Advanced Methods in Biostatistics I
- East Baltimore
- 1st term
- 3 credits
- Academic Year:
- 2019 - 2020
- Class Times:
- Tu Th, 10:30 - 11:50am
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:
- 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.
- Enrollment Restriction:
Biostatistics 1st-year PhD students.
- Instructor Consent:
Consent required for some students
- Consent Note:
Consent required for students other than Biostatistics 1st-year PhD students.
- For consent, contact: