COURSE DESCRIPTION: This 8-week course will meet three times a week for combined lecture/lab sessions to introduce the basic concepts and methods of statistics. The course will be comprised of four modules: 1) Laboratory experiments; 2) Interventional studies; 3) Observational studies; 4) Big Data. Concepts include: methods of exploring, organizing, and presenting data; fundamentals of probability; foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and possibly other topics including: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.
REQUIRED MATERIALS: No Textbook Required; Computer with R freeware installed
STUDENT PREPARATION: No specific background preparation needed.
SUITABLE FOR 1ST YEAR STUDENTS: Yes
UNIQUE TRAINING OFFERED IN THIS COURSE: Students will learn the fundamental concepts of biostatistics and gain proficiency in the R programming language. No overlap with existing courses.
STUDENT ASSESSMENTS: Web based problems sets for each lecture; Take home exam after each module; 20% homework, 60% exams (4 exams), 20% project
CREDIT HOURS: 3.0