Difference between revisions of "Principal Component Analysis (PCA)"
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== Read First == | == Read First == | ||
PCA, is a way to create an index from a group of variables that are similar in the information that they provide. This allows maximizing the information we keep, without using variables that will cause multicolinearity, and without having to choose one variables among many. | |||
== Guidelines == | == Guidelines == | ||
Revision as of 23:27, 7 February 2017
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Read First
PCA, is a way to create an index from a group of variables that are similar in the information that they provide. This allows maximizing the information we keep, without using variables that will cause multicolinearity, and without having to choose one variables among many.
Guidelines
- organize information on the topic into subsections. for each subsection, include a brief description / overview, with links to articles that provide details
Subsection 1
Subsection 2
Subsection 3
Back to Parent
This article is part of the topic Data Analysis
Additional Resources
- list here other articles related to this topic, with a brief description and link