140.752.01 | AY 2013-2014 - 2nd Term | 3 Credit(s)
TTh 10:30:00 AM
  • Contact Information
    Brian Caffo
  • Course Learning Objectives

    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
  • Course Description
    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.
  • Intended Audience
    Biostatistics PhD students
  • Methods of Assessment
    Student evaluation based on homework and a final exam.
  • Prerequisites
  • Course Schedule

    Please see the course Session for a full list of dates and items for this course.

    All pertinent course information is available at the course website

  • Academic Ethics Code

    Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.

  • Disability Support Services
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