Spring 2015
SYLLABUS and other useful information
Lectures:  MWF 99:50am, 1126 Sweeney 
Laboratory:  T 12:10  2 pm, 2272 Gilman 
Instructor:  Philip Dixon
pdixon at iastate dot edu 2121 Snedecor Hall
42142

Office Hours:  M 34, Tu 45 
TA/grader  Nehmias Ulloa 
TA office hours:  W 35, 3404 Snedecor 
Questions::  Please feel free to email ( pdixon at iastate dot edu or nulloa1 at iastate dot edu) anytime with questions or comments. 
Objectives:  By the end of the course, students should be able to analyze and interpret data from experimental studies using parametric and nonparametric methods. Students should be able to appropriately use statistical methods for comparisons of two groups, comparisons of multiple groups, and relating a response to one or more continuous variables. Students should understand how features of the study design influence the choice of statistical method and the type of conclusions that are appropriate. Students should recognize the conditions necessary for an appropriate statistical analysis, how to check if those conditions are met and understand the consequences of violating those conditions. 
Text:  Ramsey, F.L. and Schafer, D.W., 2012. The Statistical Sleuth, 3rd ed. Duxbury 
SAS info: (both 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 use statistical software, 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  Jan 1216  1  Types of studies, Statistical Inference,
Data summary 
Jan 19, Martin Luther King Day  No class  
2  Jan 2123  2  Comparison of two groups:
Hypothesis tests 
3  Jan 2630  2  Confidence Intervals 
4  Feb 26  4  Nonparametric methods 
5  Feb 913  3  Assumptions and robustness 
6  Feb 1620  5  Comparison of multiple groups 
7  Feb 2327  6  Linear combinations and multiple comparisons 
Feb 24  MIDTERM I In lab  
8  Mar 26  6  False Discovery Rate, Choosing a method 
9  Mar 913  7, 8  Linear regression 
Mar 1620  Spring break, no class  
10  Mar 2327  8, 9  Lack of Fit, Correlation, Multiple Regression 
11  Mar 30  Apr 3  9, 10, 11  Multiple regression (cont.) 
12  Apr 610  12  Model selection 
Apr 7  MIDTERM II in lab  
13  Apr 1317  13,14  Twoway ANOVA (intro) 
14  Apr 2024  18, 19  Contingency tables 
15  Apr 27  May 1  20, 21  Logistic regression 
May 4  (tentative) Final exam 7:30 am  
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). Computing will be your choice of SAS, JMP, or R. Section B is for graduate students in social sciences and discusses both experimental and observational studies. Section C is for graduate students in social sciences, especially political science, and education. Computing will be SAS. Sections D is for graduate students in the physical sciences, math, and engineering. It includes more mathematical detail. Computing will be JMP. 
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 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. 
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. Return HW Discussion and Q/A on lecture material and homework problems. Use of SAS, JMP, 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. We will rely on the computer to do most, if not all,
the appropriate calculations, so part of lab time will discuss how to use statistical
software.
The choice of software will be discussed in the first week of class. I plan to provide support for SAS, JMP, and R. You may use another package if your lab group uses something other than SAS, JMP, or R. Please check with me to make sure that package is appropriate for this class. EXCEL is not appropriate. 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:  Exams will be held during lab and the designated final exam time for a Monday 9am class.
You should bring a calculator. I will provide formulae and computer output.
My goal is to see how well you can use class material to analyze data.
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 class will follow Iowa State University's policy on academic dishonesty policy.
This policy
is available online. Anyone suspected of academic dishonesty will be reported
to the Dean of Students Office. 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. 