Statistics 568: Design and Analysis of Experiments
- Course information, assessment procedures,
etc. See also the legal stuff to
be included with outlines.
- The textbook is Experiments:
Planning, Analysis and Optimization (2nd edition), by C.F.
Jeff Wu and Michael S. Hamada.
notes. These are constantly under revision – both before and after
- Source material on
Computer Experiments: JSM 2006 presentations kindly provided by Thomas Santner and William Notz; Statistical
Science review article by Sacks, Welch,
Mitchell & Wynn. Also material by Devon Lin and BoxinTang on Latin
Hypercubes and Space-Filling Designs.
- Source material on
Optimal Design: Optimal
Designs for Non-Linear Models: Introduction and Historical Overview,
of Design, chapters (by D. Wiens) of an upcoming Handbook of Design
and Analysis of Experiments; A
Basis for the Selection of a Response Surface Design (Box & Draper
1959) and Robust
Discrimination Designs (D. Wiens 2009).
- It is assumed that
students have successfully completed courses in Design at the level of STAT368,
Regression at the level of STAT 378, Mathematical Statistics at the level
of STAT 372 and Mathematics at the level of STAT
- Academic integrity – see
the entry in course information, and in
particular see “Don’t
and using the R package:
- For a quick start you
can obtain R here and
install it on a PC by double-clicking on this .exe file and following the
instructions. There is a Mac version of R too; you'll need to consult the
'R home page' link below for this.
- What is R?
- The R home page
- R manuals: small, medium, big.
- A nice guide to anova (see ch. 16 in
particular) on R.
and code for R examples discussed in class: You should find it useful to
download and run these R scripts before the classes in which they are to
used in the text (all are here as a .zip file)
- Intro to R
- Intro to regression on R (lecture 1)
- Pulp brightness experiment, single factor
design and analysis (lecture 3)
- Torqueing bolts experiment, two factor
design and analysis (lecture 6)
- Wood experiment, split plot design and
analysis, and starch experiment, ancova
analysis (lecture 8)
- Epitaxial experiment, 2^4
factorial (lectures 9 – 11)
- Leaf spring experiment,
2^(5-1) fractional factorial (lectures 12 – 14)
- Seatbelt experiment,
3^3 and 3^(4-1) designs (lectures 15, 16)
- Simulated response surface exploration
- Epitaxial growth
parameter design (lectures 19, 20)
- Algorithmic construction
of a D-optimal design for SLR
- The importance of
clarity of exposition, and of grammatical correctness, in technical
writing cannot be over-emphasized. The best way to determine if you
understand what you are doing is to try to write it down in a form that
another reader can understand. Some comments along these lines are
included with the course information; for
some helpful resources see "Writing Aids" on my homepage.
Statistics resources at U of A:
Statistics Centre Home Page
of Doug Wiens
of Mathematical and Statistical Sciences Home Page