Discriminant analysis is used to distinguish distinct sets of observations, and to allocate new observations to previously defined groups. This method is commonly used in biology for classification of animal species, and in medicine for classification of tumor types. It is also used in facial recognition technologies for classifying pixel values, and in the credit and insurance industries for classifying risk.

Discriminant analysis has two main goals:

• Discrimination
  Construct a classifier to separate the distinct set of observations from all observations in a known population.
• Classification
  Separate unlabeled observations into labeled groups using a classifier.

For discriminant analysis, Origin provides two different probability settings:

• Equal
• Proportional to group size
  

Origin provides two methods for computing discriminant functions:

• Linear
• Quadratic

Discriminant Analysis

A tool to distinguish distinct sets of observations, and to allocate new observations to previously defined groups