Last Modified Date: 11 Jun 2020
Environment
Tableau DesktopAnswer
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Option 1 - Using Tableau 10.2 and later versions
Step 1: Create a scatterplot
This example uses Superstore sample data and is attached to this article. Open the workbook Pearson Correlation.twbx for more information.- Drag Profit to Columns and Sales to Rows.
- 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.
- Locate the R-Squared value in the Describe Trend Model dialog box. In this example, the R-Squared value is 0.229503.
Step 2: Calculate the Pearson correlation
You can use different options to find the Pearson correlation. For example:
- Use a calculator or other program
- Calculate the square root of the R-squared value. Which will be your correlation (r): √0.229498 = 0.4791
- Rounded to two digits, the value in this example is 0.48.
- Create a calculated field using the CORR function.
- Enter a formula similar to the following and click OK:
CORR([Profit], [Sales])
- This formula 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.
- Enter a formula similar to the following and click OK:
- Create a calculated field using the WINDOW_CORR function.
- Enter a formula similar to the following and click OK:
WINDOW_CORR(SUM([Profit]), SUM([Sales]))
- 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.
- Enter a formula similar to the following and click OK:
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Option 2 - Using earlier versions of Tableau Desktop
The equivalent of the three calculations used in Option 1 can also be reproduced using the following formulas.
Instead of CORR
Instead of CORR
- Select Analysis > Create calculated field
- Name the calculated field
- Enter the following formula and click OK:
COVAR([Profit], [Sales]) / (STDEV([Profit])*STDEV([Sales]))
- Select Analysis > Create calculated field
- Name the calculated field
- Enter the following formula and click OK:
WINDOW_COVAR(SUM([Profit]), SUM([Sales]))/ (WINDOW_STDEV(SUM([Profit]))*WINDOW_STDEV(SUM([Sales])))
- Select Analysis > Create calculated field
- Name the calculated field
- Enter the following formula and click OK:
WINDOW_COVAR(SUM([Profit]), SUM([Sales]),-5,0)/ (WINDOW_STDEV(SUM([Profit]),-5,0)*WINDOW_STDEV(SUM([Sales]),-5,0))
Additional Information
- A 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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