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Advanced Methods in Biostatistics II

East Baltimore
2nd term
4 credits
Academic Year:
2022 - 2023
Instruction Method:
Class Times:
  • Tu Th,  10:30 - 11:50am
Lab Times:
  • Tuesday,  9:00 - 10:20am
Auditors Allowed:
Yes, with instructor consent
Undergrads Allowed:
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Martin Lindquist



Surveys basic statistical inference, estimates, tests and confidence intervals, and exploratory data analysis. Reviews probability distributions and likelihoods, independence and exchangeability, and modes of inference and inferential goals including minimizing MSE. Reviews linear algebra, develops the least squares approach to linear models through projections, and discusses connections with maximum likelihood. Covers linear, least squares regression, transforms, diagnostics, residual analysis, leverage and influence, model selection for estimation and predictive goals, departures from assumptions, efficiency and robustness, large sample theory, linear estimability, the Gauss Markov theorem, distribution theory under normality assumptions, and testing a linear hypothesis.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Apply the theories to standard experimental designs
  2. Discuss and estimate variance components
  3. Discuss theory and application of linear mixed models
  4. Discuss the concept of best linear unbiased estimation and prediction
  5. Develop the theory of restricted maximum likelihood
  6. Discuss shrinkage estimation
Methods of Assessment:

This course is evaluated as follows:

  • 50% Homework
  • 15% Midterm
  • 35% Final Exam

Instructor Consent:

Consent required for some students

Consent Note:

Consent required for students other

than Biostatistics 1st-year PhD


For consent, contact:

Special Comments:

Please note: This is the onsite section of a course that is also offered virtually. Students will need to commit to the modality for which they register.