Model Building and Variable Selection

Model building and variable selection are two important processes in order to build an acceptable model for analysis. Statisticians coin the model with one independent variable as a univariate model and a model with more than one independent variables as multivariate model but some analysts prefer to call these as univariable and multivariable model. The main goal of a statistical analysis of effects should be the production of the most accurate effect estimates obtainable from the data and available software. Variable selection means choosing among many variables which to include in a particular model to select appropriate variables from a complete list of variables by removing those that are irrelevant or redundant. The purpose of such selection is to determine a set of variables that will provide the best fit for the model so that accurate predictions can be made. Chowdhury and Turin listed some variable selection methods: backward elimination, forward selection, stepwise selection and all possible subset selection.