Multivariate analysis (
MVA) is based on the principles of multivariate statistics. Typically, MVA is used to address situations where multiple measurements are made on each experimental unit and the relations among these measurements and their structures are important. A modern, overlapping categorization of MVA includes: •
Principal components analysis (PCA) creates a new set of
orthogonal variables that contain the same information as the original set. It rotates the axes of variation to give a new set of orthogonal axes, ordered so that they summarize decreasing proportions of the variation. •
Factor analysis is similar to PCA but allows the user to extract a specified number of synthetic variables, fewer than the original set, leaving the remaining unexplained variation as error. The extracted variables are known as latent variables or factors; each one may be supposed to account for covariation in a group of observed variables. •
Canonical correlation analysis finds linear relationships among two sets of variables; it is the generalised (i.e. canonical) version of bivariate correlation. • Redundancy analysis (RDA) is similar to canonical correlation analysis but allows the user to derive a specified number of synthetic variables from one set of (independent) variables that explain as much variance as possible in another (independent) set. It is a multivariate analogue of
regression. •
Correspondence analysis (CA), or reciprocal averaging, finds (like PCA) a set of synthetic variables that summarise the original set. The underlying model assumes chi-squared dissimilarities among records (cases). •
Canonical (or "constrained") correspondence analysis (CCA) for summarising the joint variation in two sets of variables (like redundancy analysis); combination of correspondence analysis and multivariate regression analysis. The underlying model assumes chi-squared dissimilarities among records (cases). •
Multidimensional scaling comprises various algorithms to determine a set of synthetic variables that best represent the pairwise distances between records. The original method is
principal coordinates analysis (PCoA; based on PCA). •
Discriminant analysis, or canonical variate analysis, attempts to establish whether a set of variables can be used to distinguish between two or more groups of cases. •
Linear discriminant analysis (LDA) computes a linear predictor from two sets of normally distributed data to allow for classification of new observations. •
Clustering systems assign objects into groups (called clusters) so that objects (cases) from the same cluster are more similar to each other than objects from different clusters. •
Recursive partitioning creates a decision tree that attempts to correctly classify members of the population based on a dichotomous dependent variable. •
Artificial neural networks extend regression and clustering methods to non-linear multivariate models. •
Statistical graphics such as tours,
parallel coordinate plots, scatterplot matrices can be used to explore multivariate data. •
Simultaneous equations models involve more than one regression equation, with different dependent variables, estimated together. •
Vector autoregression involves simultaneous regressions of various
time series variables on their own and each other's lagged values. •
Principal response curves analysis (PRC) is a method based on RDA that allows the user to focus on treatment effects over time by correcting for changes in control treatments over time. •
Iconography of correlations consists in replacing a correlation matrix by a diagram where the "remarkable" correlations are represented by a solid line (positive correlation), or a dotted line (negative correlation).
Dealing with incomplete data It is very common that in an experimentally acquired set of data the values of some components of a given data point are
missing. Rather than discarding the whole data point, it is common to "fill in" values for the missing components, a process called "
imputation". ==Important probability distributions==