields, including finance, economics, social sciences, and healthcare.
Statsmodels is a powerful and flexible library that provides a wide range of statistical methods for modelling and analysing data in Python. Its user-friendly interface and extensive documentation make it accessible to both beginners and experienced users.
I don’t use statsmodels a lot, focusing primarily on Python’s machine learning library, sklearn, but it is always a good idea to familiarise oneself with this library. One function that is unique to statsmodels is its summary() function, or summary2() function…
