Multivariate statistics aims to provide insights into the relationships and patterns among multiple variables in a dataset.

Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction, which transforms a set of correlated variables into a smaller set of uncorrelated variables called principal components.

Cluster analysis aims to identify natural groupings or clusters of data points based on their similarities, helping to uncover patterns and structures in the data.

Discriminant Analysis is a multivariate technique used to classify observations into different groups based on a set of predictor variables, maximizing the separation between groups.

Canonical correlation analysis (CCA) is a multivariate technique that identifies linear relationships between two sets of variables, maximizing the correlation between the linear combinations of variables in each set.

Multiple Regression Analysis is a multivariate technique used to model the relationship between a dependent variable and multiple independent variables, estimating the coefficients that best fit the data.

Structural equation modeling (SEM) is a multivariate technique that aims to test and validate causal relationships among variables, incorporating both observed and latent variables into the model.

Factor Analysis is a multivariate technique that aims to identify a smaller number of latent factors that explain the covariance structure among a larger set of observed variables.

Multidimensional scaling (MDS) is a multivariate technique used to represent high-dimensional data in a lower-dimensional space, preserving the distances or similarities between data points.

Bayesian Networks are a multivariate technique used to represent and analyze the dependence structure among variables, allowing for the representation of causal relationships and probabilistic dependencies.

Discriminant function analysis (DFA) is a multivariate technique used to identify linear combinations of variables that best discriminate between two or more groups, maximizing the separation between groups.

Logistic Regression is a multivariate technique used to analyze the relationship between a set of variables and a categorical outcome variable, estimating the probability of the outcome occurring based on the values of the predictor variables.

Partial least squares (PLS) regression is a multivariate technique used to make predictions and classifications by identifying linear relationships between variables and a response variable, even in the presence of collinearity among the predictor variables.

Multiple Regression Analysis is a multivariate technique used to analyze the relationship between a set of variables and a continuous outcome variable, estimating the coefficients that best fit the data.

Canonical variate analysis (CVA) is a multivariate technique used to identify linear combinations of variables that best discriminate between two or more groups, maximizing the separation between groups.