I am currently working to build a model using a multiple linear regression. After fiddling around with my model, I am unsure how to best determine which variables to keep and which to remove.
My model started with 10 predictors for the DV. When using all 10 predictors, four were considered significant. If I remove only some of the obviously-incorrect predictors, some of my predictors that were not initially significant become significant. Which leads me to my question: How does one go about determining which predictors to include in their model? It seemed to me you should run the model once with all predictors, remove those that are not significant, and then rerun. But if removing only some of those predictors makes others significant, I am left wondering if I am taking the wrong approach to all this.
I believe that this thread is similar to my question, but I am unsure I am interpreting the discussion correctly. Perhaps this is more of an experimental design topic, but maybe someone has some experience they can share.