1. Principal component analysis: transform a group of possibly related variables into a group of linearly unrelated variables through orthogonal transformation, and the transformed variables.

2. The nature of factor analysis: the statistical technique of extracting * * * gender factors from variable groups is studied.

Second, the application is different.

1. Application of principal component analysis: It is a commonly used multivariate analysis method, such as demography, quantitative geography, molecular dynamics simulation, mathematical modeling, mathematical analysis, etc. ?

2, factor analysis application:

(1) consumer habits and attitudes research (u&; answer

(2) Research on brand image and characteristics.

(3) service quality survey

(4) Personality test

(5) Image survey

(6) Market segmentation and identification

(7) Classification of customers, products and behaviors

Extended data:

The principle of principal component analysis is to recombine the original variables into a new set of irrelevant comprehensive variables as far as possible, and at the same time, according to the actual needs, take as few summation variables as possible to reflect the information of the original variables.

This statistical method is called principal component analysis or principal component analysis, and it is also a mathematical method to deal with dimensionality reduction. Principal component analysis (PCA) is an attempt to replace the original indicators with a new set of irrelevant comprehensive indicators.

Factor analysis is a powerful tool for social research, but how many factors are uncertain in a study. When the variables selected in the study change, the number of factors will also change. In addition, the explanation of the actual meaning of each factor is not absolute.

Baidu Encyclopedia-Principal Component Analysis

Baidu Encyclopedia-Factor Analysis