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Read this tutorial before you use Proc Corr

All of us at some point in the process of examining data, check for correlations among different variables in the data especially pair-wise correlations.

Among a large chunk of business analysts in industry, there exists a notion of ‘linear correlation coefficient’ being the only criterion for pair-wise correlation and hence at the maximum a Proc Corr is run in SAS to check for the same.  This  will of course be useful for finding out correlations between continuous variables. However it more often than not fails when confronted with real life data which frequently contains all kinds of variables, continuous, binary or multi-level categorical etc.

  • In a scenario where you are trying to find out correlation between continuous variablesProc Corr is a good choice, because it simply gives you linear correlation coefficients.
  • Now when you are looking at correlation between a binary variable and a continuous variable, your idea of correlation needs a little change in perspective. Simple linear correlation coefficient is rendered meaningless here, because one is not really dealing with meaningful numbers now, but categories. In many datasets you would observe that these categories have been given some numbers, but don’t confuse them with real numeric variables, they are just represented using numbers.  They very well could have been given some other numbers, changing the value of the linear correlation coefficient, if one was using the same to assess correlation in this case. How do you go about working around this problem then?

Read the full article and get the solution here:

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Edvancer is an online analytics training institute and offers courses across the field of analytics. Learn more at

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