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Courses Taught

Biostatistics: Community Medicine

Introduction to Biostatistics provides an introduction to selected important topics in biostatistical concepts and reasoning. This course represents an introduction to the field and provides a survey of data and data types. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data; statistical hypothesis testing and its application to group comparisons; issues of power and sample size in study designs; and random sample and other study types. While there are some formulae and computational elements to the course, the emphasis is on interpretation and concepts.

Survival Analysis

This course develops and uses statistical methods appropriate for analyzing right-censored (i.e., incomplete) time-to-event data. Topics covered include nonparametric estimation (e.g., life table methods, Kaplan Meier estimator), nonparametric methods for comparing the survival experience of two or more populations, and semiparametric and parametric methods of regression for censored outcome data. Substantial use is made of the R, STATA and SPSS statistical software packages.

Quantitative Techniques

It is applied course in statistics that is designed to provide you with the concepts and methods of statistical analysis for decision making under uncertainties. This course is a combination of lectures and computer-based practice, joining theory firmly with practice. It introduces techniques for summarizing and presenting data, estimation, confidence intervals, hypothesis testing, modeling relationships and some multivariate techniques. The lectures focuses more on understanding of key concepts and statistical thinking, and less on formulas and calculations, which can now be done on statistical software.

Categorical Data Analysis

Deals with statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal/ordinl scale and as categorical variables.

Advance Applied Linear Models

The course introduces statistical models for situations where least squares regression and standard ANOVA techniques do not apply

Statistical Computing

The purpose of this course is to introduce students to the use of modern statistical packages for analyzing various types of data commonly encountered in many areas of science. Students with limited computer experience will be introduced to some widely used statistical packages such as Excel, SPSS and R, a free version of S -PLUS. They will learn how to use these packages for analyzing various types of real life data. Mathematica will be introduced for symbolic computing with special reference to algebraic manipulation of statistical distributions.