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Advanced Methods in Biostatistics I

Course Status

1st Term
Academic Year
2022 - 2023
Instruction Method
Synchronous Online
Auditors Allowed
Available to Undergraduate
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
One Year Only

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

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 Objectives
Upon successfully completing this course, students will be able to:
  1. Review key concepts in linear algebra
  2. Lise random vectors and matrices
  3. Develop the least squares approach for linear models
  4. List projections in vector spaces
  5. Discuss the connection between least squares and maximum likelihood approaches
  6. Discuss estimability, and in particular, the Gauss Markov theorem
  7. Discuss the distribution theory under normality assumptions
  8. Compare least squares to generalized least squares
  9. Describe the concept of testing linear hypothesis
  10. Compare approaches to calculate simultaneous confidence intervals
Methods of Assessment
This course is evaluated as follows:
  • 50% Homework
  • 15% Midterm
  • 35% Final Exam
Enrollment Restriction
Biostatistics 1st-year PhD students.
Special Comments

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