Which statement regarding principal components is true?

The statement that the principal components can reflect original variables with the greatest model coefficients is true in the context of how principal component analysis (PCA) works. In PCA, each principal component is a linear combination of the original variables, and these combinations are formulated in such a way that the first principal component captures the maximum variance present in the data. Subsequent components capture decreasing amounts of variance.

When considering model coefficients, the principal components can provide insights into which original variables contribute most significantly to the variance explained by each component. Specifically, if certain original variables have high coefficients in a principal component, they indicate that these variables have a substantial influence on the variation represented by that component.

This ability to reflect the original variables with large coefficients can help in understanding which features are driving the patterns in the data, making it easier for analysts to interpret the results of PCA and to understand the underlying structure in the dataset.