This is a follow-up question from the post: PCA on correlation or covariance?
The accepted answer quotes:
You tend to use the covariance matrix when the variable scales are similar and the correlation matrix when variables are on different scales.
I have a data set that are coefficients of a linear model. Let's say $c_1, c_2, \cdots, c_n$. This means they are dimensionless and were obtained by regression. I computed the correlation among the coefficients and found out that the maximum correlation is 0.88 while lowest is -0.79.
I also made a boxplot and the results showed that the variance in some coefficients are larger and some are smaller.
I wanted to know which would be more appropriate to use: PCA cov or correlation?