Principal Components and Exploratory Factor Analysis

Principal Component Analysis (PCA) is a data-reduction technique. You use it as an intermediate step in a more complex analytical session. Imagine that you need to use hundreds of input variables, which can be correlated. With PCA, you convert a collection of possibly correlated variables into a new collection of linearly uncorrelated variables called principal components. The transformation is defined in such a way that the first principal component has the largest possible dataset overall variance, and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to (uncorrelated with) the preceding components. Principal components are ...

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