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Correlation Coefficient Formula: What is it?

Probability and Statistics > Statistics Definitions > Correlation Coefficient Formula

Also see: Correlation Coefficient (How to Find it).

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Correlation Coefficient Formula: Overview.

Correlation coefficient formulas are used to find how strong a relationship is between data. The formulas return a value between -1 and 1, where:

  • 1 indicates a strong positive relationship.
  • -1 indicates a strong negative relationship.
  • A result of zero indicates no relationship at all.

correlation coefficient formula

Graphs showing a correlation of -1, 0 and +1



  • A correlation coefficient of 1 means that for every positive increase of 1 in one variable, there is a positive increase of 1 in the other.
  • A correlation coefficient of -1 means that for every positive increase of 1 in one variable, there is a negative decrease of 1 in the other.
  • Zero means that for every increase, there isn’t a positive or negative increase. The two just aren’t related.

The absolute value of the correlation coefficient gives us the relationship strength. The larger the number, the stronger the relationship. For example, |-.75| = .75, which has a stronger relationship than .65.

Types of correlation coefficient formulas.

There are several types of correlation coefficient formulas.

One of the most commonly used formulas in stats is Pearson’s correlation coefficient formula. In fact, if you’re taking a basic stats class, this is the one you’ll probably use:

pearson correlation

Pearson correlation coefficient

Two other formulas are commonly used: the sample correlation coefficient and the population correlation coefficient.

Sample correlation coefficient

what is correlation

Sx and sy are the sample standard deviations, and sxy is the sample covariance.

Population correlation coefficient


The population correlation coefficient uses σx and σy as the population standard deviations, and σxy as the population covariance.

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Correlation Coefficient Formula: What is it? was last modified: January 30th, 2016 by Andale

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