Because the predicted R 2 only decreased slightly, the model is not overfit and you may decide it better suits your data.ДЄ predicted R 2 that is substantially less than R 2 may indicate that the model is over-fit. Consider an example where removing two components from the model that Minitab only slightly decreases predicted R 2. If you used cross-validation, compare the R 2 and predicted R 2. In some cases, you may decide to use a different model than the one initially selected by Minitab. Predicted R 2 indicates the predictive ability of the model and is only displayed if you perform cross-validation. To determine the number of components that is best for your data, examine the Model selection table, including the X-variance, R 2, and predicted R 2 values. When using PLS, select a model with the smallest number of components that explain a sufficient amount of variability in the predictors and the responses. You can also examine the Model selection plot. Examine the Method table to determine how many components Minitab included in the model. The default number of components is 10 or the number of predictors in your data, whichever is less. If you do not use cross-validation, you can specify the number of components to include in the model or use the default number of components. With cross-validation, Minitab selects the model with the highest predicted R 2 value. When you fit a PLS model, you can perform cross-validation to help you determine the optimal number of components in the model. The objective with PLS is to select a model with the appropriate number of components that has good predictive ability.

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