# Factor Analysis

Investigate multidimensional data sets to reduce or establish a relationship with Principal Component Analysis (PCA) and Multiple Linear Regression (MLR)

Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved, interpretable , uncorrelated variables called factors.

Factor analysis is part of general linear model (GLM), and just as well, it make several assumptions: there is linear relationship, there is no multicollinearity, it includes relevant variables into analysis, and there is true correlation between variables and factors. Several methods are available, but principle component analysis is used most commonly.

### Generalized Linear Model (GLM)

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### Principal Components Analysis (PCA)

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### Principal Component Regression (PCR)

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### Multiple Linear Regression (MLR)

Factor analysis is a technique that is used to reduce a large number of variables into fewer numbers of factors.