What is a lack of fit sum of squares analysis?
Basically, just the inverse of the correlation coefficient.
Normally, we are interested in how well the points fit a modeled line. In a sum of the squares analysis that is the correlation or regression coefficient.
If you want to see how poorly the data fit a model, you could take the inverse. Then, a "high" number (anything over 1.2 for example) indicates a POOR fit.
Personally, I don't see any value in the practice.
For example if