Difference between revisions of "Design and analysis of experiments"

Revision as of 11:21, 4 January 2016

This is a work in progress. It will be completed by the end of the day on 04 January 2016.

Learn the basic terminology of experiments: responses, factors, outcomes, real-world units vs coded units, confounding
Analyze and interpret data from an experiment with 2, 3 or more factors by hand
Use R to do the analysis, and interpret the various plots, such as the Pareto plot
Analyze and interpret data from an experiment with 3 or more factors
Recognize when to use a fractional factorial, to go mostly the same results as from a full factorial
Understand when to use screening experiments
Use the concepts of response surface methods to systematically reach an optimum
What you can do when you make a mistake, or hit against constraints.

Don't accept what is often believed to be true: always experiment.
In case you've been wondering how the trade-off table was constructed.
You must cover steps 16, 21 and 22 of the detailed software tutorial to master this section of the course.

@@ Line 14: / Line 14: @@
 == Extended readings/practice ==
-* R
+* Don't accept what is often believed to be true: [http://arstechnica.com/science/2015/01/orchid-mantis-astonishing-camouflage-isnt-especially-orchid-like/ always experiment].
+* In case you've been wondering how the [http://www.jstor.org/discover/10.2307/1267548 trade-off table was constructed].
+* You must cover steps 16, 21 and 22 of the detailed [[Software_tutorial| software tutorial]] to master this section of the course.
 == Resources ==