Multivariate data analysis is about separating the signal from the noise in data with many variables and presenting the results as easily interpretable plots. Any large complex table of data can easily be transformed into intuitive plots summarizing the essential information. The following methods are all based on mathematical projection, but have evolved to meet different needs.
Principal Components Analysis (PCA) provides a concise overview of a dataset and is usually the first step in any analysis. It is very powerful at recognising patterns in data: outliers, trends, groups etc.
With Projections to Latent Structures (PLS), the aim is to establish relationships between input and output variables, creating predictive models.
PLS-Discriminant Analysis (PLS-DA) and SIMCA are two powerful methods for classification. Again, the aim is to create a predictive model, but one which can accurately classify future unknown samples.
Let's continue with a demonstration of the principles and application of PCA.