ADVANCED METHODS IN BIOSTATISTICS I Syllabus
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 DescriptionIntroduces 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 AudienceBiostatistics PhD students
Methods of AssessmentStudent evaluation based on homework and a final exam.
Prerequisites140.673-674 & elementary course in matrix algebra; students must also register for 140.752
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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.
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