Mathematical Biostatistics Boot Camp
Brian Caffo
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 necessary, but not required.Sign Up
Next session: 24 September 2012 (7 weeks long)
Workload: 3-5 hours per week
About the Course
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.
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.
About the Instructor(s)
Brian Caffo, PhD is an associate professor in the Department of Biostatistics at the Johns Hopkins University Bloomberg School of Public Health. He graduated from the Department of Statistics at the University of Florida in 2001. He works in the fields of computational statistics and neuroinformatics and co-created the SMART (www.smart-stats.org) working group. He has been the recipient of the Presidential Early Career Award for Scientist (PECASE) and Engineers and Bloomberg School of Public Health Golden Apple and AMTRA teaching awards.Course Syllabus
The goal of this course is to equip biostatistics and quantitative scientists with core applied statistical concepts and methods:
- Students will learn basic mathematical biostatistics including probability distributions and their properties.
- Students will learn the basics of statistical likelihood.
- Students will learn the basics of confidence intervals.
- The course will introduce students to the display and communication of statistical data. This will include graphical and exploratory data analysis using tools like scatterplots, boxplots and the display of multivariate data.
Recommended Background
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.
Suggested Readings
Course Format
This course consists of lectures and homework assignments.
FAQ
- Is calculus really necessary for this class?Yes.
- What resources will I need for this class?Please download and install the R statistical programming language.
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