140.751.01 | AY 2013-2014 - 1st 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:

    • Review key concepts in linear algebra
    • Lise random vectors and matrices
    • Develop the least squares approach for linear models
    • List projections in vector spaces
    • Discuss the connection between least squares and maximum likelihood approaches
    • Discuss estimability, and in particular, the Gauss Markov theorem
    • Discuss the distribution theory under normality assumptions
    • Compare least squares to generalized least squares
    • Describe the concept of testing linear hypothesis
    • Compare approaches to calculate simultaneous confidence intervals
  • Course Description
    Introduces students to applied statistics for biomedical sciences. Illustrates the motivations behind many of the methods explained in 140.752-756. Focuses on analyzing data and interpreting results relevant to scientific questions of interest. Presents various case studies in detail and provides students with hands-on experience in analyzing data. Requires students to present results in both written and oral form, which in turn requires them to learn the software package R and a handful of statistical methods. General topics covered include descriptive statistics, basic probability, chance variability, sampling, chance models, inference, and regression.
  • Intended Audience
    Biostatistics PhD students
  • Methods of Assessment
    Student evaluation based on homework and a final exam.
  • Prerequisites
    140.673-674 & elementary course in matrix algebra; students must also register for 140.752
  • 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.

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