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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)


BMTRY 700 or instructor consent. Typically offered each spring semester.


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