Multivariate Data Analysis (MVDA)

is an exceptionally
versatile method of visualising and understanding huge datasets.

With the use of our SIMCA-P+ software
large and impenetrable data tables are transformed into easily understood diagnostic plots and charts. The Umetrics approach emphasises accurate prediction of results and model interpretability.

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.

Overview 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.

Relationships With Projections to Latent Structures (PLS), the aim is to establish relationships between input and output variables, creating predictive models.

Classification LS-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.

Abbrevations

PLS = Principal Component Analysis
PCA = Projection of Latent Structures