Resampling methods have been in use for more than three
decades now and most readers of my course notices have probably
encountered or applied it. But it had a rocky start – when
resampling first appeared on the scene, some greeted it with
skepticism and even ridicule. The idea of drawing "resamples"
from your original sample seemed somehow incestuous. The idea
stuck, and resampling became a breakthrough idea in statistics.
Learn more in my online course, "Introduction to Resampling
Methods," Feb. 25 - March 25.
Feb 11: Modeling in R
Feb 18: Multivariate Statistics
Feb 25: Introduction to Resampling Methods (more below)
Mar 18: The Bootstrap (a natural follow-on to Resampling)
“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. It is a
companion course to two other more advanced courses on
bootstrap methods and randomization tests.
The course will require about 15 hours per week; there are no
set hours when you must be online. Participants can ask
questions and exchange comments directly with me via a private
discussion board throughout the period.
A bit about me: In addition to running (and teaching at)
statistics.com, I co-developed Resampling Stats software
(used in the course) and distribute XLMiner data mining software.
I have written some on resampling, and am a co-author of
"Data Mining for Business Intelligence" (Wiley, 2nd ed. 2010).
Registration and details are here
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