Introduction to Resampling Methods

Introduction to Resampling Methods

taught by Peter Bruce and Robert LaBudde

Aim of Course:

The online course, "Introduction to Resampling Methods" introduces the basic concepts and methods of resampling, including bootstrap procedures and permutation (randomization) tests. The approach of the course is to teach inference: interval estimation, one- , two- and k-sample comparisons, correlation, regression, from a resampling perspective, without complex theory, mathematics or confusing statistical notation. A companion course, Bootstrap Methods, covers the bootstrap with more theory and in greater detail.

See:  Bootstrap Methods

Course Program:

WEEK 1: The Resampling Approach to Inference

  • Historical perspective
  • Hypothesis tests vrs. confidence intervals
  • Permutation tests
  • The bootstrap
  • Virtues of a "naive" do-it-yourself approach
  • The 4-step process
    • Specify population(s)
    • Specify resampling procedure
    • Calculate statistic or estimate of interest
    • Repeat and keep score
  • Working with measured data

WEEK 2: Working with Count Data

  • The contingency table
  • Choice of test statistics
  • Fisher's Exact Test
  • Chi-Square Test
  • Dose-Response Relationship

WEEK 3: Working with Multivariate Data

  • Correlation
  • Regression


Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

In addition to assigned readings, this course also has supplemental readings available online, discussion tasks, and example software codes.

Introduction to Resampling Methods

Who Should Take This Course:
Analysts with data or statistics not suitable for standard analysis (small sample sizes, for example, or non-standard statistics), analysts who have had some statistics and want to deepen their knowledge of statistical inference, statisticians unfamiliar with resampling seeking a basic introduction, instructors interested in the easy-to-understand, non-formula-based resampling approach.
You should be familiar with introductory statistics.  Try these self tests to check your knowledge.
Organization of the Course:

This course takes place online at the Institute for 3 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:
About 15 hours per week, at times of  your choosing.

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
  4. Digital Badge - Courses evaluated by the American Council on Education have a digital badge available for successful completion of the course.  
  5. Other options - Specializations, INFORMS CAP recognition, and academic (college) credit are available for some courses
Course Text:
The required text for this course is Resampling: The New Statistics by Julian Simon, available online here.
Course illustrations and exercises will be provided in R, and also in Resampling Stats for Excel.  Click Here for information on obtaining a free (or nominal cost) copy of various software packages for use during the course.


March 06, 2020 to March 20, 2020

Introduction to Resampling Methods


March 06, 2020 to March 20, 2020

Course Fee: $469

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Email jdobbins "at" to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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