Fall 2012
SYLLABUS and other useful information
Lectures:  MWF 99:50am, Room 2272 Gilman (section F) Available anytime though the class web site or Blackboard (section XW). Also accessible to those in section F, but I recommend coming to class if you're in Ames. Will be posted on the class web site by noon each lecture day 
Laboratory:  W 2:10  4 pm, Room 2272 Gilman (Section F) Will be posted by noon Thursday on the class web site and Blackboard (section XW) 
Instructor:  Philip Dixon
pdixon at iastate dot edu 2121 Snedecor Hall
5152942142

Office Hours:  Thursday 910 am and 45 pm 
TA/grader  Senniang Chen, 3211 Snedecor, 5152946609, snchen at iastate dot edu 
TA office hour:  Thursday, 11am  noon 
Questions::  Please feel free to email ( pdixon at iastate dot edu or snchen at iastate dot edu ) anytime with questions or comments. 
Text:  Ramsey, F.L. and Schafer, D.W., 2001/2012. The Statistical Sleuth, Either the 2nd ed. or the 3rd ed. is acceptable this year. Duxbury 
SAS info: (all optional) 
Elliot, R. J., 2000. Learning SAS in the Computer
Lab. 2nd ed. Duxbury Delwiche, L. D. and Slaughter, S. J. 1998. The Little SAS book, 2nd ed. SAS Institute Press 
Goals:

1) Understand variation and its consequences for drawing conclusions
from data.
2) Be familiar with some standard statistical methods: when and how to use them how to interpret statistical results. 3) Be able to apply statistical principles to novel problems. This class emphasizes the appropriate analysis of experimental data. I presume you will be using class material within the next year. If it will be two or three years before you analyze data, I suggest you delay taking 401. 
Grading:  Weekly Homework: 120 pts
Two Midterms: 100 pts each Final: 130 pts 
Course Outline  (proposed): 
Week  Dates  Chapter  Topic 
1  Aug 20  24  1  Types of studies, Statistical Inference,
Data summary 
2  Aug 27  31  2  Comparison of two groups:
Hypothesis tests 
Sep 3, Labor Day  No class  
3  Sep 57  2  Confidence Intervals 
4  Sep 1014  4  Nonparametric methods 
5  Sep 1721  3  Assumptions and robustness 
6  Sep 24  28  5  Comparison of multiple groups 
7  Oct 1  5  6  Linear combinations and multiple comparisons 
Oct 8  MIDTERM I due at start of class (9am)  
8  Oct 8  12  6  False Discovery Rate, Choosing a method 
9  Oct 15  19  7, 8  Linear regression 
10  Oct 22  26  8, 9  Lack of Fit, Correlation, Multiple Regression 
11  Oct 29  Nov 2  9, 10, 11  Multiple regression (cont.) 
12  Nov 5  9  12  Model selection 
Nov 12  MIDTERM II due at start of class (9am)  
13  Nov 12  16  13,14  Twoway ANOVA (intro) 
Nov 19  23  Thanksgiving break, no class  
14  Nov 26  30  18, 19  Contingency tables 
15  Dec 5  9  20, 21  Logistic regression 
Dec 14  (tentative) Final exam due by 5pm  
Details:
Sections of 401  The different sections of 401 are not interchangeable. Each is essentially
a different course, except sections A and F, which have the same audience.
Section F focuses on the analysis of data from experimental studies, although we do briefly discuss observational studies. It will use examples relevant to the target audience (agriculture and biology). If there are substantial numbers of online students from other specific fields, I will include examples relevant to their field(s) where possible. Section C is for students in the physical sciences, math, and engineering. It includes more mathematical detail. Sections D and E are for students in social sciences and education. Section G is for undergraduates in all fields. 
Student background:  Section F is intended for graduate students working in agriculture
or the biological sciences, broadly interpreted.
The prerequesite (Stat 101, 104, 105, or 226) is enforced for
undergraduates; it is waived for graduate students. The material I cover is intended for graduate students who will be analyzing data from their own experimental studies within a year of taking the class. Others are welcome but be aware that I have graduatelevel expectations. I use a graduatelevel grading scheme (mostly A's and B's) but I reserve the right to give lower grades when appropriate. Students in the online section are more diverse because XW is the only section taught online in the fall. However, I teach section XW concurrently with section F, so the online section will have the same homework assignments, exams, expectations and grading scheme. If you are in education, you may want to take the online Stat 401 taught next spring by Dr. Mac Shelley. The Spring semester online section is oriented towards students in education and the social sciences. 
Text:  Each chapter includes two case studies, main material and
a section of related issues. Please skim the case studies and read the main
material in the assigned chapter(s) prior to the start of the
lectures. In some chapters, parts of the related issues will also be
assigned. These will be announced in class.
My lectures will cover the same concepts, but I will often use different examples and may use a different presentation. There is not time to lecture on all the details. I expect you to read the assigned material and ask questions on anything you don't understand. It will probably help to reread the chapter(s) after the relevant lectures. Through the semester, I will distribute a reading list identifying the most important parts of each chapter. 
Lab:  Lab time will be used for four different activities:
Some handson illustrations of statistical principles. Use of SAS Return HW (section F) Discussion and Q/A on lectures and homework problems. 
Homework:

