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Kendall Rank Correlation Explained.

Joseph Magiya
Towards Data Science
3 min readJun 17, 2019

What about the Kendall Rank Correlation (also known as Kendall’s tau-b)? What is it? How do I get started? When do I use the Kendall’s tau-b? Hey, just teach me everything you know about Kendall Rank Correlation. “ — A curious mind.

What is correlation?

Correlation is a bi-variate analysis that measures the strength of association between two variables and the direction of the relationship. In terms of the strength of relationship, the value of the correlation coefficient varies between +1 and -1. A value of ± 1 indicates a perfect degree of association between the two variables. As the correlation coefficient value goes towards 0, the relationship between the two variables will be weaker. The direction of the relationship is indicated by the sign of the coefficient; a + sign indicates a positive relationship and a — sign indicates a negative relationship.

Usually, in statistics, we measure four types of correlations:

  • Pearson correlation (parametric)
  • Kendall rank correlation (non-parametric)
  • Spearman correlation (non-parametric)
  • Point-Biserial correlation.

Kendall Rank Correlation

Also commonly known as “Kendall’s tau coefficient”. Kendall’s Tau coefficient and Spearman’s rank correlation coefficient assess statistical associations based on the ranks of the data. Kendall rank correlation (non-parametric) is an…

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Towards Data Science
Towards Data Science

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