Courses that I have taught at Michigan

Service Courses

Biostatistics 523

The goal of this course is to provide students with skills necessary to carry out regression analyses, commonly used method in many health investigations. Both experimental designs and observational studies are considered. The regression models considered are linear, nonlinear, logistic, Poisson, multinomial and ordinal regression models with an emphasis on multiple linear regression models. The Analysis of Variance (ANOVA) and covariance (ANCOVA) are also be covered. The ideas of confounding, effect modifications/interactions, and causal versus association inferences are also covered. There are extensive discussions of inferences and interpretations, approaches for fitting and assessing goodness of fit of these regression models. The primary software package used in this class is SAS. The Lab component of the course consists of practical implementation of the methods covered in the lectures. This is a second course in biostatistics and the first course in regression analysis.

Biostatistics 524

The goal of this course is to provide students basic understanding of statistics. The topics include graphical and numerical presentation of data, Design of Experimental and Observational Studies, Probability, Basics of inference: Estimation, sampling distribution, confidence intervals and signifcance tests. Numerous applications one sample and  two sample problems, continuous and discrete outcomes. Nonparametric methods and simple linear regression. This course is a part of the University of Michigan's Master's degree in Clinical Research Design and Statistical Analysis (CRDSA). This is an 18-month program where students meet one extended weekend (Thursday-Sunday) a month to take courses and do homework problems between sessions. This program allows students to maintain full time employment and earn Masters degree. For more details see OJOC.

Biostatistics 560

The objective of this course is to learn fairly advanced statistical techniques used in Epidemiology. This course begins with logistic and Poisson regression models, survival analysis, log-linear models for categorical data, models for longitudinal data. Students use SAS to analyze several data sets. This course requires Biostatistics 523. Most of the students are doctoral students in Epidemiology.   

Biostatistics 581

The objective of this course is to learn about statistical models and methods for the analysis longitudinal data. The emphasis of this course is on applications in health sciences. The basic theoretical reasoning underpinning each method/model are be provided. In this course the students  learn different parametric approaches for analyzing repeated measurements and explore the assumptions underlying each approach. Issues concerning missing data in longitudinal studies are addressed. The student  learn several aspects of analyses of longitudinal data with continuous, binary, ploytomous and count outcome variables in greater depth through several data analysis examples and term projects. This course is also a part of the OJOC program.

Courses taken by Masters and Doctoral Students in Biostatistics

Biostatistics 650

This is an introductory regression analysis with emphasis on Multiple linear regression. This course is taken by the first year students in the Biostatistics Masters program. All aspects of ordinary least squares, weighted, ridge and robust regressions are discussed. Model diagnostics and remedies are discussed. All aspects of inferences based on these models are also discussed. Nonlinear and generalized linear regreession models are briefly introduced. Students use R and SAS to peform analysis of data.

Biostatistics 682

This is an introductory Bayesian analysis courses for masters and doctoral students. This course is taken by students in Biostatistics, Statistics and Engineering. The students are introduced basics of a Bayesian  view of statistical inference, simple examples and introduce Bayesian computations. The details about various iterative and non-iterative simulation techniques, model checking and diagnostics and several practical applications are discussed. The students  use  R, SAS and Winbugs to  perform analysis.

Biostatistics 870

This course is designed for doctoral students in Biostatistics and Statistics and covers both theoretical and practical aspects of modeling longitudinal data. Population average and subject specific models are discussed; Both parametric and nonparametric models are discussed; Estimating equations, maximum likelihood and Bayesian approaches are discussed. Missing data issues are also dicussed. Students write their own programs in R, SAS, C or Fortran to develop and evaluate methodology.

Biostatistics 880

This course is taken mostly by doctoral and by some masters level students and covers the topic of analysis of incomplete data. The topics include Missing data mechanisms and patterns, possibe approaches for analysing data with missing values: Weighting, multiple imputation, maximum likelihood and fully Bayesian approaches. Students learn both theoretical and practical aspects of inference from incomplete data under a variety of contexts: Experimental design, observational studies, cross-sectional  and longitudinal data and ignoorable and non-ignorable missing data mechanism.

Survey Methodology Courses


This is an applied sampling course taken by the Masters and doctoral level students learn about design and estimation problems from complex surveys. More emphasis is on designing of surveys including telephone, mail, web and face-to-face surveys. Frame construction, inclusion probability calculations, clustering, stratification and weighting are dicussed. Approximations needed to calculate the sampling variances of population means, proportions and totals are discussed. Area probability sampling and dual frame designs are also discussed.


The objective of this course is to introduce advanced analytical technique using the complex survey data. Inference for parameters in various regression models are discussed using linearization and replication techniques. Students learn,  through practical examples, the  impact of ignoring the complex design features. Approximations of designs to calculate the sampling variances are discussed. Weighting and imputation methods for handling incomplete data are dicussed.


The objective of this course is to provide theoretical and practical aspects of weighting and imputation in complex surveys. Cell adjustment, response propensity and post-stratification methods for constructing weights are discussed. Both implicit (such as Hotdeck) and explicit model based imputation methods are discussed. Practical examples are used to illustrate the methodology and get hands-on experience.


The objective of this course is to introduce Bayesian Inference for Surveys. The emphasis is on inferring abou the finite population quantities through predictive inference of the non-sampled portion of the population. All complex design features are discussed in the Bayesian context including ignorable and non-ignorable sampling mechanisms.  The methodolgy is implemented through writing programs in R, SAS and Winbugs.