STEPWISE PROCEDURES IN DISCRIMINANT ANALYSIS
Several multivariate measurements require variables selection and ordering. Stepwise procedures ensure a step by step method through which these variables are selected and ordered usually for discrimination and classification purposes. Stepwise procedures in discriminant analysis show that only important variables are selected, while redundant variables (variables that contribute less in the presence of other variables) are discarded. The use of stepwise procedures is employed as to obtain a classification rule with a low error rate. Here in this work, variables are selected based on Wilks’ lambda and partial F. The variable with the minimum and maximum F is included in the model first, followed by the next most important variable as can be observed from the forward selection. Backward elimination deletes the variable with the smallest F and the largest in a step by step fashion. SPSS is used to illustrate how stepwise procedures can be employed to identify the most important variable to be included in the model based on Wilks’ and partial F. The analysis revealed that only variables X1, head width at the widest dimension and X4, eye-to-top-of-head measurement are the most important variables that are worthy of inclusion into the discriminant function.