Incremental Effect Modeling of Categorical Data Using Logistic Regression

• 영문명: 로지스틱 회귀를 이용한 범주형 계수치 자료의 증분효과 모형
• 발행기관: 한국산업경영시스템학회
• 저자명: 안해일(Haeil Ahn)
• 간행물 정보: 『한국산업경영시스템학회 학술대회』2014년 춘계학술대회 논문집, 119~124쪽, 전체 5쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2014.05.10

영문 초록

In this paper, we have considered the modeling and analyses of categorical data. We modeled binary data with categorical predictors, using logistic regression to develop a statistical method. We found that ANOVA-type analyses often performed unsatisfactory, even when using arcsine-square-root transformations. We concluded that such methods are not appropriate, especially in cases where the fractions were close to 0 or 1. The logistic transformation of fraction data could be a promising alternative, but it is not desirable in the statistical sense. The major purpose of this paper is to demonstrate that logistic regression with an ANOVA-model like parameterization aids our understanding and provides a somewhat different, but sound, statistical background. We examined a simple real-world example to show that we can efficiently test the significance of regression parameters, look for interactions, estimate confidence intervals, and calculate the difference between the mean values of the referent and experimental subgroups. This paper demonstrates that precise confidence interval estimates can be obtained using the proposed ANOVA-model like approach. The method discussed here can be extended to any type of fraction data analysis, particularly for experimental design.

목차

Abstract

1. Introduction

2. Fractions

3. Logistic Model

4. CONCLUSION

References

키워드

ANOVA Logistic Regression Logistic Transformation Incremental Modeling