Finding the Pearson Correlation

Published: 15 Feb 2017
Last Modified Date: 25 Apr 2017


How to find the Pearson correlation of two measures in Tableau Desktop.


Tableau Desktop


Please note that New correlation and covariance functions were added to Tableau Desktop 10.2, for more information see What's new in Tableau Desktop.

Step 1: Create a scatterplot

This example uses Superstore sample data. 
  1. Drag Profit to Columns and Sales to Rows.
  2. Drag Customer Name to Detail.
  3. In the Analysis menu, uncheck Aggregate Measures
  4. Right-click the view and choose Trend Lines > Show Trend Lines.
  5. Right-click the view again and select Trend Lines > Describe Trend Model

Step 2:

Option 1: Calculate the correlation

Locate the R-Squared value in the Describe Trend Model dialog box. In this example, the R-Squared value is 0.229503.
User-added image
Using a calculator or other program, calculate the square root of the R-squared value. This is your correlation (r). Rounded to two digits, the value in this example is 0.48. 

Option 2: New in Tableau 10.2 use one of the built-in functions

  • CORR(expression 1, expression 2) aggregate function

Returns the Pearson correlation coefficient of two expressions. The Pearson correlation measures the linear relationship between two variables. Results range from -1 to +1 inclusive, where 1 denotes an exact positive linear relationship, as when a positive change in one variable implies a positive change of corresponding magnitude in the other, 0 denotes no linear relationship between the variance, and −1 is an exact negative relationship.

  • WINDOW_CORR(expression1, expression2, [start, end]) table calculation
Returns the Pearson correlation coefficient of two expressions within the window. The window is defined as offsets from the current row. Use FIRST()+n and LAST()-n for offsets from the first or last row in the partition. If start and end are omitted, the entire partition is used.


The following formula returns the Pearson correlation of SUM(Profit) and SUM(Sales) from the five previous rows to the current row.

WINDOW_CORR(SUM[Profit]), SUM([Sales]), -5, 0)

Additional Information

alculationA correlation, r, is a single number that represents the degree of relationship between two measures. The correlation coefficient is a value such that -1 <= r <= 1.

A positive correlation indicates a relationship between x and y measures such that as values of x increase, values of y also increase.

A negative correlation indicates the opposite—as values of x increase, values of y decrease.

The closer the correlation, r, is to -1 or 1, the stronger the relationship between x and y.

If r is close to or equal to 0, there is a weak relationship or no relationship between the measures.

As a general rule, you can interpret r values this way:

  • +.70 or higher indicates a very strong positive relationship
  • +.40 to +.69 indicates a strong positive relationship
  • +.20 to +.39 indicates a moderate positive relationship
  • -.19 to +.19 indicates no or a weak relationship
  • -.20 to -.39 indicates a moderate negative relationship
  • -.40 to -.69 indicates a strong negative relationship
  • -.70 or lower indicates a very strong negative relationship
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