# Using Multivariate Methods to Explore Data

Presenter: Laura Higgins

#### Understanding and Using Multivariate Methods  (PCA, Clustering and K-Means)

See how to:

• Determine common variation and meaningful groups of variables
• Group records that have a large number of variables into smaller clusters of new variables based on common characteristics
• Interpret correlations between variable pairs using Scatterplot Matrices, Color Maps on Correlations and Parallel Coordinate Plots
• Use Local Data Filter to examine correlations for different categories
• Standardize variables for analysis by putting them on same scale
• Find shared and uncorrelated variation among variables
• Interpret Eigen Values to determine how many components to examine
• Interpret Eigen Vectors, Partial Contribution of Variables, and Bartlett Tests to determine what new components mean
• Save Principal Component values to data table for further exploration and analysis
• Use K-Means Clustering to group observations that share similar values across a number of continuous variables
• Interpret K-Means Biplots, Cluster Summaries, Cluster Means and Parallel Coordinate Plots
• Color points by cluster
• Save cluster formulas to data table so clusters can be updated when new data is added
• Determine when to use Johnson Transformation to mitigate skewness
• Use Hierarchical Clustering to group observations that share similar values across a number of categorical  or continuous variables
• Interpret Hierarchical Cluster Dendrograms. Cluster Summaries, Cluster Means and Constellation Plots