PCA calculator — principal component analysis
Paste two or three series with equal observation counts. The calculator derives principal components from a correlation or covariance matrix.
Scree chart
Bars show the share of total variance explained by each component.
Components and loadings
| Component | Eigenvalue | Variance share | Loading A | Loading B | Loading C |
|---|---|---|---|---|---|
| Fill in the fields above — the result is computed on this page. | |||||
How do I use this page?
- Paste equal-length series.
- Choose scaling suitable for the data.
- Read the explained variance and loadings.
What this page does not do: The tool supports 2–3 variables and 3–5000 complete observations. It does not impute missing values or choose the final component count.
Further reading
Theory — what is principal component analysis?
Principal component analysis (PCA) rotates the coordinate system so the first component captures as much of the data's variance as possible, while the second captures as much of the remaining variance as possible while staying perpendicular to the first. The calculator needs two or three series with equal observation counts and derives components from a correlation or covariance matrix. Standardising the data is advisable when variables have different units or clearly different variances, since otherwise the variable with the largest scale can dominate the result.
Frequently asked questions
When should I standardise?
Does a loading sign matter?
See also
A/B test calculator
Compare two proportions in an A/B test. Calculate conversion rates, difference, uplift, z score, p value and c
A/B test planning — sample size, MDE and duration
Plan a two-proportion experiment, estimate detectable effect and traffic duration.
ACF and PACF autocorrelation calculator
Calculate ACF and PACF across time-series lags. Review the chart, reference bounds and coefficient table.