Course Description

Statistical analyses are a fundamental component of experimental design in many biomedical research fields. Particularly when working with large, messy data, proper understanding of statistics is essential to perform proper statistical analyses. This course will build on students' existing knowledge of statistics to help them expand their analysis toolkits and will cover topics including modeling, bootstrapping, simulations, imputation, and basic machine learning. Students will attend lectures to gain theoretical understanding of topics before applying concepts through practice problems and projects using the R programming language. Note that students should have basic proficiency in R and simple statistical analyses before enrolling in this course to be successful.

Learner Outcomes

By the end of this course, students should be able to:

  • Fit and interpret multiple regression models including interaction and non-linear terms
  • Fit and interpret logistic regression models
  • Evaluate model fit to perform model selection
  • Perform bootstrapping and simulation analyses to quantify statistical confidence
  • Fit basic machine learning models
  • Understands the strengths and weaknesses of different machine learning models.
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Section Title
Statistics for Biomedical Researchers
Online Asynchronous
Jun 15, 2022 to Aug 02, 2022
Delivery Options
Fee (Includes Tuition and Technology Fee)
Eligible Discounts Can Be Applied at Checkout credit (2 Credits) $625.00
Potential Discount(s)
Available for Academic Credit
2 Credit(s)
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