STAT 5303: Designing Experiments
Spring Semester 2014

Grades have been submitted. They should be available soon at OneStop.

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 and hence 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.


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 Stat5303 package depends on certain other R packages:
car (version 2.0 or later), effects, FrF2, mvtnorm, perm, RLRsim, and tseries.
You should install these and all the packages they depend on.

!!NEW!! The new version of this is actually a pair of packages; one is cfcdae (Companion to a First Course in Design and Analysis of Experiments) and the other is Stat5303libs. Install both packages.

Companion (cfcdae) R package (0.8-2) for Mac

Companion (cfcdae) R package (0.8-2) for Windows

Stat5303libs R package (0.7-3) for Mac

Stat5303libs R package (0.7-3) for Windows

Download the packages and save the files into a place where R can find them (e.g., your home directory or the desktop). Start R, set the working directory to that location (e.g., use setwd(), and then use

(The repos=NULL says not to find it online but to look for the package in the local files, and replace XXX by tgz or zip as appropriate.) Then use
to install the other package.

When you start R you then use two library commands: library(cfcdae) and library(Stat5303libs); you do not use library(Stat5303) for the new version.


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.
For Macintosh/Linux oehlert_1.02.tar.gz
For Windows
Download the package and save the file into a place where R can find it (e.g., your home directory or the desktop).
Start R, set the working directory to that location (e.g., use setwd(), and then use

(The repos=NULL says not to find it online but to look for the package in the local files; you need to replace XXX by tar.gz or zip as appropriate.)
Once the package is installed, you can do
from within R to load all of the data. At that point, the command
should give you problem 4 from chapter 17.

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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