First, we should split the original data set into disjoint training and testing data sets, so that we can better evaluate and compare different models. One possible simple way is to random select a proportion, say, 10% of observations from the data for use as a test sample, and use the remaining data as a training sample building different models. Note that in practice, it is more reasonable to select much larger proportion, say 30% or 20%, as testing sample. Here we chose only 10% as the testing sample, so that we can list those testing observations explicitly below