Multivariate Data Analysis (MVDA)
is an exceptionally
versatile method of visualising and understanding huge
With the use of our SIMCA-P+ software
impenetrable data tables are transformed into easily
understood diagnostic plots and charts. The Umetrics
approach emphasises accurate prediction of results and
Multivariate Data Analysis is about separating the signal
from the noise
in data with many variables
the results as easily interpretable plots.
Any large complex
table of data can easily be transformed into intuitive plots
summarizing the essential information.
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.
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.
PLS = Principal Component Analysis
PCA = Projection of Latent Structures