How to Calculate Nonparametric Rank Correlation in Python
Last Updated on August 8, 2019
Correlation is a measure of the association between two variables.
It is easy to calculate and interpret when both variables have a well understood Gaussian distribution. When we do not know the distribution of the variables, we must use nonparametric rank correlation methods.
In this tutorial, you will discover rank correlation methods for quantifying the association between variables with a non-Gaussian distribution.
After completing this tutorial, you will know:
- How rank correlation methods work and the methods are that are available.
- How to calculate and interpret the Spearman’s rank correlation coefficient in Python.
- How to calculate and interpret the Kendall’s rank correlation coefficient in Python.
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Tutorial Overview
This tutorial is divided into 4 parts; they are:
- Rank Correlation
- Test Dataset
- Spearman’s Rank Correlation
- Kendall’s Rank Correlation
Rank Correlation
Correlation refers to the association between the observed values of two variables.
The variables may have a positive association, meaning that as the values for one variable increase, so do the values of the other variable.
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