The Generalized Linear Model (GLMz) expands the General Linear Model (GLM) so that the dependent variable is linearly related to the factors and covariates via a specified link function. GLMz allows for the dependent variable to have a non-normal distribution. GLMz covers widely used statistical models, such as linear regression for normally distributed responses, logistic models for binary data, logistic models for ordinal data, loglinear models for count data, complementary log-log models for interval-censored survival data, plus many other statistical models through its very general model formulation. The Generalized Estimating Equations (GEE) procedure extends the GLMz to allow for analysis of repeated measurements or other correlated observations, such as clustered data.
- Linear regression
- Binary logistic regression
- Binary probit regression
- Ordinal logistic regression
- Poisson regression
- Repeated measures
Dr. Karuthan Chinna has a Master Degree in Applied Statistics from the Michigan State University, USA and a PhD in Multivariate Quality Control from the Multimedia University, Malaysia. Dr. Karuthan Chinna has been teaching statistics for 35 years and is also very active in research and consulting. He has published two books; one on Biostatics and the other one on SPSS. He has been a statistical consultant for several projects at the Ministry of Health and other institutions.
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Julius Centre University of Malaya (JCUM)
Department of Social & Preventive Medicine, Faculty of Medicine,
University of Malaya, Malaysia.
The Julius Centre University of Malaya (JCUM) was established in 2008. Using advanced epidemiological methods and health data analytics, JCUM aims to be a regional referral centre for learning and research in the areas of epidemiology, clinical epidemiology, evidence-based medicine, and biostatistics. The head of this centre is Assoc Prof Dr Noran Naqiah Mohd Hairi