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PCA in JMP is a Course


Ended Mar 20, 2019
2 credits

Spots remaining: 14

Full course description

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. Attend this course on [date] from [time] in [location]. 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: 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 Networked Learning Initiatives (NLI).