Multivariate statistics or multivariate statistical analysis in statistics describes a collection of procedures which involve observation and analysis of more than one statistical variable at a time.
There are many different models, each with its own type of analysis:
Canonical correlation analysis tries to establish whether or not there are linear relationships among two sets of variables (covariates and response).
Regression analysis attempts to determine a linear formula that can describe how some variables respond to changes in others .
Principal components analysis attempts to determine a smaller set of synthetic variables that could explain the original set.
Discriminant function or canonical variate analysis attempt to establish whether a set of variables can be used to distinguish between two or more groups.
Principal coordinate analysis attempts to determine a set of synthetic variables that best preserves the distance relationships between records.
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 to perform a regression analysis to estimate and test the influence of covariates on a binary response.
