statisticalrethinkin..

152 5. MULTIVARIATE LINEAR MODELSvariables are in reality strongly associated with the outcome variable. is frustrating phenomenonarises from the details of how statistical control works. So once you understandmulticollinearity, you will better understand multivariate models in general.To explore multicollinearity, let’s begin with a simple simulation. en we’ll turn to theprimate milk data again and see multicollinearity in a real data set.5.3.1. Multicollinear legs. e simulation example is predicting an individual’s height usingthe length of his or her legs as predictor variables. Surely height is positively associatedwith leg length, or at least the simulation will assume it is. Nevertheless, once you put bothleg lengths into the model, something vexing will happen.e code below will simulate the heights and leg lengths of 100 individuals. For each,first a height is simulated from a Gaussian distribution. en each individual gets a simulatedproportion of height for their legs, ranging from 0.4 to 0.5. Finally, each leg is salted with alittle measurement or developmental error, so the le and right legs are not exactly the samelength, as is typical in real populations. At the end, the code puts height and the two leglengths into a common data frame.R code5.28N