Conference proceeding
Assessing the Effects of Different Types of Covariates for Binary Logistic Regression: EMPOWERING THE APPLICATIONS OF STATISTICAL AND MATHEMATICAL SCIENCES
2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II), Vol.1643, pp.425-430
AIP Conference Proceedings
01/01/2015
DOI: 10.1063/1.4907476
Abstract
It is well known that the type of data distribution in the independent variable(s) may affect many statistical procedures. This paper investigates and illustrates the effect of different types of covariates on the parameter estimation of a binary logistic regression model. A simulation study with different sample sizes and different types of covariates (uniform, normal, skewed) was carried out. Results showed that parameter estimation of binary logistic regression model is severely overestimated when sample size is less than 150 for covariate which have normal and uniform distribution while the parameter is underestimated when the distribution of covariate is skewed. Parameter estimation improves for all types of covariates when sample size is large, that is at least 500.
Details
- Title: Subtitle
- Assessing the Effects of Different Types of Covariates for Binary Logistic Regression: EMPOWERING THE APPLICATIONS OF STATISTICAL AND MATHEMATICAL SCIENCES
- Creators
- Hamzah Abdul Hamid - Universiti Malaysia PerlisYap Bee Wah - Universiti Teknologi MARAXian-Jin Xie - The University of Texas Southwestern Medical CenterHezlin Aryani Abd Rahman - Univ Teknol MARA, Fac Comp & Math Sci, Ctr Stat & Decis Sci Studies, Shah Alam 40450, Selangor, Malaysia
- Contributors
- M S Mohamad (Editor)WNSW Yusoff (Editor)NAZM Noar (Editor)R Zakaria (Editor)M R AbHamid (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II), Vol.1643, pp.425-430
- Publisher
- Amer Inst Physics
- Series
- AIP Conference Proceedings
- DOI
- 10.1063/1.4907476
- ISSN
- 0094-243X
- eISSN
- 1551-7616
- Number of pages
- 6
- Language
- English
- Date published
- 01/01/2015
- Academic Unit
- Preventive and Community Dentistry; Biostatistics; Dental Research
- Record Identifier
- 9984367622602771
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