Fall 2009
SYLLABUS and other useful information
Lecture:  MWF 1111:50am, Carver 205  
Lab/Discussion: Tu, 2:104, Carver 205  
Instructor:  Philip Dixon
pdixon@iastate.edu 2121 Snedecor Hall
5152942142
FAX: 5152944040 (shared machine, put my name on front page) 

Office Hours:  On campus: Weds 34, Thursday 23 in 2121 Snedecor Off campus: M,F 12:10  1, in 2121 Snedecor. These are times I will be in my office and available for calls. However, feel free to email or call anytime and I will get back to you asap.  
Grader / office hours  On campus: Chuanlong Du
dclong@iastate.edu office hours, Thursday 10  11, 2406 Snedecor Off campus: Rui Zhong zhongrui@iastate.edu phone: 5152941765, office hours, tbd 

Questions:  Please feel free to email me (pdixon@iastate.edu), Chuanlong (dclong@iastate.edu), or Rui (zhongrui@iastate.edu) anytime with questions or comments.  
If you have questions about homework answers or homework grades,
please contact Chuanlong or Rui first. 

Text:  Littell, R., Stroup, W.W.,
and Freund, R. 2002. SAS for Linear Models, 4th edition. If you are buying a used copy, make sure you get the 4th edition. The 3rd edition lacks important material. This book is not a traditional textbook. The perfect text for this course doesn't exist. This is the most affordable "secondbest" choice. All the details from lectures are in the required course packet. If you prefer a traditional textbook to supplement lecture notes, I recommend you purchase a copy of Kutner et. al (see optional text).  
Course Packet:  on sale at Copyworks in
Campustown (corner of Lincoln Way and Welsh). This includes lecture notes, copies of graphs and handouts, and lab information. The lecture notes include copies of all my transparencies. They include a lot of material not in the required text and some not in the optional text. Lab information contains all the case studies and SAS information. The course packet is revised each year. I highly recommend you get a copy of the 2009 packet. Off campus students will be mailed a copy of the course packet. 

Optional texts: 
Kutner, M.H., Nachtsheim,
C.J., Neter, J. and Li, W. 2005. Applied Linear Statistical Models (5th edition) McGrawHill/Irwin If you a Statistics (or Econometrics) grad student, you should probably own a copy of the text. It is a useful compendium and reference book. There are reserve copies in Parks Library. If you are not going to be a practicing statistician, you can get by without owning a copy.  
SAS texts: These emphasize the data
management and nonstatistical aspects of SAS. Elliot, R. J., 2000. Learning SAS in the Computer Lab. 2nd ed. Duxbury (bookstores: Stat 579) Delwiche, L. D. and Slaughter, S. J. 2003. The Little SAS book, 3nd ed. SAS Institute Press (amazon.com, sas.com, other booksellers)  
Goals:

The catalog says: Introduction to statistical methods for analyzing data from experiments and observational studies. Focus on detecting and modeling systematic effects of experimental factors, making predictions, and quantifying sources of variation. Methods covered include twosample procedures, analysis of variance, simple and multiple linear regression, analysis of covariance, and Chisquare tests. I add: we will also study blocking and simple mixed models (e.g. split plot experiments and 'truly random' blocks. Lab focuses on using the SAS data analysis program, choosing appropriate statistical methods, and communicating statistical results.  
My translation:

Rigorous introduction to the art and science of using statistical methods to answer scientific questions. Discuss commonly used statistical methods, how they are used, why they work, how to do the computations, and how to communicate the results. Apply methods to novel situations.  
Prerequisites:  Knowledge of basic statistical concepts such as mean, standard deviation, normal distribution, binomial distribution, confidence intervals, tests of hypotheses. Knowledge of calculus and matrix algebra.  
Grading:  Homework / Lab assignments: 110 pts
Two Midterms: 130 pts each Final: 130 pts 

