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)

Generalized Linear Model (GLM)

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Egestas quis ipsum suspendisse ultrices gravida. Ultrices mi tempus imperdiet nulla. Enim sit amet venenatis urna cursus eget nunc. Lorem mollis aliquam ut porttitor leo a diam sollicitudin.

Principal Components Analysis (PCA)

Principal Components Analysis (PCA)

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Egestas quis ipsum suspendisse ultrices gravida. Ultrices mi tempus imperdiet nulla. Enim sit amet venenatis urna cursus eget nunc. Lorem mollis aliquam ut porttitor leo a diam sollicitudin.

Principal Component Regression (PCR)

Principal Component Regression (PCR)

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lacus sed viverra tellus in hac habitasse platea dictumst. Pellentesque elit ullamcorper dignissim cras. Imperdiet nulla malesuada pellentesque elit eget gravida cum sociis natoque. Sit amet consectetur adipiscing elit duis tristique.

Multiple Linear Regression (MLR)

Multiple Linear Regression (MLR)

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