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In statistics, canonical-correlation analysis ( CCA ), also called canonical variates analysis, is a way of inferring information from cross-covariance matrices.
Aug 28, 2020 · Learn what canonical correlation analysis (CCA) is and how it can derive the relationship between two sets of variables. See an example of CCA applied to a real-world data set of psychological and academic variables.
Canonical correlation analysis explores the relationships between two multivariate sets of variables (vectors), all measured on the same individual. Consider, as an example, variables related to exercise and health.
Jun 29, 2021 · Canonical Correlation Analysis can be used to model the correlations between two datasets in two ways: Focusing on a dependence relationship, and model the two datasets in a regression-like manner: data set y as a function of data set x.
Learn how to use canonical correlation analysis to explore the relationships between two multivariate sets of variables measured on the same individual. See examples from exercise and health, environmental health and toxins, and sales performance and aptitude data.
Canonical Correlation Analysis (CCA) connects two sets of variables by finding linear combinations of variables that maximally correlate. There are two typical purposes of CCA: Data reduction: explain covariation between two sets of variables using small number of linear combinations.
Canonical correlation analysis is used to identify and measure the associations among two sets of variables. Canonical correlation is appropriate in the same situations where multiple regression would be, but where are there are multiple intercorrelated outcome variables.
