Principal Components Analysis in JMP
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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: https://itpals.vt.edu/index/softwarelicensingcenter.html 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).