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.
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When dealing with ordinal data, many methods require you to assign a number or score to each level of a...

Question: A disease is present in 5 out of 100 people, and a test that is 90% accurate is administered to 100...

[latexpage] On Quora, someone asked about hypothesis tests for skewness and kurtosis. I wrote an answer...

Cluster analysis is a set of methods for finding subjects (people, corporations, drugs, whatever) that "go...

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

When it comes to measures of central tendency or location, the arithmetic mean and the median get a lot of...

Question: Does the butterfly effect imply that correlation implies causation? My answer: No. The butterfly...

Most generally, a dependent variable (DV) is something which we think depends on one or more independent...

Regression refers to a collection of techniques for modeling one variable (the dependent variable or DV), as a...

[latexpage] The chisquare test can refer to several different types of tests. Here I will discuss the...

I got into a conversation on Twitter (find me there as @peterflomstat) about the userfriendliness of...

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