This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
Statistics is a thriving discipline that provides the fundamental language
of all empirical research. Biostatistics is simply the field of statistics
applied in the biomedical sciences.
This course puts forward key mathematical and statistical topics to help students understand biostatistics at a deeper level. After completing this course, students will have a basic level of understanding of the goals, assumptions, benefits and negatives of probability modeling in the medical sciences. This understanding will be invaluable when approaching new statistical topics and will provide students with a framework and foundation for future self learning.
Topics include probability, random variables, distributions, expectations, variances, independence, conditional probabilities, likelihood and some basic inferences based on confidence intervals.
Developed in collaboration with Johns Hopkins Open Education Lab.
Knowledge of calculus, set theory and a moderate level of mathematical literacy are prerequisites for this class. A small amount of programming is useful, but not required.
This course consists of lectures and homework assignments.
Is calculus really necessary for this class?
What resources will I need for this class?
Please download and install the R statistical programming language.