Statsmodels is a Python library for conducting statistical analysis and modeling. It provides a range of tools and functions for estimating, testing, and evaluating statistical models, as well as for conducting statistical tests and other common tasks in data analysis.
Statsmodels is built on top of the popular scientific computing library NumPy, and it is designed to be compatible with the broader scientific Python ecosystem, including libraries such as Pandas and scikit-learn. It is widely used by researchers and data scientists for a variety of purposes, such as hypothesis testing, regression analysis, and time series forecasting.
Statsmodels is released under the BSD 3-clause license, and it is available for download from the Python Package Index (PyPI). It is well-documented and supported by an active community of users and contributors.
Overall, Statsmodels is a powerful and widely-used Python library for conducting statistical analysis and modeling. It provides a range of tools and functions for estimating, testing, and evaluating statistical models, and it is an important part of the broader scientific Python ecosystem.
Time series analysis and forecasting is a tough nut to crack, but the ARIMA model has been cracking it for decades. ARIMA, short for “Auto-Regressive Integrated Moving Average,” is a powerful statistical modeling technique for time series analysis. It’s particularly effective when the time series you’re analyzing follows a clear pattern, like seasonal changes in … Read more
