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The International Year of Statistics (Statistics2013)
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Department of

Statistics

NCSU Dept of Statistics
5109 SAS Hall
2311 Stinson Drive
Raleigh, NC 27695-8203

Tel: (919) 515-2528
Fax: (919) 515-7591


SIBS Instructors

Karen Pieper

  • clinical trials
  • biostatistics in cardiovascular disease research




Karen Pieper received her Master's degree at the University of North Carolina in Chapel Hill in 1987. She has since worked at Duke University as a cardiovascular clinical trials statistician, at the University of Virginia as a general medical center statistician, and as a private statistical consultant. She returned to the Duke Clinical Research Institute in 1999. At present, she is the Associate Director of Clinical Trials Statistics and oversees the manuscripts for all completed clinical trials and registries, primarily in the field of cardiology. She is also is involved in mentoring and education for faculty and physicians in training as well as beginning statisticians.

Herle McGowan

  • statistics education
  • research methods in educational settings




I became an assistant professor at NC State in 2009, after receiving a Ph.D. in Statistics from the University of Michigan. My research is in the field of statistics education, which focuses on how students learn statistics and how to best teach statistical concepts. In particular, I am interested in the use of educational technology and course redesign. One of my current projects involves restructuring a large lecture course to make the learning experience more efficient for students by capitalizing on the technology they commonly use. This restructuring also makes the course more cost-efficient for the university.

Kimberly Weems

  • measurement error models
  • generalized linear models
  • environmental statistics





My research considers nonlinear models in which the predictor variables are measured with error. For example, suppose we want to use blood pressure to predict heart disease. If blood pressure is measured with error, we are interested in how the error effects the parameter estimates and how we can correct for these effects. I also work with undergraduate students on a variety of research projects that develop regression models for air pollution, specifically ozone and particulate matter.

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