Statistical Hypothesis Analysis in Python with ANOVAs, Chi-Square, and Pearson Correlation
Introduction
Python is an incredibly versatile language, useful for a wide variety of tasks in a wide range of disciplines. One such discipline is statistical analysis on datasets, and along with SPSS, Python is one of the most common tools for statistics.
Python’s user-friendly and intuitive nature makes running statistical tests and implementing analytical techniques easy, especially through the use of the statsmodels library.
Introducing The statsmodels Library In Python
The statsmodels library is a module for Python that gives easy access to a variety of statistical tools for carrying out statistical tests and exploring data. There are a number of statistical tests and functions that the library grants access to, including ordinary least squares (OLS) regressions, generalized linear models, logit models, Principal Component Analysis (PCA), and Autoregressive Integrated Moving Average (ARIMA) models.
The results of the models are constantly tested against other statistical packages to ensure that the models are accurate. When combined with SciPy and Pandas, it’s simple to visualize data, run statistical tests, and check relationships for significance.
Choosing A Dataset
Before we can practice statistics with Python, we