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Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

In this class students will learn the fundamentals of statistical inference. Students will receive a broad overview of the goals, assumptions and modes of performing statistical inference. Students will be able to perform inferential tasks in highly targeted settings and will be able to use the skills developed as a roadmap for more complex inferential challenges.


4 weeks

3-5 hours per week

Part time/ Full time: – Part time

Fees: Join for Free

Important Date:

Course Start Date: 31-Oct-2016

Interested people must have knowledge of R programming and mathematical aptitude.

  • Brian Caffo – Johns Hopkins University
  • Jeff Leek – Johns Hopkins University
  • Roger D. Peng – Johns Hopkins University
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