Using Classification Methods in Identifying the Labor Force Categories in Palestine

Abstract:

Multinomial logistic regression(MLR) and Discriminant Analysis (DA) are two techniques that commonly used  for data classification. Both of them are applied at Labor Force in Palestine data 2012 in order to predict the probability of a specific categorical of  Labor Force (LF)  based upon several explanatory variables. we used real data on LF, from a survey of LF 2012 which was conducted by Palestinian Central Bureau of Statistics(PCBS). The data sample size had been 25353 observations from West Bank and Gaza Strip. The target group was the age group  (15- 65) years for both sexes.  Labor Force data has 12 variables;  the dependent variable  is nominal  with three categories and 11 independent variables. So, we have two models for each techniques. Correct classification is 83.5% for MLR model compared with 81.1% for DA. In addition that the area under the ROC curve is 91.89% for MLR and 52.8% for DA These results demonstrate that MLR can be more powerful analytical technique.

Key Words:  Confusion Matrix – Roc curve – Multinomial Logistic Regression –  Discriminant Analysis - Odds ratio