Department of Public Health Sciences
BMTRY 701 : Biostatistical Methods II (Regression methods)
The course is intended to focus on biostatistical applications by providing a broad coverage of critical biostatistical applications topics. The primary audience for the sequence is M.S. students (or first-year PhD students that do not have a prior M.S. degree) in biostatistics, but the course will be delivered at a level that it may be taken by graduate students in related scientific fields such as bioinformatics and epidemiology.
- Simple linear regression (least squares estimation, partitioning sums of squares, hypothesis testing of slope and intercept, model fit, confidence intervals of mean response, prediction intervals)
- Correlation (the correlation coefficient, test of hypothesis and confidence intervals, testes of equality of correlation coefficients, multiple correlation coefficient, partial correlation)
- Analysis of variance (inference based on F-statistic, R-notation, sums of squares, one-way ANOVA)
- Multiple linear regression (general linear model, linear contrasts, testing of general linear hypotheses, confidence intervals, prediction intervals)
- Model Specification (biologic plausibility, interaction, confounding, indicator variables, iterative predictor selection routines)
- Model Diagnostics (diagnostic plots, multicollinearity, residual analysis, influence diagnostics)
- Nonstandard conditions (transformations, heterogeneous variance, weighted least squares)
- Maximum likelihood (principle of maximum likelihood, statistical inference via maximum likelihood, likelihood ratio tests)
- Logistic regression (ungrouped versus grouped data, interpretation, maximum likelihood estimation)
- Poisson regression (maximum likelihood estimation, interpretation, goodness of fit)
- Survival analysis (life tables, Kaplan Meier estimator, log-rank test, proportional-hazards (Cox) model, graphical examination of model assumptions)
PREREQUISITES AND OFFERINGS
BMTRY 700 or instructor consent. Typically offered each spring semester.