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Measures of Association

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2016-03-26 7:35:35
Statistics for Big Data For Dummies
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Measures of association quantify the strength and the direction of the relationship between two data sets. Here are the two most commonly used measures of association:

  • Covariance

  • Correlation

Both measures are used to show how closely two data sets are related to each other. The main difference between them is the units in which they are measured. The correlation measure is defined to assume values between –1 and 1, which makes interpretation very easy.

Covariance

The covariance between two samples is computed as follows:

image0.jpg

The covariance between two populations is computed as follows:

image1.jpg

Correlation

The correlation between two samples is computed like this:

image2.jpg

The correlation between two populations is computed like this:

image3.jpg

About This Article

This article is from the book: 

About the book author:

Alan Anderson, PhD is a teacher of finance, economics, statistics, and math at Fordham and Fairfield universities as well as at Manhattanville and Purchase colleges. Outside of the academic environment he has many years of experience working as an economist, risk manager, and fixed income analyst. Alan received his PhD in economics from Fordham University, and an M.S. in financial engineering from Polytechnic University.

David Semmelroth has two decades of experience translating customer data into actionable insights across the financial services, travel, and entertainment industries. David has consulted for Cedar Fair, Wachovia, National City, and TD Bank.