Analysis of the data structure

Minitab offers two analyses to evaluate the covariance structure of your data:
Principal Components Analysis
Principal components analysis helps you to understand the covariance structure in the original variables and/or to create a smaller number of variables using this structure. In Minitab, choose Stat > Multivariate > Principal Components.
Factor Analysis
Like principal components, factor analysis summarizes the covariance structure of the data in a smaller number of dimensions. The emphasis in factor analysis is the identification of underlying "factors" that might explain the dimensions associated with large data variability. In Minitab, choose Stat > Multivariate > Factor Analysis.

Internal consistency

Item Analysis
An item analysis evaluates how consistently multiple items in a survey or test measure the same construct. In Minitab, choose Stat > Multivariate > Item Analysis.

Grouping observations

Minitab offers cluster analysis methods and discriminant analysis for grouping observations or variables:
Cluster Observations
A cluster observations analysis groups observations that are "close" to each other when the groups are initially unknown. This analysis is a good choice when no outside information about grouping exists. The choice of final grouping is usually made by what is logical for your data after viewing clustering statistics. In Minitab, choose Stat > Multivariate > Cluster Observations.
Cluster Variables
A cluster variables analysis groups variables that are "close" to each other when the groups are initially unknown. You might want to cluster variables to reduce their number and simplify your data. The method used to cluster variables is similar to that used to cluster observations. . In Minitab, choose Stat > Multivariate > Cluster Variables.
Cluster K-Means
A cluster K-means analysis groups observations that are "close" to each other. K-means clustering works best when enough information is available to make good initial cluster designations. In Minitab, choose Stat > Multivariate > Cluster K-Means.
Discriminant Analysis
Discriminant analysis classifies observations into two or more groups if you have a sample with known groups. You can use discriminant analysis to investigate how the predictors contribute to the groupings. In Minitab, choose Stat > Multivariate > Discriminant Analysis.

Correspondence analysis

Minitab offers two methods of correspondence analysis to explore the relationships between categorical variables:
Simple Correspondence Analysis
Simple correspondence analysis explores relationships in a 2-way classification. You can also use this analysis with 3-way and 4-way tables because Minitab can collapse them into 2-way tables. Simple correspondence analysis decomposes a contingency table similar to how principal components analysis decomposes multivariate continuous data. Simple correspondence analysis conducts an eigen analysis of data, breaks down variability into underlying dimensions, and associates variability with rows and/or columns. In Minitab, choose Stat > Multivariate > Simple Correspondence Analysis.
Multiple Correspondence Analysis
Multiple correspondence analysis extends simple correspondence analysis to the case of 3 or more categorical variables. Multiple correspondence analysis performs a simple correspondence analysis on an indicator variables matrix in which each column corresponds to a level of a categorical variable. Instead of a 2-way table, the multi-way table is collapsed into 1 dimension. In Minitab, choose Stat > Multivariate > Multiple Correspondence Analysis.
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