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Department: Biostatistics
Term: 1st term
Credits: 3 credits
Contact: Brian Caffo
Academic Year: 2013 - 2014
Course Instructor:

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.

Learning Objective(s):
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

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
Enrollment Restriction: Biostatistics 1st-year PhD students.
Instructor Consent: Consent required for all students

Consent required for students other than Biostatistics 1st-year PhD students.

For consent, contact:

140.673-674 & elementary course in matrix algebra; students must also register for 140.752

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