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Principal Components Analysis in JMP

Ended Dec 1, 2021
2 credits

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Full course description

Term: Fall 2021

Date: December 1st, 2021

Time: 5:00pm - 7:00pm

Location: Online Only

Instructors: Frances McCarty, Jennifer Van Mullekom, & Katherine Miller

Presented By: Statistical Applications & Innovations Group (SAIG)



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. In this short course, we will cover:

• Understanding what scenarios PCA can be useful for.

• Understanding what PCA is.

• Learning to apply PCA transformation in JMP.

• Interpreting PCA for dimension reduction / what not to do.

• Using PCA for robust modeling.

 No previous experience with JMP is required! Bring your laptop with JMP installed on your machine. You can download JMP from the Virginia Tech Software Service Center from the following links: to an external site. If you already have JMP on your laptop, make sure it’s the most up to date versions. This course is jointly sponsored by the Statistical Application and Innovations Group (SAIG) and Professional Development Network (PDN).