What Canonical discriminant analysis is
Canonical discriminant analysis is a statistical technique used to find the differences between two or more groups of data. It is a multivariate technique used to find the linear combinations of variables that best discriminate between two or more groups of objects. It is often used in classification problems and works by maximizing the ratio of between-group variance to within-group variance.
Steps for Canonical Discriminant Analysis:
- Gather the data and define the groups or classes.
- Calculate the mean vectors and covariance matrices of the groups.
- Calculate the within-class and between-class scatter matrices.
- Calculate the eigenvalues and eigenvectors of the scatter matrices.
- Select the eigenvectors corresponding to the largest eigenvalues.
- Transform the original data using the selected eigenvectors.
- Use the transformed data to classify the objects into their respective groups.
Examples
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Canonical discriminant analysis can be used to determine differences between groups of data, such as differences between men and women in a psychological study.
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Canonical discriminant analysis can be used to analyze differences between countries in terms of economic growth rates.
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Canonical discriminant analysis can be used to identify differences between different types of cancer patients in terms of their response to treatment.
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Canonical discriminant analysis can be used to identify differences between different types of plants in terms of their growth and yield.