Principal Component Analysis
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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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Signal versus noise Description and inference That is statistics

[latexpage] In ordinary regression the model is: $ Y = \beta_0 + \beta_1x_1 + \beta_2_x_2 + .... +...

When learning statistics, you may learn about ANOVA (analysis of variance), ANCOVA (analysis of covariance)...

Regression to the mean is a well known statistical artifact affecting correlated data that is not perfectly...

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

On this site I have written quite a lot about regression analysis.
But what is...

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

Question: How does ridge regression work? My answer: OLS models are BLUE  best linear unbiased...

At first, this seems quite simple. Weigh yourself Monday. Weigh yourself next Monday. Did your weight go...

This is a paper for NESUG (NorthEast SAS Users' Group) 2010, which you can see as a PDF articleNESUG2010.

If you picture the data as a 2 x 2 crosstab, then quasicomplete separation occurs when one of the cells is...

A young statistician name Myers Says tenure is all he desires But his dreams won't be met, He'll be fired,...