# Principal component (PC) as a substitute for colinear covariates?

I am working on a spatial linear regression and I can tell there is collinearity between covariates. Can I use PCA (Principal Component Analysis) images instead of original covariates to estimate the dependent variable? I am assuming PC1=Variable 1, PC2=Variable 2, etc. Or are there any other methods to solve the collinearity problem?

• What is your goal in fitting the regression model? Collinearity is only a problem in some cases. – shadowtalker Aug 25 '14 at 16:17
• I am trying to estimate the response variable from covariates using spatial linear regression. – Kaleab Aug 25 '14 at 16:24
• To be more specific: are you interested in the magnitudes and directions of the coefficients, or making accurate predictions, or both? – shadowtalker Aug 25 '14 at 16:34
• @ssdecontrol .Both the coefficients and sign of the predictors as well as accurate estimation of response variable using these PC's of covariates. – Kaleab Aug 25 '14 at 16:45
• Thanks for clarifying, that will help me write a more helpful answer. – shadowtalker Aug 25 '14 at 16:54

The choice to use the PCA transformation of the data can lead to a better estimation of the output $y$ but to understand the role of the original variables will be more difficult.