Experimental Design and Data Analysis for Biologists

We can easily find ourselves with lots of predictors. This situation has been common in ecology and environmental science but has spread to other biological disciplines as genomics, proteomics, metabolomics, etc., become widespread. Models can become very complex, and with many predictors, collinearity is more likely. Fitting the models is tricky, particularly if we’re looking for the “best” model, and the way we approach the task depends on how we’ll use the model results. This chapter describes different model selection approaches for multiple regression models and discusses ways of measuring the importance of specific predictors. It covers stepwise procedures, all subsets, information criteria, model averaging and validation, and introduces regression trees, including boosted trees.