Summary: This course is for JMP users who work with data that have 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. In the course, emphasis is on understanding the results of the analysis and presenting conclusions with graphs.
Duration: 14 hours of content.
Modalities:
- live online with instructor -- This course is available periodically in our public course schedule. The public courses are an opportunity to learn this content with a live instructor, but they are currently only offered in English and at times most convenient to a US audience (because most of our instructors are in US time zones). Don't see what you are looking for? Let us know.
- through a third-party training vendor -- Any course in our JMP Curriculum could be taught by a licensed training vendor, including through the training department at your own company. Contact your JMP representative to learn more.
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.
Learning Objectives:
- 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.
Course Outline:
Introduction to Multidimensional Data
- Multidimensional analysis
- Review of matrix algebra
Principal Component Analysis
- Interpretation of principal components
- Finding principal components
Principal Component Regression
- Principal component regression
Factor Analysis
- Factor extraction
- Factor rotation
Cluster Analysis
- Introduction to cluster analysis
- Hierarchical clustering
- K-means clustering
- Variable clustering
Discriminant Analysis
- Discrimination
- Classification
Partial Least Squares Regression
- PLS algorithms
- PLS reports
