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.
Details
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.