How to run a Discriminant Analysis using SPSS

Discriminant Analysis is a statistical technique used to classify observations into groups based on their characteristics or features. It's particularly useful when you have a set of observations and you want to classify them into predefined categories or groups based on a set of continuous predictors or features.

Here's how it generally works:

Data Collection: Gather data on several variables for each observation.

Training: Using a subset of the data (training data), the model learns the statistical properties of the variables for each group or category.

Discriminant Function: It then creates a discriminant function based on these properties. This function is a linear combination of the predictor variables that maximally separates the groups.

Classification: For new observations, the model applies this discriminant function to predict the group to which they belong.

It's worth noting that there are different types of Discriminant Analysis, such as Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), which differ in their assumptions about the distribution of the predictor variables.

LDA assumes that the predictor variables have a multivariate normal distribution and that the variance-covariance matrices of the predictor variables are equal across groups. QDA relaxes these assumptions and allows for different variance-covariance matrices for each group.


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