STAT 5303: Designing Experiments
Spring Semester 2017


Basic Information

Here is the course information handout.

Official University policies on many issues are available here.

Office Hours

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.

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.


Homework

!!NEW!! Assignment #14 (due Tuesday, May 2):

P17.1
P17.2

Assignment #13 (due Tuesday, April 25):

E10.1 (Do the version in the pdf file; do not do the version in the hardbound textbook.
E10.3
P9.2

Assignment #12 (due Tuesday, April 18):

E18.1
P18.1

Assignment #11 (due Tuesday, April 11):

P16.1

Assignment #10 (due Friday, April 7):

E15.2
P15.1

Assignment #9 (due Tuesday, April 4):

E14.2
E14.4
P14.1
P14.5
P14.6 Just do parts b, c, and d; omit part a.

Assignment #8 (due date changed to Tuesday, March 28):

P13.1
P13.2
P13.4
P13.12

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.
E12.4

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.
E11.6 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):

E5.1, P5.2, P5.3.
E6.2, E6.3, E6.5,

Assignment #2 (due Friday, February 3):

P3.2, E4.1, E4.2, E4.4

Assignment #1 (due Friday, January 27):

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

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.

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

  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.

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



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