Introduction to Design and Analysis of Experiments
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Professor Douglas Wiens
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Course
materials:
- Course information, assessment procedures,
etc.
- Tentative course outline, allowing you to keep up
with the readings.
- Lecture
notes.
Assignments:
Due dates are on the course outline. Solutions will be posted in the glass case at the north end of the 4th floor of CAB. The relevant pages of the text are scanned and included below, for those of you who don't have the current edition.
- Assignment
2 and scans.
- Assignment
3 and scans.
- Assignment
4 and scans.
Exams:
- Sample Midterm Exam and additional review
questions. The midterm exam covers lectures 1 - 13. Some review notes are here.
- Sample Final Exam. The final exam covers the
entire course. Some review notes are here.
Examples
of projects:
- A project carried out by
STAT 368 students Angie Chiu,
Dan Metes and Aaron Sauve.
- A sample project.
- The author of the text
has a Student
Companion Site, containing a wealth of additional material including
examples of projects submitted in his Industrial Engineering course.
Technical
Writing:
- 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.
Obtaining
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
- An R manual
- A
brief introduction to R
- Matrix
operations in R
R examples worked in class: You should
find it useful to download and run these R scripts before the classes in
which they are to be discussed.
- R commands for the paired t-test using the 'cement' data (Lecture 4)
- R commands for the one-way ANOVA using the 'tensile strength' data
(Lectures 5 - 8)
- R commands for the RCBD using the 'hardness' data (Lectures 9, 10)
- R commands for the Latin Square design using the 'hardness' data (Lectures
11, 12)
- R commands for the BIBD using the 'hardness' data (Lecture 13)
- R commands for the two factor factorial design using
the 'battery' data (Lectures 14, 15)
- R commands for Tukey's test for non-additivity (Lecture 16)
- R commands for the 3x2x2 factorial design using the
'softdrink bottling' data (Lecture 16)
- R commands for the 2^2 factorial design using data from
Section 6-2 (Lecture 17)
- R commands for the unreplicated factorial design using
data from Table 6-10 (Lectures 18, 19)
- R commands for the 2^4 factorial design in 2
blocks example (Lectures 19, 20)
- R commands for the 2^4 factorial design in 4
blocks example (Lecture 21)
- R commands for the partially confounded factorial
design example (Lecture 22)
- R commands for the fractional factorial design example
(Lectures 23, 24)
- R commands for the random effects CRD model with a single factor example
using data from Table 13-1 (Lecture 25)
- R commands for the two random factors factorial
example using data from Table 13-3 (Lecture 26)
- R commands for the two mixed factors factorial
example using data from Table 13-3 (Lecture 27)
- R commands for the two-stage nested design example using data in Problem
14-2 (Lecture 28)
- R commands for the nested factorial design example using data
in Example 14-2 (Lecture 29)
- R commands for the split-plot design example using data in Table 14.14
(Lecture 30)
- R commands for the unbalanced RCBD example (Lecture 32)
- R commands for the 2^3 factorial by regression
example (Lecture 32)
- R commands for the analysis of covariance example using data in Table
15-10 (Lecture 33)
Statistics resources at U of A:
- Several of our courses,
including this one, can be used to obtain accreditation as a
practising statistician in Canada.
Department
of Mathematical and Statistical Sciences Home Page