I have a continuous outcome (dependent) variable, which is body weight and I'm wondering which of my 20 candidate predictors (independent variables) are the most important ones for prediting body weight. Usually I'd just go for a linear regression and try to rationalize my variable selection by judging coefficient sizes, significance and subject matter knowledge. But after colliding with all threads about principal component analysis, I'm wondering whether the linear regression I usually go for is suboptimal for this purpose.
Variable selection is of inferior importance, since I am likely to select variables on subject matter knowledge. I am primarily interested in finding the strongest predictors of the dependent variable and a measure of their impact after multivariable adjustment. This is something I would typically carry out by means of linear regression and present the coefficients along with confidence limits.
What is your opinions on this? How should this analysis be carried out? I'm an R user.