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Timeline for PCA and scree plot and slope

Current License: CC BY-SA 4.0

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Aug 27, 2023 at 15:33 comment added user392987 thanks, Peter. greatly appreciated :)
Aug 27, 2023 at 15:23 comment added Peter Flom That's correct.
Aug 27, 2023 at 15:11 comment added user392987 thank you Peter. Long story short, when you have correlated variables, one option is to use the pca other than dropping redundant variables or use AIC/BIC. Then, you now have orthogonal variables as well as PCs are orthogonal. Can you please confirm this? thx
Aug 27, 2023 at 12:14 comment added Peter Flom Yes. PCs (at least if you do orthogonal rotation) are not correlated and can't be colinear. But they may not be the best solution to the problem of collinearity. I think it is rare for them to be the best solution.
Aug 27, 2023 at 2:48 comment added user392987 thank you , Peter. I have one question: you know all PC are not correlated with one another and using PCs can mitigate the issue of collinearity. all PC are not correlated with one another --> PC1 and PC2 are independent of each other (no correlation) using PCs can mitigate the issue of collinearity --> PC is not a linear combination of other x's. can you please confirm my interpretation is correct? thanks in advance
Aug 26, 2023 at 23:09 comment added Peter Flom Correlation is a measure of the linear relationship between two continuous variables. Collinearity is a measure of how close one variable is to being a linear combination of other variables. You can have low correlations among all variables and still have collinearity (if one variable is a sum of a bunch of uncorrelated variables). And you can have collinearity among categorical variables.
Aug 26, 2023 at 18:54 comment added user392987 Thank you Peter for the detailed answer. CAn you please explain the difference between correlation and collinearity? Having a hard time differentiating the two
Aug 26, 2023 at 18:42 history answered Peter Flom CC BY-SA 4.0