Historical View
In 1889 Galton gave us the normal distribution, this statistical methods used in traditional, established the correlation coefficient and linear regression. In thirty s of 20th century Fisher proposed analysis of variance and discriminant analysis, SS Wilkes developed the multivariate analysis of variance, and H. Hotelling determined principal component analysis and canonical correlation. Generally, in the first half of the 20th century, most of the theory of multivariate analysis has been established. 60 years later, with the development of computer science, psychology, and multivariate analysis methods in the study of many other disciplines have been more widely used. Programs like SAS and SPSS, once restricted to mainframe utilization, are now readily available in Windows-based.The marketing research analyst now has access to a much broader array of sophisticated techniques in which to explore the data. The challenge becomes knowing which technique to select, and clearly understanding its strengths and weaknesses.
Multivariate statistical analysis is the use of mathematical statistics methods to study and solve the problem of multi-index theory and methods. The past 20 years, with the computer application technology and the urgent need for research and production, multivariate statistical analysis techniques are widely used in geology, meteorology, hydrology, medicine, industry, agriculture and economic and many other fields, has become to solve practical problems in effective way. Simplified system architecture to explore the system kernel, can use principal component analysis, factor analysis, correspondence analysis and other methods, a number of factors in each variable to find the best subset of information from a subset of the description contained in the results of multivariable systems and the impact of various factors on the system.
In multivariate analysis, controlling for the prediction of the model has two categories. One is the prediction model, often using multiple linear regression, stepwise regression analysis, discriminant analysis or stepwise regression analysis of double screening modeling. The other is a descriptive model, commonly used cluster analysis modeling techniques. In multivariate analysis system, the system requires a similar nature of things or phenomena grouped together, to identify the links between them and the inherent regularity, many previous studies are mostly
qualitative treatment by a single factor, so the results do not reflect the general characteristics of the system. For example numerical classification, general classification model constructed using cluster analysis and discriminant analysis techniques.