Advantages of the projection to latent structures (PLS) method
Introduction to Projection to Latent Structures (PLS) Visualization latent variable models with linking and brushing Using indicator variables in a latent variable model Determining the number of components to use in the model with cross-validation Algorithms to calculate (build) PCA models Preprocessing the data before building a model Interpreting loadings and scores together More about the direction vectors (loadings) Extended topics related to designed experiments Blocking and confounding for disturbances Highly fractionated designs: beyond half-fractions Generators: to determine confounding due to blocking Generating the complementary half-fraction Example: analysis of systems with 4 factors Assessing significance of main effects and interactions Example: design and analysis of a three-factor experiment Analysis of a factorial design: interaction effects Analysis of a factorial design: main effects Changing one single variable at a time (COST) Experiments with a single variable at two levels Design and analysis of experiments in context Outliers: discrepancy, leverage, and influence of the observations More than one variable: multiple linear regression (MLR) Summary of steps to build and investigate a linear model Least squares models with a single x-variable The industrial practice of process monitoring Statistical tables for the normal- and t-distribution The normal distribution and checking for normality General summary: revealing complex data graphically
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