Standardising Variables: z-Scores before PCA
This lesson is part of the course
In this lesson
You compute the mean and sample standard deviation of four city indicators, turn every value into a z-score, count the unusual cities and see why unscaled data would let one variable dominate.
Buy the course once for $9.99 to unlock every lesson and lab, the final exam and the certificate – lifetime access, no subscription.
Lessons in this course
- Standardising Variables: z-Scores before PCA Locked
- Covariance & Correlation Matrices with MMULT and TRANSPOSE Locked
- Power Iteration: the First Eigenvector with Repeated MMULT Locked
- Eigenvalue & Explained Variance: How Much Does PC1 Capture? Locked
- The Second Component: Deflation and Another Power Iteration Locked
- Scree Plot & Cumulative Variance: How Many Components? (with a Chart) Locked
- Component Scores & Loadings: Placing and Naming the Cities (with a Chart) Locked
- Reconstruction Error: What You Lose with k Components Locked