Finding the relationship between input variables and the output from the process can be vital to implementing a successful Six Sigma strategy. In the below chart, we can say that the data is correlated strongly. But, how strongly?

To find out, we can use a calculation, which will tell us how linear the relationship is between the variables. We can’t use this for data that follows a non-linear pattern, so it’s always worth graphing before starting your calculations.

So, we use the above formula to find R which is the correlation co-efficient.

- N = the number of data points.
- Xi = individual X measurement.
- Yi = individual Y measurement.
- X and Y Bar = mean of all X or Y measures.
- Standard deviation of X values is depicted by the standard deviation sign.
- Standard deviation of Y values is depicted by the standard deviation sign.

If R is less than zero, we can say that there is a negative correlation. So, if one value goes up, the other goes down. If however, it’s greater than zero, we can say that there is a positive correlation. That is, if one variable goes up, so does the other.

The closer R is to 1 or -1, the stronger the relationship is. If it’s equal to 1 or minus one, then it’s a perfect linear relationship.

You can see from the worked example below that we have a result of 0.99. Which shows a very closely, positively correlated data set.

We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it.Ok