Difference in goals between pca and fa, Applied Statistics

In PCA the eigknvalues must ultimately account for all of the variance. There is no probability,'no hypothesis, no test because strictly speaking PCA is not a statistical procedure. The PCA is merely a mathematical manipulation to recast m variables as m factors. Factor analysis (FA), however, brings a priori knowledge to the problem solving exercise.

'There is a short list of primary assumptions behind factor analysis. But basically. factor analysis assumes that there are  cowelations/covariances between the m variables in the datp set that are a result of p underlying, mutually uncorrelated factors.

Posted Date: 4/4/2013 3:47:37 AM | Location : United States







Related Discussions:- Difference in goals between pca and fa, Assignment Help, Ask Question on Difference in goals between pca and fa, Get Answer, Expert's Help, Difference in goals between pca and fa Discussions

Write discussion on Difference in goals between pca and fa
Your posts are moderated
Related Questions
Grid is the set of pairs {1, 2, 3, 4} x {1, 2, 3, 4}. Image is the power set of Grid. An element of Image is a subset of Grid and can be represented by a diagram on a 4 by 4

The cornlnunalities h j represent the fraction of the total variance' 'accounted for of variabie j. Ry calculating the communalities we can keep track of how much of-the orig

Read the “JET Copies” Case Problem on pages 678-679 of the text. Using simulation estimate the loss of revenue due to copier breakdown for one year, as follows: 1. In Excel, use a

velocity of a particle which moves along the s-axis is given by v=2-4t+5t then find position velocity,acceleration

Admissibility A very common concept which is applicable to any procedure of the statistical inference. The underlying notion is that the procedure/method is admissible if and o


Binomial Distribution Binomial distribution  was discovered by swiss mathematician James  Bernonulli, so this distribution is called as Bernoulli distribution also, this is a d

objective of the testing stochastic regression


The Null Hypothesis - H0: β0 = 0, H0: β 1 = 0, H0: β 2 = 0, Β i = 0 The Alternative Hypothesis - H1: β0 ≠ 0, H0: β 1 ≠ 0, H0: β 2 ≠ 0, Β i ≠ 0      i =0, 1, 2, 3