Connect the Dots: Factor Analysis

Explore mean and median as central measures, and range as a spread indicator affected by outliers; learn variance, its standard deviation, and the n-1 denominator for sample estimates.

Learn how principal components analysis transforms correlated variables into orthogonal components by maximizing variance, using eigenvectors and eigenvalues from eigen decomposition for regression.

Understand PCA results: principal components are orthogonal and ordered by variance, with eigenvalues showing each component’s share of variance. Use the scree plot elbow to decide which components to keep.

Learn how to perform eigen decomposition in Excel using VBA to compute principal components, eigenvalues, and eigenvectors from the covariance matrix, and assess variance explained to select key components.

Import a csv of securities, parse dates, and build a frame; compute returns by dividing by the previous day and subtracting one, then standardize with scale for principal components analysis.

Explore principal components analysis in Python by standardizing data, computing covariance and correlation matrices, deriving eigenvectors for the first three components, and applying linear regression assessed by R-squared.