Mathematical Biostatistics Boot Camp 2

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Course Date: 04 August 2014 to 22 September 2014 (7 weeks)

Price: free

Course Summary

Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.


Estimated Workload: 3-5 hours/week

Course Instructors

Brian Caffo

Brian Caffo, PhD is a 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 Description

This class presents fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples. Students having taken this class should be able to summarize samples, perform relevant hypothesis tests and perform a collection of two sample comparisons. Classical non-parametric methods and discrete data analysis methods are discussed.  The class is taught at a master's of biostatistics introductory level and requires Mathematical Biostatistics Boot Camp 1 as a prerequisite.

FAQ

Will I get a Statement of Accomplishment after completing this class?

Yes. Students who achieve a sufficient grade will receive a Statement of Accomplishment signed by the instructor.

What resources will I need for this class?

RStudio and R

Syllabus

  • Hypothesis Testing
  • Power and sample size and two group tests
  • Tests for binomial proportions
  • Two sample binomial tests, delta method
  • Fisher's exact tests, Chi-squared tests
  • Simpson's paradox, confounding
  • Retrospective case-control studies, exact inference for the odds ratio
  • Methods for matched pairs, McNemar's, conditional versus marginal odds ratios
  • Non-parametric tests, permutation tests
  • Inference for Poisson counts
  • Multiplicity

Format

This course consists of video lectures, weekly homework assignments, discussion forums, and weekly quizzes.

Course Workload

3-5 hours/week

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