Which of the following is true about the cumulative proportion of variance explained by principal components?

The cumulative proportion of variance explained by principal components is a key concept in principal component analysis (PCA). As additional principal components are included in the analysis, each new component captures some of the remaining variance in the dataset. Since PCA is designed to maximize explained variance, every subsequent component can only add to, or at a minimum, maintain the amount of variance explained.

As a result, the cumulative proportion of variance explained will always increase or stay the same as more principal components are added. It will never decrease because every new principal component has at least some non-negative contribution to explained variance. Therefore, saying that it increases as more principal components are added accurately reflects the underlying mechanics of PCA and its goal of revealing the structure of the data by retaining the most significant variance. Thus, option C is the correct statement regarding the behavior of cumulative proportion of variance explained in relation to the principal components.