Pearson Correlation Coefficient

Pearson’s correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship.

Pearson Correlation Coefficient Calculator

The Pearson correlation coefficient is used to measure the strength of a linear association between two variables, where the value r = 1 means a perfect positive correlation and the value r = -1 means a perfect negataive correlation. So, for example, you could use this test to find out whether people's height and weight are correlated (they will be - the taller people are, the heavier they're likely to be).

Requirements for Pearson's correlation coefficient

• Scale of measurement should be interval or ratio
• Variables should be approximately normally distributed
• The association should be linear
• There should be no outliers in the data

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Pearson Correlation Coefficient - Practical Uses in Investing

For an investor who wishes to diversify a portfolio, the Pearson coefficient can be useful. Calculations from scatter plots of historical returns between pairs of assets such as equities-bonds, equities-commodities, bonds-real estate, etc., or more specific assets such as large cap equities, small cap equities and debt-emerging market equities will produce Pearson coefficients to assist the investor in assembling a portfolio based on risk and return parameters. Note, however, that a Pearson coefficient measures correlation, not causation. If large cap and small cap equities have a coefficient of 0.8, it will not be known what caused the relatively high strength of association.