If you would like to include ALL the independent variables, you may try principal component analysis. It also shows you which independent variables are highly correlated with each other.
You can find tons of explanations about principal component analysis. Look for principal component analysis, factor analysis, or principal component regression. Sangdon Lee, Ph.D., "Rafal" <[EMAIL PROTECTED]> wrote in message news:<[EMAIL PROTECTED]>... > Hello > > Many times on this list voices against stepwise regression has appeared > unfortunately no substitutes have been proposed for selection of > 'independent' variables which should be included in final model. I have data > from network of sample plots from mountain area. On plots indicators of tree > stand damage and tree characteristics were measured. I want to create model > for forest damage depending on plot characteristics like altitude, age etc. > Unfortunately 'independent' variables are not 'independent'. For example > tree age is highly correlated with altitude because older tree stands occupy > higher locations. I want to select variables correlated with tree stand > damage. I took all variables which I suspect can have influance on damage > and created a linear regression model with all variables. Can I use > calculated partial correlations between modelled damage and each > 'independent' variable to select variables which I should use in final > model? If it is not wise approach what methods would you suggest? > > Thanks for help > > Rafal . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
