Drawn from nearly four decades of Lawrence L. Kupper’s teaching experiences, this text presents theoretical statistical concepts, numerous exercises, and detailed solutions that span topics from basic probability to statistical inference. The text links theoretical principles to real-world situations, including some of the authors’ own biostatistical work that has addressed complicated design and analysis issues in the health sciences. The authors explain the biostatistical principles needed to solve the exercises, which range in level of difficulty from fairly basic to more challenging. By working through these exercises, students develop a deep understanding of the principles of biostatistical theory.
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Lawrence L. Kupper, Ph.D., is an emeritus alumni distinguished professor of biostatistics at the University of North Carolina at Chapel Hill. Dr. Kupper has received several teaching and research awards during his career and has been involved with many research areas in the health sciences, including epidemiology, environmental and occupational health, maternal and child health, and medicine. His research interests concern the development of innovative biostatistical methods for the design and analysis of public health research studies. Brian H. Neelon, Ph.D., is a research statistician with the Children's Environmental Health Initiative (CEHI) in the Nicholas School of the Environment at Duke University. Before working at Duke University, Dr. Neelon was a Postdoctoral Research Fellow in the Department of Health Care Policy at Harvard University. He earned his Ph.D. from the University of North Carolina. His research interests include Bayesian methods, longitudinal data analysis, finite mixture models, and health policy statistics. Sean M. O'Brien, Ph.D., is an assistant professor of biostatistics and bioinformatics at the Duke University School of Medicine. Dr. O'Brien earned his Ph.D. from the University of North Carolina. His research interests include statistical methods for healthcare provider profiling, observational studies, and Bayesian data analysis.
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