I have quick-checked a quite big data set, if there is a sense to spend time on the deeper analysis. I would like to know, if any predictor(s) are able to explain the response. I choosed the PLS Regression - with Cross Validation. I have included all the predictors without checking significance - without 2-way interactions. The results are: R-sq - 65% with 4 components R-sq (pred) - 0% R-sq is the percentage of variation in the response that is explained by the model. R-sq (pred) - Predicted R-sq --------- What does it mean for the process? With the data i can explain 65% of variance in the response. But the predictive ability is 0% - or near to 0%. How could be such an information used? Okay, 65% could be explained, but in case that the predictive ability is 0%, then there is no need to spend the time on the process improvement (with the actual data)?