STAT 5303: Designing Experiments
Spring Semester 2017
Here is the course information handout.
Official University policies on many issues are available
Ford Hall 332
|Monday: || || 2:00 p.m. to 3:00 p.m.
|Wednesday: || || 2:00 p.m. to 3:00 p.m.
|Friday: || || 2:00 p.m. to 3:00 p.m.
Additional times are available by appointment, and I usually have extra office hours before tests.
Some good news and some bad news:
The bad news is that the textbook is out of print so it is not readily available from bookstores, although you may find a used copy somewhere.
The good news is that the author (Prof. Oehlert) has generously made the book available as a no-cost download. Go to
for links to the PDF file for the book itself and datafiles for examples and exercises from that book.
You can also buy a hard copy version of that PDF file from Paradigm Copies.
Assignment #9 (due Tuesday, April 4):
P14.6 Just do parts b, c, and d; omit part a.
Assignment #8 (due date changed to Tuesday, March 28):
Assignment #7 (due Tuesday, March 21):
E12.1, E12.2, and P12.1: Just do the first of the "standard five questions"---draw the Hasse diagrams. Show model terms, indicating which are fixed and which are random, and which are nested and which are crossed; you don't need to find test denominators or expected mean squares.
Assignment #6 (due Tuesday, March 7):
E11.1: Find point estimates, but do not find confidence intervals,
E11.2 (a) and (b): For part (b) just find point estimates, but do not find confidence intervals,
E11.5: Find point estimates for both "between" and "within" variance components, but don't find confidence intervals.
Do this problem twice. The first time, do it exactly as written in the textbook. Each individual participates in two sessions, so n = 2, and you need to find how many individuals are needed so power will be 0.9.
The second time, suppose that each individual participates in three sessions, so n = 3. Now find the required number of individuals for power to be 0.9.
Assignment #5 (due Friday, February 24):
E8.1, E8.2, P8.4, P8.5
Add E10.4 and P10.7 to this assignment.
Assignment #4 (due Friday, February 17):
E7.1, E7.2, E7.3, E7.4, E7.5.
Assignment #3 (due Friday, February 10):
Assignment #2 (due Friday, February 3):
Assignment #1 (due Friday, January 27):
P3.1 (For this problem don't do the analysis---just identify the experimental units and the treatments.)
These handouts (written by Prof. Oehlert) demonstrate using R to work through examples; many are from the text but there are additional examples.
As you work through them, remember you want to see both
(1) how to get the computer to perform certain tasks for you, and
(2) why you want the computer to do those tasks.
In other words, you should learn something about the underlying statistical concepts in addition to learning how to use the computer.
Here is a handout with examples showing how to use R to perform randomization tests.
This R handout uses the resin lifetime data set (Example 3.2 in the text) to demonstrate analysis of a completely randomized design, and also discusses dose-response polynomial modeling.
Our analysis procedures depend on certain assumptions. Some violations of those assumptions are not too serious, but others can cause problems.
This handout discusses how you can detect problems and what you can do about them.
This handout shows how to use R to find power and sample size for the ANOVA F test.
This handout shows how to use R to analyze data from experiments where the treatments are combinations of factors at various levels.
R can also deal with two-series factorials.
Here is a random effects handout. The R command we will use for these models is lmer() (pronounced "elmer") which is part of the "lme4" package. We will also use some functions from the Stat5303 package to perform additional analyses after running lmer().
Here are data files for the calves and resistors examples discussed in the random effects handout.
Here is information about nesting and mixed effects in R and
here is a data file for the particle counts example discussed in this handout.
Here is information about
restricted mixed models in R and here is background information about REML.
Here is a data file for the cheese tasting example discussed in the restricted models handout.
Here is an example of a randomized complete block design in R.
We can analyze Latin square designs in R.
This handout also shows how to deal with an outlier in the data by using a dummy variable to effectively remove that observation from the dataset.
However, doing that also destroys balance, so we need to consider the order in which terms are entered in the model in R. Examples are presented in the handout.
Of course, there is a handout for balanced incomplete block designs.
Basic R information
We will use R.
Here is an introduction to R.
You may download R for Macintosh, Windows, and Linux from the
R package Stat5303
We will use Prof. Oehlert's R package called Stat5303, which
adds some extra commands that we will need.
The current version of this package is here.
You will need to download and install two packages, but they will require several other packages, too, so it may take a while. Fortunately, you only need to do that once.
Then each time you start R you will need to use
to make the extra commands available in that R session.
This is as much a list of what the project is not as it is a list of what it is.
- There is more information in the text---see chapter 20.
- The project involves a designed experiment, not an observational study or a survey.
- You need to clearly identify experimental units and treatments and, if appropriate, blocking factors and measurement units.
- Describe how you use randomization in your experiment.
- You should analyze the data using methods we have covered in this course, such as ANOVA for a continuous response.
- Remember that we've studied comparative experiments, so you probably want to examine differences between treatments.
- No human subjects, nor anything else that would require review board approval.
- You need to propose a question, plan an experiment to answer that question, perform that experiment, collect and analyze the data, and write a final report covering all of that.
- Remember to include in both the proposal and the final report enough background information so someone who is not trained in your area of expertise can understand what you're trying to do and why someone would want to do it.
- This has to be a new experiment; you cannot just take some old data from work you did before and reanalyze that.
- This is to be an actual experiment, and not just a computer simulation.
- The proposal is not like a contract, but it needs to be specific enough so I can tell tell if what you're planning fits within these guidelines.
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