# Principal components

Principal components are the linear combinations of the original variables.

## Variances

Variances of each principal component show how much of the original variation in the dataset is explained by the principal component.

When the data is standardized, a component with a variance of 1 indicates that the principal component accounts for the variation equivalent to one of the original variables. Also, the sum of all the variances is equal to the number of original variables.

## Coefficients

Coefficients are the linear combinations of the original variables that make up the principal component. The coefficients for each principal component can sometimes reveal the structure of the data. Absolute values near zero indicate that a variable contributes little to the component, whereas larger absolute values indicate variables that contribute more to the component.

Often, when the data is centered and standardized, the coefficients are normalized so that the sum of the squares of the coefficients of a component is equal to the variance of the component. In this normalization, the coefficients can be interpreted as the correlation between the original variable and the principal component, and are often called loadings (a term borrowed from factor analysis).

## Scores

Scores are new variables that are the value of the linear combination of the original variables. The scores are normalized so that the sum of squares equals the variance of the principal component.

**Available in Analyse-it Editions**

Standard edition

Method Validation edition

Quality Control & Improvement edition

Ultimate edition

- What is Analyse-it?
- Administrator's Guide
- User's Guide
- Statistical Reference Guide
- Distribution
- Compare groups
- Compare pairs
- Contingency tables
- Correlation and association
- Principal component analysis (PCA)
- Principal components
- Scree plot
- Calculating principal components
- Biplot
- Monoplot
- Creating a biplot
- Creating a correlation monoplot
- Factor analysis (FA)
- Item reliability
- Fit model
- Method comparison
- Measurement systems analysis (MSA)
- Reference interval
- Diagnostic performance
- Control charts
- Process capability
- Pareto analysis
- Study Designs
- Bibliography

Version 5.40

Published 29-Jul-2019