# Introduction To Multivariate Analysis

```@inproceedings{Chatfield1980IntroductionTM,
title={Introduction To Multivariate Analysis},
author={Chris Chatfield and Alexander J. Collins},
year={1980}
}```
• Published 1 December 1981
Part One. Multivariate distributions. Preliminary data analysis. Part Two: Finding new underlying variables. Principal component analysis. Factor analysis. Part Three: Procedures based on the multivariate normal distribution. The multivariate normal distribution. Procedures based on normal distribution theory. The multivariate analysis of variance. The multivariate analysis of covariance and related topics. Part Four: Multi-dimensional scaling and cluster analysis. Multi-dimensional scaling…
157 Citations
Distance-based Multivariate Two Sample Tests
• Mathematics
• 2004
Multivariate statistical inference is synonymous with multivariate normal distribution. Most multivariate models and tests are based on the Gaussian hypothesis. But often the variables are not
Principal component analysis for grouped data—a case study
• Mathematics
• 1999
Two of the most popular descriptive multivariate methods currently employed are the principal component analysis and canonical variate analysis methods. Canonical variate analysis is the most
Interpretation of transformed axes in multivariate analysis
• Geology
• 1993
SUMMARY Several multivariate statistical techniques involve an orthogonal transformation to new axes. These lead to new co-ordinates of a configuration usually in a lower dimensional space. Often it
Approximation of power in multivariate analysis
• Mathematics
Stat. Comput.
• 2005
This paper presents simple yet extremely accurate saddlepoint approximations to power functions associated with the following classical test statistics: the likelihood ratio statistic for testing the general linear hypothesis in MANOVA; the likelihood ratios for testing block independence; and Bartlett's modified likelihood ratio statistics for testing equality of covariance matrices.
Introduction to Multivariate Statistical Procedures
The type of data to be studied is a deciding factor in all statistical methods and is very important in studies using multivariate statistics. Data may be classified as continuous or discrete, normal
Distribution of sample correlation coefficients
Abstract : Let (Y,X sub 1, ...,X sub K) be a random vector distributed according to a multivariate normal distribution, and let R sub i denote the sample correlation coefficient between Y and X sub
Independence distribution preserving joint covariance structures for the multivariate two-group case
• Mathematics
• 2001
We characterize the general nonnegative-definite and positive-definite joint observation covariance structures for the two-group case such that the two sample mean vectors are independent of the two
Asymptotic distribution of sample covariance determinant
Under normality, an asymptotic distribution of sample covariance determinant will be derived. We show that this asymptotic distribution is more applicable in practice than the classical one. This
Multivariate methods for index construction
This chapter demonstrates a range of situations where multivariate methods have a role to play in index construction and in initial stages of data exploration with specific subsets of the survey data, before further analysis is done to address specific survey objectives.