I have a set of independent variables (X1, X2, ..., X10) and I have run a PLS to find a combination of the X1, X2, ..., X10 that best predicts an outcome Y (a single-variable outcome).

As a result, I get a vector of loadings that are the coefficients to weight each independent variable and calculate the combination that predicts Y.

How are the loadings different from the beta coefficients of a multiple linear regression of the scaled X1, X2, ..., X10 (i.e. each with a mean of 0 and an SD of 1) against the scaled Y, fitted without interaction?



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