Using Multivariate Methods to Explore Data

See how to:    

• Understand and use Multivariate Methods (PCA, Clustering and K-Means)
• Create and interpret multivariate Scatter Plots and Correlations
• Group records that have a large number of variables into smaller clusters of new variables based on common characteristics
• Cluster rows that share similar values across a number of variables
• Use Hierarchical Clustering to create a dendrogram, which is a dynamic, responding tree graph for small tables up to several thousand rows
• Use k-Means Clustering for table, up to hundreds of thousands of rows
• Use Self- Organizing Maps to from clusters and form form them in a particular layout on a 2- dimensional cluster grid where proximity in the gird indicates cluster similarity
• Perform Principal Components Analysis (PCA) for continuous factors, where the first principal component is the linear combination of the standardized original variables that has the greatest possible variance. and each subsequent principal component is the linear combination of the variables that has the greatest possible variance and is uncorrelated with all previously defined components
• Analyze Multiple Correspondence for categorical data (similar to PCA, but for categorical data)
• Use  Factor Analysis to identify latent factors

Resources

• JMP® for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists... by Ann Lehman, Norm O'Rourke, Larry Hatcher, Edward J. Stepanski
• Visual Six Sigma: Making Data Analysis Lean, Second Edition by Ian Cox, Marie Gaudard, Mia L. Stephens