Final answer:
When using PCA to transform data and running a regression model on it, interpreting the regression coefficients in terms of the original attributes can be challenging.
The coefficients represent the relationship between the principal components and the dependent variable, but cannot be directly interpreted in terms of the original attributes. Additional context or analysis is needed to understand the contribution of each original attribute.
Step-by-step explanation:
When you use Principal Component Analysis (PCA) to transform your data and then run a regression model on it, interpreting the regression coefficients in terms of the original attributes can be challenging.
This is because PCA transforms the original attributes into linear combinations called principal components, which don't have a straightforward interpretation in terms of the original attributes. However, you can still gain some understanding by examining the coefficients of the regression model.
The coefficients of the regression model represent the linear relationship between the principal components (derived from PCA) and the dependent variable. They indicate the relative importance and direction of the different principal components in influencing the dependent variable.
However, in terms of the original attributes, the interpretation becomes more abstract. The coefficients tell us how the linear combinations of the original attributes (the principal components) contribute to the prediction of the dependent variable, but they don't directly tell us how each individual original attribute contributes separately.
Therefore, while the regression coefficients provide valuable information about the relationship between the transformed principal components and the dependent variable, it is not possible to directly interpret them in terms of the original attributes without additional context or analysis.