— College of Public Health — Departments — Biostatistics — Faculty & Staff — Robin High, MBA, MA

Robin High, MBA, MA

Statistician III, UNMC Department of Biostatistics

Robin High, MBA, MA, joined UNMC in 2008 and is a statistician III for the Department of Biostatistics in the UNMC College of Public Health.

He previously held roles at the University of Oregon, Oregon State University and the Texas Research and Development Foundation Austin Research Engineers in Austin, Tx.

Education

MM University of Oregon, Eugene, 1996
MA University of Texas, Austin, 1983
MBA University of Texas, Austin, 1981
BM University of Nebraska, Lincoln, 1976

Professional Interests

Specialized statistical applications include the graphical representation of data, repeated measures, Linear and Non-Linear Models with fixed and random effects, Generalized Linear Mixed Models for continuous and discrete data, and estimation by maximum likelihood of parameters for non-normal distributions, as well as the design and analysis of experiments and statistical power calculations.

Advanced user of SAS software for data manipulation, graphics, and data analyses.

Selected Publications

Jennifer L. Huberty, Richard R. Rosenkranz, Mary A. Balluff, Robin High, Describing weight status and fitness in a community sample of children attending after-school programming. (in review)
Edward V. Fehringer, Robin High, CT Evidence of Bone Incorporation Between Radial Fins of an Uncemented Glenoid Component Central Peg (in review)
Dharam Kaushik, Robin High, Curtis J Clark, Chad LaGrange, Malfunction of the Da Vinci robotic system during robotic - assisted laparoscopic prostatectomy: an international survey. (in review)
Hess, J. A., LD Kincl, JT Albers, and RR High "Ergonomic Evaluation of an Extension Screw Gun to Improve Work Postures" (in review).
Introduction to Statistical Power Calculations for Linear Models with SAS 9.1, Paper presented at the Pacific Northwest SAS User's Group Conference, Seattle, Washington (2007).

Professional Affiliations

American Statistical Association