★★★★★
"Super simple written"
Principal Component Analysis
Explore interactively how PCA finds the axes of maximum variance. Use the 2D view to drag points directly and the 3D view to rotate the data cloud.
PCA Variation
Drag the blue line to rotate the direction. The right view shows the points projected onto that line.
Drag the blue line to rotate the direction.
Points projected into one-dimensional space.
Variance explained by this line92.6%
PCA in 2D
Drag points to watch the principal components rotate. Shift+click adds a point, Alt+click removes one.
Drag points or reshape the cloud with sliders.
PC1
PC2
Mean
PC1 variance94.3%
PC2 variance5.7%
Mean(0.44, 0.32)
Points projected into PC1 (x) and PC2 (y).
PC1 axis
PC2 axis
PCA in 3D
Rotate the view to see how the principal axes align with the direction of maximum variance.
Drag to rotate. The axes show PC1, PC2, and PC3.
PC1
PC2
PC3
Points projected onto PC1 (x) and PC2 (y).
PC1 axis
PC2 axis
PC1 variance62.5%
PC2 variance35.4%
PC3 variance2.1%
Real World Example
Explore PCA on a real dataset from the UCI Wine study. See how the variables compress into a few principal components.
Wine Data from: Aeberhard, S. & Forina, M. (1992). Wine [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5PC7J.
Statistics made easy
- many illustrative examples
- ideal for exams and theses
- statistics made easy on 464 pages
- 8th revised edition (March 2026)
Only €8.99
★★★★★
"It could not be simpler"
★★★★★
"So many helpful examples"