Multivariate Analysis

Use VisualStat's multivariate analysis procedures to analyze your data when you have made multiple measurements on items or subjects. You can choose a method depending on whether you want to analyze the data structure or assign observations to groups.

 

Principal Component Analysis finds a smaller set of synthetic variables that capture the variance in an original data set. The first principal component accounts for as much of the variability in the data as possible, and each succeeding orthogonal component accounts for as much of the remaining variability as possible.

Correspondence Analysis decomposes a contingency table in a manner similar to how principal components analysis decomposes multivariate continuous data. An eigen analysis of the data is performed, and the variability is broken down into underlying dimensions and associated with rows and/or columns.

Hierarchical Cluster Analysis detects natural groupings in data. In hierarchical cluster analysis, each object is initially assigned to its own singleton cluster. The analysis then proceeds iteratively, at each stage joining the two most similar clusters into a new cluster, continuing until there is one overall cluster.