Workshop Objective
This course is designed to introduce participants to Bootstrapping and Bootstrapping methods. The course takes a graphical approach to bootstrapping and permutation testing, illuminating basic statistical concepts of standard errors, confidence intervals, p-values and significance tests.
Workshop Content
This course will consider a variety of statistics (mean, trimmed mean, regression, etc.), and a number of sampling situations (one-sample, two-sample, stratified, finite-population), stressing the common techniques that apply in these situations. We'll look at applications from a variety of fields, including telecommunications, finance, and biopharm.
Bootstrapping and permutation methods let us do confidence intervals and hypothesis tests when formulas are not available. This lets us do better statistics, e.g. use robust methods (we can use a median or trimmed mean instead of a mean, for example), they can help clients understand statistical variability, and some of the methods are more accurate than standard methods.
Workshop Outline
- Introduction to Bootstrapping
- General procedure
- Why does bootstrapping work?
- Sampling distribution and bootstrap distribution
- Bootstrap Distributions and Standard Errors
- Distribution of the sample mean
- Bootstrap distributions of other statistics
- Simple confidence intervals
- Two-sample applications
In particular, participants will learn how to use resampling methods:
- to compute standard errors,
- to check the accuracy of the usual Gaussian-based methods,
- to compute both quick and more accurate confidence intervals,
- for a variety of statistics and for a variety of sampling methods, and
- to perform significance tests in some settings.
Target Audience and Availability
This full-day or half-day course would be of interest to computer scientists working with data, ...
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NISS can help you facilitate this course at your institution. Please write to us at officeadmin@niss.org or call us at (202) 800-3880.