Homework assignments will be posted on the web site and
announced in class.
Goal is to provide practice using statistical concepts. Discussion with friends and classmates is strongly encouraged. Please write up your answers individually. Copying papers is not a good way to learn and will not be tolerated. No late homework accepted. Lowest homework score will be dropped. Solutions will be posted on the class web page soon after the due date. Homework for both sections F and XW will be due Friday at 9am. 
Major data analyses: 
Twice during the semester, the homework assignment will be a data analysis problem. You will be provided a description of a study and the data. You will analyze the data in an appropriate manner and write a short report, organized as sections of a scientific paper. Again, you are encouraged to work together on the analysis but each of you is to write your own report. 
Computing:

This class focuses on statistical concepts, not details of a specific
computing package.
I will use SAS in class; we will teach the basics of SAS in lab. SAS is available for Windows, Mac, and UNIX. It also available by remote terminal server. You may use another package if your lab group uses something other than SAS. Please check with me to make sure that package is appropriate for this class. EXCEL is not appropriate. R is very useful but many parts of the ANOVA material are hard to do in R. We will provide help for SAS and may be able to help with other packages. If you plan on taking Stat 402, I strongly recommend you learn
SAS now.
Many other packages can do the analyses we need for 401. Only
SAS can do some of the analyses we need in 402.

Exams:  Exams for both sections will be take home exams. I will give you descriptions of
studies, data, and ask you to answer some questions. You will be expected to use
the computer. The exams are open notes and open book. You are to work individually but the TA and I are very willing
to answer questions about code
and help you fix computing problems.
My goal is to see how well you can use class material to analyze data.
Both sections have the same due date and time.
Makeup exams will be given only if you contact me and get approval prior to the scheduled exam. 
Other
questions: 
Please ask in class or email me: pdixon at iastate dot edu 
Disability accommodation: 
Iowa State University complies with the Americans with Disabilities Act and Sect 504 of the Rehabilitation Act. If you have a disability and anticipate needing accommodations in this course, please contact Philip Dixon within the first two weeks of the semester. Retroactive requests for accommodations will not be honored. Before meeting with me, you need to obtain a SAAR form with recommendations for accommodations from the Disability Resources Office, Room 1073, Student Services Building. Their telephone number is 5152947220. 
Academic honesty policy 
The ISU academic honesty policy is printed in the University
catalog and is available
online. To clarify how this applies to
your work in this class: On homework assignments: I encourage you to help each other interpret the problems, write code, debug code, and interpret the output. You may share code, but I encourage you to understand that code even if you didn't write it. You are required to write your answers in your own words. On exams: You are to do all work individually. 