Model selection
REGRESSION
Starting with the full model, tests for multicollinearity were conducted with all variables included, and the model selection process proceeded iteratively by removing variables one at a time until there was no longer a positive test for multicollinearity. Variables found to be most collinear, based on individual multicollinearity tests with non-significant regression coefficients, were removed first. If only significant variables were remaining as collinear, the least significant were removed next. When there was no longer an overall positive test for multicollinearity, remaining statistically insignificant variables were removed and the fit of the model from before and after was compared.Further details on the model selection methodology and a full list of overall and individual tests used for the multicollinearity testing can be found in 