Categorical Data Analysis
The main ideas of the course are to develop a critical approach to the analysis of categorical data often encountered in health sciences research. This process will include gaining some technical insight (mechanics of the statistical methodology behind the ideas) as well as applications of these methods in healthrelated data. Some of the main objectives of this course are:

To develop an intuitive and critical approach to the analysis of frequency tables.

To examine basic ideas and methods of generalized linear models (e.g. Logistic regression, Multinomial Logistic Regression, Ordinal Logistic Regression and Log Linear Models)

To gain experience in categorical data analysis using statistical software packages (SPSS/Open Epi,etc.)
Quick review:
Some of the basic sampling techniques, variable types, probability distributions relevant in our course (binomial, multinomial, Poisson, etc.), expectation, concept of likelihood, tests for oneway tables
Contingency tables: Review of 2 X 2 tables and r X c tables, tests for independence and homogeneity of proportions, Fisher's exact test, McNemar's test. Introduction to threeway tables, full and conditional independence, collapsing. (Contigency Table Analysis)
Introduction to generalized linear models: Logistic regression, interpretation of coefficients, model selection, diagnostics, goodness of fit. Introduction of multinomial regression, polytomous regression, poisson regression.
Loglinear models: for multiway tables
Special topics:
Books:
Agresti, Alan (2002) Categorical Data Analysis, Second Edition, Willey
Agresti, Alan (2007) An Introdiction to Categorical Data Analysis, Welley
Hosmer, D.W. and Lemeshow, S. (2000) Applied Logistic Regression, Second Edition, Wiley

About
Nadeem Shafique Butt, Assistant Professor of Biostatistics at King Abdulaziz University Also Visiting/Adjunct faculty member at University of the Punjab,
Phone:
Fax:

Categorial Data Analysis
 Advance Applied Linear Models
 Advanced Applied Statistics
 Business Statistics
 Categorial Data Analysis
 Decision Models and Risk Analysis
 Introduction to Statistics
 Mathematical Modeling for Business
 Probability and Statistics
 Quantitative Techniques
 Statistical and OR Computing
 Statistical Computing
 Statistical Inference
 Statistics for Enviornmental Engineers
 Statistics for Managers
 Survival Analysis

Related Links