DEVELOPMENT OF STATISTICAL PROGRAM FOR DISCRIMINANT ANALYSIS FOR MIXED VARIABLES USING R-CODE

DEVELOPMENT OF STATISTICAL PROGRAM FOR DISCRIMINANT ANALYSIS FOR MIXED VARIABLES USING R-CODE, By Iwuagwu, Chukwuma E. Ibegbulem, Zebulon O. Nwosu, Obinna M. Ekwuribe, Celestine.

Department of Statistics, Abia State Polytechnic, Aba, Abia State.

Abstract

Discriminant analysis is a statistical technique that tried to predict on the bases of one or more independent variables. Whether an individual or other objects can be placed into a particular category of a categorical dependent variables. This research has its aim on the development of statistical program for discriminant analysis for mixed variables (discrete and continuous) using R-programming language. The data for the research were obtained from simulated data and real data. In the simulated experiment, a training data set was generated using R-code. The real data came from secondary data which consist of fifteen variables obtained from UNDP report on human development. The result obtained with R-programming language revealed that the developed program gave satisfactory result in terms of minimizing the average error rate.

Keywords: Location model, Discriminant, Discrete and Continuous, R-Code

AsPoly Journal of Sciences, Engineering and Environmental Studies

59

1.1 Introduction

Discriminant analysis is a statistical technique that allows one in understanding the differences of objects between two or more groups with respect to several variables simultaneously. The procedure tried to predict on the basis of one or more predictors or independent variables, whether an individual or other subjects can be placed into a particular category of a categorical dependent variables. The purpose is to determine the predictor variables on the base, which groups can be determined and discriminant model is built on the basis of a set of observations for which the groups are known.