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Department: Biostatistics
Term: 2nd term
Credits: 3 credits
Contact: Brian Caffo
Academic Year: 2012 - 2013
Course Instructor:

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 Objective(s):
Upon successfully completing this course, students will be able to:
Apply the theories to standard experimental designs
Discuss and estimate variance components
Discuss theory and application of linear mixed models
Discuss the concept of best linear unbiased estimation and prediction
Develop the theory of restricted maximum likelihood
Discuss shrinkage estimation

Methods of Assessment: Student evaluation based on homework and a final exam.
Location: East Baltimore
Class Times:
  • Tuesday 10:30 - 11:50
  • Thursday 10:30 - 11:50
Enrollment Minimum: 10
Instructor Consent: No consent required


Auditors Allowed: Yes, with instructor consent
Grading Restriction: Letter Grade or Pass/Fail