Please note: this course will take place in hotel Hilton in Amsterdam.
Learn how to
This course is for JMP users who deal with data with many variables. The course demonstrates various ways to examine high dimensional data in fewer dimensions, as well as patterns that exist in the 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. Strong emphasis is on understanding the results of the analysis and presenting your conclusions with graphs.
- use principal components 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 components regression (PCR)
- identify natural groupings in the data via cluster analysis
- classify observations into groups with discriminant analysis
- fit complex multivariate predictive models with partial least squares (PLS) regression models.
Who should attend
Individuals who work with high dimensional data and have a need to identify patterns or groups in the data or have a need to build models to predict response outcome(s) or group assignments
Before attending this course, you should complete the JMP® Software: Statistical Decisions Using ANOVA and Regression course.
This course addresses JMP software.