Predict response values for Partial Least Squares Regression

Stat > Regression > Partial Least Squares > Prediction

In PLS, there are two primary reasons to calculate and store predicted response values using your PLS model: testing prediction quality with a test data set and predicting new responses. For more information, go to Prediction in PLS regression.

New observation for continuous predictors

Enter the new observation for each continuous predictor in the same order that each predictor is entered in the model. You can enter one numeric value for each predictor or one numeric column of new observations for each predictor. Columns must have the same number of rows.

New observation for categorical predictors

Enter the new observation for each categorical predictor in the same order that each predictor is entered in the model. You can enter one value for each predictor or one column of new observations for each predictor. Columns must have the same number of rows. If you type a new observation, you must enclose text values in double quotes (e.g., "Female").

New observation for responses (optional)

Enter the numeric columns containing the response values that correspond to the new observations. If you enter response values, Minitab calculates a test R² to help you evaluate the model's predictive ability. You cannot type the response values; they must be stored in columns. The number of response columns must equal the number of responses in the model and have the same number of rows as the predictors containing new observations.

Confidence level

Enter the level of confidence for the confidence intervals and the prediction intervals. Usually, a confidence level of 95% works well. A 95% confidence level indicates that if you took 100 random samples from the population, the confidence intervals for approximately 95 of the samples would contain the mean response. Similarly, the prediction interval indicates that you can be 95% confident that the interval contains the value of a single new observation.

Storage

Fits: Store the fitted value for new observations.
SE of fits: Store the estimated standard errors of the fitted values.
Confidence limits: Store lower and upper limits of the confidence interval for the prediction.
Prediction limits: Store lower and upper limits of the prediction interval.