Everything about Multivariate Statistics totally explained
The word
Multivariate, is defined as: "having or involving a number of independent mathematical or statistical variables".
Multivariate statistics or
multivariate analysis in
statistics describes a collection of procedures which involve observation and
analysis of more than one
statistical variable at a time.
Sometimes a distinction is made between univariate (for example,
ANOVA,
t-tests) and multivariate statistics.
There are many different models, each with its own type of analysis:
- Hotelling's T-square is a generalization of Student's t statistic that's used in multivariate hypothesis testing.
- Multivariate analysis of variance (MANOVA) methods extend analysis of variance methods to cover cases where there's more than one dependent variable and where the dependent variables can't simply be combined.
- Discriminant function or canonical variate analysis attempt to establish whether a set of variables can be used to distinguish between two or more groups.
- Regression analysis attempts to determine a linear formula that can describe how some variables respond to changes in others. Regression analyses are based on forms of the general linear model.
- Principal components analysis attempts to determine a smaller set of synthetic variables that could explain the original set.
- Linear discriminant analysis (LDA) computes a linear predictor from two sets of normally distributed data to allow for classification of new observations.
- Logistic regression allows regression analysis to estimate and test the influence of covariates on a binary response.
- Artificial neural networks extend regression methods to non-linear multivariate models.
- Multidimensional scaling covers various algorithms to determine a set of synthetic variables that best represent the pairwise distances between records. The original method is principal coordinate analysis.
- Canonical correlation analysis tries to establish whether or not there are linear relationships among two sets of variables (covariates and response).
- Recursive partitioning creates a decision tree that strives to correctly classify members of the population based on a dichotomous dependent variable
Further Information
Get more info on 'Multivariate Statistics'.
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