See editing example When to use the Pearson correlation coefficient When r is 0, a line of best fit is not helpful in describing the relationship between the variables: 3 or between 0 and –.3, the points are far from the line of best fit: 5 or less than –.5, the points are close to the line of best fit: When r is 1 or –1, all the points fall exactly on the line of best fit: When the slope is positive, r is positive. When the slope is negative, r is negative. The Pearson correlation coefficient also tells you whether the slope of the line of best fit is negative or positive. Visualizing the Pearson correlation coefficientĪnother way to think of the Pearson correlation coefficient ( r) is as a measure of how close the observations are to a line of best fit. Specifically, we can test whether there is a significant relationship between two variables. The Pearson correlation coefficient is also an inferential statistic, meaning that it can be used to test statistical hypotheses. Specifically, it describes the strength and direction of the linear relationship between two quantitative variables.Īlthough interpretations of the relationship strength (also known as effect size) vary between disciplines, the table below gives general rules of thumb: Pearson correlation coefficient ( r) value The Pearson correlation coefficient is a descriptive statistic, meaning that it summarizes the characteristics of a dataset. Pearson product-moment correlation coefficient (PPMCC).The Pearson correlation coefficient ( r) is the most widely used correlation coefficient and is known by many names: What is the Pearson correlation coefficient? Frequently asked questions about the Pearson correlation coefficient.Reporting the Pearson correlation coefficient.Testing for the significance of the Pearson correlation coefficient.Calculating the Pearson correlation coefficient.When to use the Pearson correlation coefficient.Visualizing the Pearson correlation coefficient.What is the Pearson correlation coefficient?.The higher the elevation, the lower the air pressure. When one variable changes, the other variable changes in the opposite direction. The price of a car is not related to the width of its windshield wipers. There is no relationship between the variables. The longer the baby, the heavier their weight. When one variable changes, the other variable changes in the same direction. It is a number between –1 and 1 that measures the strength and direction of the relationship between two variables. The Pearson correlation coefficient ( r ) is the most common way of measuring a linear correlation.
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