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Real-world data can be highly multivariate, and students in statistics, data science, and the physical and social sciences should be equipped to analyze such data with appropriate multivariate analysis methods. JMP statistical software can make multivariate methods more accessible and engaging to students through its interactive, no-code interface.

This webinar presents JMP tools and tips for teaching two common and widely applicable multivariate methods: clustering and principle components analysis (PCA). Topics include:

• Overview of unsupervised learning methods in JMP
• Exploring and understanding multicollinearity
• Teaching examples of PCA, hierarchical clustering, and k-means clustering
• Relevant teaching tips and resources from the JMP Academic team