Last Modified Date: 25 Apr 2017
AnswerPlease 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 scatterplotThis example uses Superstore sample data.
- Drag Profit to Columns and Sales to Rows.
- Drag Customer Name to Detail.
- In the Analysis menu, uncheck Aggregate Measures.
- Right-click the view and choose Trend Lines > Show Trend Lines.
- Right-click the view again and select Trend Lines > Describe Trend Model.
Option 1: Calculate the correlationLocate the R-Squared value in the Describe Trend Model dialog box. In this example, the R-Squared value is 0.229503.
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
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)
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