STAT 401A: Statistical Methods for Research Workers
Ag. and Bio. Sciences

Spring 2015

SYLLABUS and other useful information



 

Lectures MWF 9-9:50am,
Laboratory: T 12:10 - 2 pm,
Instructor:  Philip Dixon
pdixon at iastate dot edu

2121 Snedecor Hall
Ames IA 50011-1210

515-294-2142
on campus: 4-2142
 

Office Hours:
TA/grader
TA office hour:
Questions:: Please feel free to e-mail ( pdixon at iastate dot edu or ) anytime with questions or comments.
Text Ramsey, F.L. and Schafer, D.W., 2001/2012. The Statistical Sleuth, 3rd ed. Duxbury
SAS info:
(all optional)
Elliot, R. J., 2009. Learning SAS in the Computer Lab. 3rd ed. Cengage Learning
Delwiche, L. D. and Slaughter, S. J. 2012. The Little SAS book, 5th 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
Aug 20 - 24 1 Types of studies, Statistical Inference,
Data summary
Aug 27 - 31 2 Comparison of two groups:
    Hypothesis tests
Sep 3, Labor Day No class
Sep  5-7 2     Confidence Intervals
Sep 10-14 4     Nonparametric methods
Sep 17-21 3     Assumptions and robustness
Sep 24 - 28 5 Comparison of multiple groups
Oct 1 - 5 6 Linear combinations and multiple comparisons
Oct 8 MIDTERM I due at start of class (9am)
Oct 8 - 12 6 False Discovery Rate, Choosing a method
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 Two-way 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.
Section A 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). We will use and teach SAS for computing, but I will cover the use of R for those interested.
Section B is for undergraduates and uses JMP for computing.
Section C is for graduate students in social sciences and education.
Sections D is for graduate students in the physical sciences, math, and engineering. It includes more mathematical detail.
Student background: Section A is intended for graduate students working in agriculture or the biological sciences, broadly interpreted. The prerequisite (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 graduate-level expectations. I use a graduate-level grading scheme (mostly A's and B's) but I reserve the right to give lower grades when appropriate.
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 hands-on illustrations of statistical principles.
Return HW
Discussion and Q/A on lecture material and homework problems.
Use of SAS and/or R
 
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.
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.
I will also introduce R for those wishing to use that program instead of SAS. The differences between the two programs will be discussed in first lab period.

You may use another package if your lab group uses something other than SAS or R. Please check with me to make sure that package is appropriate for this class. EXCEL is not appropriate.   We will provide help for SAS and R 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 you will need in 402. 
 

Exams:
Other
questions:
Please ask in class or e-mail 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 515-294-7220.
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.