I am working on a project with many small stamped and molded components. We developed the product and processes in the US and are now running production in China. My customer requires me to pass a correlation between my measurement system in the US and my measurement system in China. Both are OGP optical comparators. We selected and numbered 30 pieces of each component and measured all the critical to function dimensions in the US and shipped the parts to China where they were measured again. I am running correlation analysis and a fitted line regression plot to compare my data sets. My customer has the following three requirements to pass correlation: 1. R-squared >0.75 2. Slope SE to 1 3. Bias SE to 0 As a bit of back story, I have only been a quality engineer for 9 months so I am still learning the statistics side of my job. My background is 27 years in manufacturing process engineering with an emphasis on continuous improvement. I am Greenbelt certified twice over. So to get to my question, the results of my analyses are showing very low R-squared values and low slope values. However, when the total part to part variation is .002 to .01mm acros a group of 30 pieces (min to max) the data looks more like a cloud than a line. Will I ever be able to pass a correlation between these two machines without getting my measurement error to something far less than my part to part variation? I really believe that based on the high accuracy of these parts that I will never be able to pass correlation. I appreciate any input you may have and would be happy to post a couple of sample data sets if it would be helpful.