Official University policies on many issues are available here.
|Monday (5/13):||11:00 a.m. to 2:00 p.m.|
|Tuesday (5/14):||10:30 a.m. to 12:30 p.m.|
|Wednesday (5/15):||11:00 a.m. to 12:00 p.m.|
|Thursday (5/16):||10:30 a.m. to 12:00 p.m|
|Friday (5/17):||10:00 a.m. to 11:30 a.m|
|Saturday (5/18):||approximately 2:30 p.m. to 3:30 p.m. in Ford 367 and 3:40 p.m. to 4:00 p.m. in Rapson 45|
The exam is open book and open notes, but only physical books or notes are permitted. No electronic books or notes are allowed.
Bring your printed project report to hand in with your final exam.
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.
Assignment #12 (due Thursday, May 9):
Assignment #11 (due Thursday, May 2):
Assignment #10 (due Thursday, April 25):
Assignment #9 (due Thursday, April 11):
P14.6 Just do parts b, c, and d, but omit part a
Assignment #8 (due Thursday, March 28):
E12.1 and E12.2: 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 #7 (due Thursday, March 14):
E11.1 find point estimates, but do not find confidence intervals,
E11.2 (a) and (b), but 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.
Assignment #6 (due Thursday, March 7):
E10.4 and P10.7.
Assignment #5 (due Thursday, February 28):
E8.1, P8.2, P8.3, and P8.7.
Assignment #4 (due Thursday, February 21):
E7.2, E7.3, and P7.2.
Assignment #3 (due Thursday, February 14):
E4.2, E4.4, P4.2, P4.3, E6.2, E6.3, E6.5, and P6.1.
Assignment #2 (due Tuesday, February 5):
E3.3, E3.4, and P3.2.
Assignment #1 (due Tuesday, January 26):
E2.1, E2.5, and P3.1 (For this problem don't do the analysis---just identify the experimental units, treatments, and measurement units).
We will use R. You may download R for Macintosh, Windows, and Linux from the "CRAN" link on the R-Project home page.
Here is information about R and here is more information about R.
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.
Stat5303 R package (0.7-1) for Mac
Stat5303 R package (0.7-1) 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
install.packages("Stat5303_0.7-1.XXX",repos=NULL)(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.)
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.
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".
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.
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 oehlert_1.02.zip
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
install.packages("oehlert_1.02.XXX",repos=NULL)(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.)
library(oehlert)from within R to load all of the data. At that point, the command
pr17.4should 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.
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.
Comments? Questions? Send me an e-mail note: firstname.lastname@example.org