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Principal Components Analysis in JMP

Course Date: 4/6/2022

Who This Course Is For

Often researchers face the challenge of having data that includes many correlated variables. PCA is a technique that is often used in these cases to aid in interpreting the observations and variables. The results of PCA can often be used in ANOVA and regression models.



Duration:  2 hours

Course Dates: 4/6/2022

Venue: Virtual 

Required Software:  JMP software

Cost: Free to VT Participating Colleges and Administrative Units

Pre-requisites:  No previous coding experience is required.  Knowledge of basic descriptive statistics is assumed.

Prework includes downloading course materials and JMP software.

By the end of this course you will be able to:


  • Understand what scenarios PCA can be useful for.
  • Understand what PCA is.
  • Learn to apply PCA transformation in JMP.
  • Interpret PCA for dimension reduction / what not to do.
  • Use PCA for robust modeling.


This is a hands-on, interactive course where you will submit pre-written code followed by exercises where you write your own code based on examples.  You will leave with code that you can apply to your datasets.