Off campus section:  I will send a detailed letter to all off campus students
shortly before classes start. Basically, the off campus sections
will "run" 34 days behind the on campus section. That gives a few
extra days to get started in the semester, and it pushes due dates
back so they fall after a weekend. Lectures are taped and linked to the class web site. You may watch (and rewatch) them at your leisure. You may also watch the lectures and labs as they happen (MWF at 11 am CT, Tu 2:10  4) and ask questions live. Your delivery fee includes a copy of the course notes. You will have to buy a copy of the textbook. Details are in the letter. You will need to arrange a proctor to give out and collect exams. Additional information for off campus students, including textbook, proctor, and examination information, will be in my letter. Off campus students often have multiple responsibilities. I recognize this and am very willing to work with you. Please contact me if you have any concerns or special circumstances (e.g. temporary offsite work assignment). If in doubt, please ask. 
Course Outline (tentative):  
Week  Dates  Topic:  
1  Aug 24  28  Introduction, review, two sample inference (randomization)  
2  Aug 31  Sep 4  Modelbased inference, sample size/power  
Sep 7, Labor Day  No class  
3  Sep 9  11  Power, Diagnostics  
4  Sep 1418  ANOVA  
5  Sep 2125  ANOVA, Contrasts and Multiple comparisons  
6  Sep 28  Oct 2  Pairing and Blocking  
7  Oct 59  Simple Linear Regression  
Oct 8  MIDTERM I (on campus), to be scheduled in the evening, covering through end of week 5 Off campus date tbd, the week around Oct 12 is likely. 

8  Oct 1216  Multiple regression  
9  Oct 1923  Multiple regression (cont.)  
10  Oct 2630  Regression Diagnostics  
11  Nov 26  Model building and model selection  
12  Nov 913  Factorial ANOVA  
Nov 12  MIDTERM II (on campus), to be scheduled in evening covers material through end of week 10 off campus date to be determined, the week around Nov 16 is likely 

13  Nov 1620  Factorial ANOVA (cont.), random effects  
Nov 2327  Thanksgiving break, no class  
14  Nov 30  Dec 4  Split plot designs, Repeated Measures  
15  Dec 711  Count Data, Contingency tables  
Dec 16  FINAL (on campus), (Weds) 9:45  11:45  
Details:
Lab:  "Lab" activities (Tu 2:10  4) include:
Some handson illustrations of statistical principles. Discussion of case studies Use of SAS. Discussion and Q/A on lectures and homework problems. 
Homework:  Homework problems provide practice applying lecture and text material. Discussion with friends and classmates is strongly encouraged. Please write up your answers individually. Copying papers is not a good way to learn. Generally assigned on Friday. Due dates are not the same for on and offcampus students: On campus: due the next Friday in lecture. Off campus: due 4pm the subsequent Monday. Changes to due dates will be announced and posted on the web page. In general, off campus duedates (homework, exams) will be half a week or so after the on campus due dates. No late homework accepted. Lowest homework score will be dropped. Solutions will be posted on the class web site, shortly after the off campus due date. 
Exams:  On campus midterms will be held in the evening, because that is
the only time I can arrange
a larger room to provide ample space. The On campus final is scheduled at the official time for a Monday 11 am class. I expect you to be present at this time (Weds, Dec 16, 9:45  11:45). I will make allowances only if the University changes the official time for the final. The final will focus on the material in weeks 1115, but because of the nature of the material, it will include concepts from throughout the course. Off campus exams will be sent to proctors shortly before the oncampus exam times. You schedule an exam time with your proctor. Exam dates and policies for off campus students are discussed in my introductory letter. 
Makeup exams will be given only if you contact me and get approval
prior to the scheduled exam.
All exams are closed book.
My exams from fall 2007 (.pdf format) are available on the class web site 

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 accommodation(s) 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 1076, Student Services Building. Their telephone number is 5152947220. 
Academic Honesty Policy:  The ISU academic honesty policy is printed in the University catalog
and 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 homework problems, write SAS code, debug SAS code, and interpret the output. You may share SAS code, but I encourage you to understand that code even if you didn't write it. I do require you to write your answers in your own words. On exams: You are to do all work individually. I want to see what you can do. 
Other
questions: 
Please ask in class, email me: pdixon@iastate.edu or email the grader. 