Factor Analysis reduces the information in a model by reducing the dimensions of the observations. This procedure has multiple purposes. It can be used to simplify the data, for example reducing the number of variables in predictive regression models. If factor analysis is used for these purposes, most often factors are rotated after extraction. Factor analysis has several different rotation methods—some of them ensure that the factors are orthogonal. Then the correlation coefficient between two factors is zero, which eliminates problems of multicollinearity in regression analysis?
- Badges
- Science topic
- Similar topics
- Philosophy
- Philosophy of Mathematics
- Informality
More Muawya Ahmed Hussein's questions See All
Similar questions and discussions