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An Application on Multinomial Logistic Regression Model

Pakistan Journal of Statistics and Operation Research

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Title An Application on Multinomial Logistic Regression Model
Creator El-Habil, Abdalla M; Associate Professor
Department of Applied Statistics, Faculty of Economics and Administrative Sciences, Al-Azhar University, Gaza - Palestine
Subject Statistics
Multinomial logistic regression model - categorical data analysis - maximum likelihood method - generalized linear models – classification

Description This study aims to identify an application of Multinomial Logistic Regression model which is one of the important methods for categorical data analysis. This model deals with one nominal/ordinal response variable that has more than two categories, whether nominal or ordinal variable. This model has been applied in data analysis in many areas, for example health, social, behavioral, and educational.To identify the model by practical way, we used real data on physical violence against children, from a survey of Youth 2003 which was conducted by Palestinian Central Bureau of Statistics (PCBS). Segment of the population of children in the age group (10-14 years) for residents in Gaza governorate, size of 66,935 had been selected, and the response variable consisted of four categories. Eighteen of explanatory variables were used for building the primary multinomial logistic regression model. Model had been tested through a set of statistical tests to ensure its appropriateness for the data. Also the model had been tested by selecting randomly of two observations of the data used to predict the position of each observation in any classified group it can be, by knowing the values of the explanatory variables used. We concluded by using the multinomial logistic regression model that we can able to define accurately the relationship between the group of explanatory variables and the response variable, identify the effect of each of the variables, and we can predict the classification of any individual case.
Publisher College of Statistical and Actuarial Sciences
Date 2012-03-28
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Source Pakistan Journal of Statistics and Operation Research; Vol 8. No. 2, 2012; 271-291
Language eng