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Partial Least Squares Regression (PLS)

Best for high-collinearity predictors and more variables than observations (chemometrics, spectra, process modeling). Auto-reports regression coefficients, R²X, R²Y, cross-validated Q², VIP variable importance, and recommends the optimal number of components.

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Partial Least Squares Regression

The first column is the response Y; the remaining columns are predictors X. Leave-one-out (LOO) cross-validation is supported.

Data (first column Y, rest X; space/tab separated, one row per observation)

One row per observation. The first column is the dependent variable Y; the rest are predictors X (can be highly correlated).

Max components

Standardize X/Y

Cross-validation

Model Result

AI Interpretation

AI Report

Generate a beautiful HTML chart report. Download as Word or print to PDF.

AI Report Preview

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