# STAT 5303: Designing Experiments Spring Semester 2018

## Basic Information

Here is the syllabus.

Official University policies on many issues are available here.

### Textbook

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 site 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

### Computing handouts

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.

### Basic R information

We will use 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

`  library(cfcdae)`
and
`  library(Stat5303libs)`
to make the extra commands available in that R session.

### Data

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("http://www.stat.umn.edu/~corbett/classes/5303/RDataFiles/exmpl3.2",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.

## Project

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.

Comments? Questions? Send me an e-mail note: corbett@umn.edu

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