Multivariate Procedures (I) in NCSS
Introduction
Multivariate analysis refers to a group of statistical techniques that analyze two or more variables at a time, including: discriminant analysis, factor analysis, cluster analysis, logistic regression, MANOVA, and principal component analysis.
Features
Discriminant Analysis
> Canonical coefficients
> Classification reports
> Influential variable reports
> Linear discriminant functions, scores, and coefficients
> Prior probabilities
> Stepwise variable selection
Cluster Algorithms
> Complete linkage
> Dendrograms
> Fuzzy clustering
> Hierarchical
> K-means algorithm
> Medoid partitioning
> Nearest neighbor
> Regression clustering
> Single linkage
Factor and Principal Component Analysis
> Bartlett’s sphericity test
> Communality estimates
> Correlation matrix input
> Eigenvalue/vector analysis
> Factor loadings and scores
> Gleason-Staelin redundancy
> Missing value estimation
> Outlier detection
> Principal axis method
> Quartimax rotation
> Robust estimation
> Score and loading plots
> Scree plot
> T2 analysis
> Varimax rotation |
Equality of Covariance
> Bartlett’s variance test
> Box’s M test
> Cronbach’s alpha
> Eigenvalue analysis
> Maximum likelihood
MANOVA
> Approximate F-test
> Box’s M test
> Canonical analysis
> Homogeneity of variance test
> Hotelling’s T2
> Lawley-Hotelling trace
> Pillai’s trace
> Roy’s largest root
> Wilks’ lambda
Logistic Regression
> Beta estimates
> Chi-square tests
> Classification table
> Deviance statistics
> Likelihood ratio tests
> Multiple-group dependent variable
> Normal probability plots
> Odds ratios
> Predict new observations
> Residual diagnostics
> Step-down variable selection
> Step-up variable selectionClick here to see more Multivariate procedures available in NCSS. |
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