STAT 5303: Designing Experiments
Fall Semester 2015

Basic Information

Here is the course information handout.

Official University policies on many issues are available here.


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 his page 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.


!!NEW!! Assignment #4 (due Tuesday, October 13):

E6.2, E6.3, E6.5.

Assignment #3 (due Tuesday, October 6):

E5.1, P5.2, P5.3.

Assignment #2 (due Tuesday, September 29):

E4.1, E4.2, E4.4

Assignment #1 (due Tuesday, September 22):

E2.1, E2.5, E3.3, E3.4,
P3.1 (For this problem don't do the analysis---just identify the experimental units and the treatments.),


Computing handouts

These handouts (written by Prof. Oehlert) demonstrate using R to work through examples from the text. 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.

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-Project home page.

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.


Because typing in data is tiresome and can be a source of errors, the data from the homework problems and examples (from the text) are available as files that R can read. There are a couple of ways to get data into R.

Individual data files

One approach is a collection of individual data files. (These are plain text files, so you can also read them with an editor and cut/paste what is needed into other programs too.) These files are set up to use the read.table() command in R.

Data sets for examples are named exmpl6.3 for example 3 from chapter 6.
Data sets for exercises are named ex3.1 for chapter 3, exercise 1, and
data sets for problems are named pr3.3 for chapter 3, problem 3.

Here are dataset files for R that you can download. So, for example---provided your machine is connected to the Internet---from within R you can use

 resindata <- read.table("",header=TRUE) 
to save the data for Example 2 from Chapter 3 as a data frame called "resindata".
You don't even have to type that in; just copy and paste and you're done.

When you are not connected to the Internet, you could use

 resindata <- read.table(file="exmpl3.2",header=TRUE) 
This would allow you to load data into R without an active Internet connection, but of course you have to already have downloaded the file to your machine.

One package

Another way to get data into R is to load an R package with all the data.

Russ Lenth at the University of Iowa has also provided two R packages that include the data sets from the book.
One package is for Mac (and Linux), the other is for Windows.

You can get these here.

Note that the data set names, variable names, and variable codings in the oehlert data package and the direct-web-accessible data files may not be the same.


This is as much a list of what the project is not as it is a list of what it is.

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