Multivariate Statistical Analysis

A Multivariate Statistical Analysis is a multivariate analysis that is a statistical analysis.

• AKA: MANOVA.
• Example(s):
  • Multivariate Hypothesis Testing.
  • …
• Counter-Example(s):
  • Univariate Statistical Analysis.
• See: Multivariate Statistical Model, Statistical Inference, Multiple Regression, Multivariate Normal Distribution, Multinomial Trial.

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References

2014

• (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/multivariate_statistics Retrieved:2014-10-21.
  • Multivariate statistics is a form of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. The application of multivariate statistics is multivariate analysis.

    Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical implementation of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the actual problem being studied.

    In addition, multivariate statistics is concerned with multivariate probability distributions, in terms of both:

     :*how these can be used to represent the distributions of observed data;

     :*how they can be used as part of statistical inference, particularly where several different quantities are of interest to the same analysis.

    Certain types of problem involving multivariate data, for example simple linear regression and multiple regression, are NOT usually considered as special cases of multivariate statistics because the analysis is dealt with by considering the (univariate) conditional distribution of a single outcome variable given the other variables.

2014

• http://www.multivariatestatistics.org/ Retrieved: 2017-10-26.
  • QUOTE: Multivariate analysis is the area of statistics that deals with observations made on many variables. The main objective is to study how the variables are related to one another, and how they work in combination to distinguish between the cases on which the observations are made.

    The analysis of multivariate data permeates every research discipline: biology, medicine, environmental science, sociology, economics, education, linguistics, archaeology, anthropology, psychology and behavioural science, to name a few, and has even been applied in philosophy. All natural and physical processes are essentially multivariate in nature — the challenge is to understand the process in a multivariate way, where variables are connected and their relationships understood, as opposed to a bunch of univariate processes, i.e. single variables at a time, isolated from one another.

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