Answer

Step 1: Create a scatterplot

1. Drag Profit to Columns and Sales to Rows.
2. In the Analysis menu, uncheck Aggregate Measures. 
3. Right-click the view and choose Trend Lines > Show Trend Lines.
4. Right-click the view again and select Trend Lines > Describe Trend Model. 
5. 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: 

1. Use a calculator or other program
   1. Calculate the square root of the R-squared value. Which will be your correlation (r): √0.229498 = 0.4791
   2. Rounded to two digits, the value in this example is 0.48.
2. 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.
3. 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.

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