JMP®: Analyzing and Modeling Multidimensional Data

Methods for unsupervised learning are presented in which relationships between the observations, as well as relationships between the variables, are uncovered. The course also demonstrates various ways of performing supervised learning where the relationships among both the output variables and the input variables are considered. In the course, emphasis is on understanding the results of the analysis and presenting conclusions with graphs.

Learn how to

• Use principal component analysis to reduce the number of data dimensions
• Use loading plots to understand the relationships between variables
• Interpret principal component scores and perform factor analysis
• Build more stable models by removing collinearity with principal component regression (PCR)
• Identify natural groupings in the data via cluster analysis
• Identify clusters of variables
• Classify observations into groups with discriminant analysis
• Fit complex multivariate predictive models with partial least squares (PLS) regression models.

Duration: 4 half-day sessions

Visit the course overview to learn more and register for an upcoming session.