Use Principal Components Analysis to identify a smaller number of uncorrelated variables, called "principal components", from a large set of data. With this analysis, you create new variables (principal components) that are linear combinations of the observed variables. The goal of principal components analysis is to explain the maximum amount of variance with the fewest number of principal components.
For example, an analyst uses a principal components analysis to analyze customer responses to several characteristics of a new shampoo. The analyst wants to determine whether they can form a smaller number of uncorrelated variables that are easier to interpret and analyze than the observed variables that they measured.
Principal components analysis is commonly used as one step in a series of analyses. For example, you can use principal components before you perform a regression analysis, in order to avoid multicollinearity or to reduce the number of predictors relative to the number of observations.
To perform a principal components analysis, choose.
To model each observed variable as a linear functions of factors, use Factor Analysis. Factor analysis describes the covariance among variables in terms of a few unobservable (latent) factors.