Advanced concepts in data base analysis, model development, and ancillary functions in geographic information systems. Lecture, two hours; laboratory, four hours per week.

This course provides an introduction to Bayesian ideas and data analysis applied to the biosciences. The course illustrates current approaches to Bayesian modeling and computation in biostatistics.

As a foundational course to the Honors curriculum, Knowledge and Society takes a vested interest in helping students develop an appreciation for the pursuit of truth and knowledge. By exploring the links between subjective and social phenomena, students will gain a clearer understanding of the way inquiry and curiosity are implicated in our familial, cultural, historical, and political worlds. This course aims to foster an environment where students are equipped with the tools to confidently discern the assumptions, premises, and expectations of variant claims to knowledge and truth.

Multinomial and product-multinomial models; large-sample theory of estimation and testing, Pearson chi-square and modified chi-square statistics, Pearson-Fisher Theorem, Wald Statistics and generalized least squares technique; applications to problems of symmetry, association and hypotheses of no interaction in multi-dimensional contingency tables.

Analysis of time to event data encountered in Public Health and Medicine. Survival distributions and hazard functions. Time to event analysis using Kaplan-Meier method and life-table method. Accelerated failure time model, logit model for discrete data, complimentary log-log model, and proportional hazards model. Tests for goodness-of-fit, graphical methods, and residual and influence statistics. Time- dependent covariates, non-proportional hazards, left truncation, and late entry into the risk set. Sample size and power, competing risks, and time to event analysis with missing data.