Principal Component Analysis
HomePrincipal Component Analysis
Principal Component Analysis
Principal component analysis is a technique for reducing the dimensions of a set of data. Unlike factor analysis, it is not designed to uncover latent traits, but simply to reduce the number of variables while retaining as much of the variance in the original data as possible.
Featured Posts

Are you writing a dissertation? Congratulations! You've gotten through undergraduate education, graduate...

Macros can be a very complex topic, but some very simple macros can make life easier for a data analyst or...

Sir Ronald Fisher famously said:
"To call in the statistician after the experiment is done may be...

Stevens (Stevens 1946) divided types of variables into four categories, and these have become entrenched in...

When we have quantitative data, one thing we often want to know is where the center is, and, for that, we can...

Question: How can any stochastic phenomenon follow a continuous distribution, since the probability of...

Regression is a set of statistical techniques for relating a dependent variable to one or more independent...

If you often have regression problems in which you have a great many independent variables, partial least...

Today, we think of probability as an intensively mathematical subject. But the mathematics took a long to...

In a recent article in Sociological Methodology entitled "How to impute interactions, squares, and other...

This is a talk that I gave at NDRI. I also gave a version of this talk at Yale and at BrainScope

The average, or mean, is one of the simplest statistics there is. You have a bunch of numbers, you add them...