Gnumeric · Scientific analysis · Advanced

Principal Component Analysis (PCA) in Gnumeric

Prepare numeric input for Gnumeric PCA, interpret eigenvalues and loadings, and avoid scale-related analysis mistakes.

Official documentation reviewed; native application verification pending · Updated 10 October 2026 · Editorial standards

Principal component analysis (PCA) summarizes variation across several numeric variables. Its output is commonly described using eigenvalues, eigenvectors (loadings), and the proportion of variation represented by each component.

Build a small multivariable dataset

Open paired variables (CSV). It contains three columns named X, Y, and Z across eight observations. Y is exactly 2×X+1, while Z has a different pattern.

This deliberately includes redundant information between X and Y. You should expect at least one component to reflect their shared direction of variation, but the precise eigenvalues depend on the selected data matrix and the tool’s treatment of scaling.

Run the PCA tool

Ensure each feature is a numeric column and each row represents one observation.
Find the Principal Component Analysis tool in Gnumeric’s statistical-analysis tools.
Enter the full data range and group factors by columns.
Enable Labels if your input range includes X, Y and Z in the first row.
Output the covariance/eigenvalue results to a new sheet and examine the component with the largest reported share of variation.

The official documentation explicitly describes PCA output containing a covariance matrix plus eigenvalues and eigenvectors.

Interpret with care

Large numerical scales dominate covariance PCA. A variable measured in thousands can influence results more than one measured in fractions. Standardize variables first if your analysis requires equal weighting; check whether your tool provides a scaling option.
Loadings can change sign without changing the underlying component, so do not interpret a sign flip as a different PCA solution.
Missing or nonnumeric cells can change which observations are analyzed.
Correlation isn’t causation: PCA extracts variance directions, not causal explanations.

Before PCA, you may want a correlation matrix and descriptive statistics.

Verification note: This is an official-documentation-based workflow; no native Gnumeric PCA output is being represented as tested.

Official source: Application handbook or manual. This guide is an editorial draft prepared for staging; exact menus and outputs must be checked in the target software/version before public release.