APM 620 EXPERIMENTAL DESIGN AND ANOVA (Spring, 2014)
COURSE DESCRIPTION : APM 620 is an applied course in the design, analysis, and interpretation of experiments and observational studies. At the end of the course, you will be able to: 1) select a practical statistical design that answers specific research questions with adequate statistical precision or power; 2) recognize the model and assumptions of the statistical method used; 3) formulate the appropriate statistical analysis of the data to answer research questions; 4) select relevant information for analysis and interpretation from the output of a statistical package; and 5) write clear, concise summaries describing the statistical design of an experiment, and the analysis and interpretation of the results. The course is designed to provide you with the ability to design, analyze, and interpret results for your research, and to critique the statistical designs and analyses of other researchers.
PREREQUISITE : APM 391, APM 510 or an equivalent introductory statistics course leading to understanding of confidence intervals, hypothesis testing, Type I and II errors, power, sampling distributions
LECTURE : Tuesday and Thursday 11:00-12:20, Baker 145
INSTRUCTOR : Dr. Steve Stehman (322 Bray, 470-6692, svstehma@syr.edu)
OFFICE HOURS : Tuesday and Thursday 1:00-2:00, or by appointment (schedule by email please)
TEXT (optional) : Robert Kuehl, Design of Experiment: Statistical Principles of Research Design and
Analysis , 2000 (2nd ed.); the first edition of the same book (1994) is also acceptable. Any basic statistics text that discusses experimental design and ANOVA would be useful for background reading.
RELATED TEXTS (on 2-hour reserve in Moon Library):
Petersen, R.G. Design and Analysis of Experiments (covers basic material well, simpler treatment
of topics than presented by Kuehl)
Mead, R. The Design of Experiments (extremely strong on explaining concepts, but difficult to
read until you have some familiarity with the basic issues of a given topic)
Kuehl, Statistical Principles of Research Design and Analysis (1st edition)
OTHER MATERIALS : Large three-ring binder to hold lecture notes and handouts, calculator (basic math functions adequate, add, subtract, multiply, divide, square root)
COURSE OUTLINE – Major Topics (approximately one week per topic)
· Review of basic statistical concepts - confidence intervals, p-values, Type I & II errors, power
· Completely randomized design (CRD) - ANOVA table, F-test, origin of “Analysis of Variance”
· General approaches to analysis - comparisons of means
· Contrasts and planned comparisons
· Unplanned and pairwise comparisons of means
· Factorial treatment designs - several treatment factors combined in one experiment
· Randomized complete block designs (RCBD) - basic experiment design to improve power
· Assumptions - how to assess, implications of departures from assumptions, remedies
· Subsampling and pseudoreplication - identifying experimental units and proper error terms
· Expected mean squares - how to verify the test statistic for a specified hypothesis
· Fixed and random effects - analysis and interpretation derived from different models
· Split-plot designs - use of different sized experimental units in same study (with factorial design)
· Quantitative treatments - levels of treatment factor are quantities
·
Repeated measures - time is a treatment factor and same experimental units are observed over time
· Practical considerations of experiment design and selection of treatments
· Analysis of covariance - use of quantitative auxiliary information to improve comparisons
· Nonparametric methods for ANOVA will be presented at the same time as their parametric counterparts

COMPUTING : MINITAB will be the primary tool for statistical computing. We will also interpret output from other statistical packages primarily focusing on SAS. The focus on MINITAB is a change from past years. Information on how to obtain a free personal copy of MINITAB will be provided.
EVALUATION AND GRADING
Item
Exam 1
Exam 2
Points
250 (February 27)
275 (April 10)
Final
Homework
Total
325 (Friday, May 2, 12:45-2:45 p.m.)
150
1000
I will try to adhere to the Exam 1 and Exam 2 dates given, but please be alert for schedule changes. The final exam is scheduled by the College and will not be changed. Exams will be open book and notes. The final exam is comprehensive covering the entire course. In the past, I have had 3 exams plus a final, so this will be the second year I will have only 2 exams plus a final.
COURSE GRADES : Your course grade will be determined by the Total Points earned on the items listed in the Evaluation section.
A
A-
B+
B
B-
C+
935-999
895-935
850-895
725-850
650-725
600-650
Excellent
Very Good
Good
Satisfactory, basic competency achieved
Adequate, but improvement is needed to become fully functional
Inadequate understanding of several key topics
Inadequate understanding of most topics
Spent semester mentally vacationing
C
F
500-600
1-500
HOMEWORK : Homework problems are designed for practicing techniques and analyses discussed in lecture, with emphasis on proper choice of statistical procedure and interpretation of statistical results. You may work with other students on homework problems. You may also ask me questions about homework.
You will be provided with solutions to homework problems prior to the due date of the assignment. You should use the solutions to check your work. If you have questions after comparing your answers to the solutions, you should clearly indicate on your homework what it is you want me to check, or alternatively, state what your question/confusion is. If you do not have any questions or issues for the assigned problems, you should write “No Questions" on the front of your homework and hand in your work. Your homework should be organized and neatly written. You do not have to type or wordprocess your homework, but prepare your homework so that you can easily refer to it and recognize what you did if you look at your answers later in the semester. You are permitted two late assignments, but a late assignment must be handed in within one week of the due date. These deadlines are provided to motivate you to work consistently throughout the semester. Stay up to date to maximize your learning!
The above schedule and procedures in this course are subject to change in the event of unforeseen extenuating circumstances.
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1. Lecture: The lectures generally follow a traditional approach of presenting content focusing on the lecture notes provided. Most of the factual material and details (e.g. definitions, formulas, description of examples) are provided in the lecture notes so you do not have to copy basic information in lecture. However, the lecture note handouts have space for you to write additional information as topics are discussed in class. In general, I assume you are encountering the course material for the first time when I present it in lecture (although you are welcome to read the notes and book ahead of time). In lecture, my intent is to have you see and hear the course material presented in a sequence of topics and within a context that I think makes the most sense for learning. Lecture introduces basic facts and methods, and on occasion, I will review the “big picture” context for the topics presented. However, you should also spend time developing your own view of the big picture that makes the most sense to you. In general, lecture will not be an active learning environment in that you will not be engaging in learning from a “discover it yourself” approach.
The lectures are predicated on several assumptions: 1) One of the objectives of the course is to allow you to develop an awareness of many different experiment designs and analyses as well as some basic underlying theory and concepts. I assume that while you are aware of some methods and some theory, there is a large component of both methods and theory you would not likely be aware of if I did not point you in the right direction. 2) A more problem-based orientation to the course would likely result in a better grasp of some methods, but would also likely sacrifice breadth of coverage (i.e., you would know a few things very well, but not be aware of what else is out there). 3) I assume that most students would have a difficult time learning ANOVA from a book. Lecture, in an odd sense, is kind of like a “book on tape” where the narrator not only reads the story, but also explains to you what is happening. One of my goals for improving the course is to inject more of an active-learning feature, although that would not necessarily take place in lecture and instead might be more prominent in homework or other outside of lecture activities.
4) Total lecture time for the course is approximately 35 hours (I don’t count in-class exams, and inevitably 1-3 lectures are cancelled because I am out of town), so it is important to use lecture time efficiently.
2. Homework : 10-12 assignments, approximately one per week. Homework provides practice using formulas and interpreting results of various analyses. I will provide solutions to the homework problems prior to the due date. You should check your answers with the solutions, and if you have any questions, write those questions on your homework and I will respond. I have moved to this system because I think it is more valuable if you have quick feedback on your work, and you can assess if your answer is correct as easily as I can if you have the solutions.
This approach does place a high level of responsibility on you to put in a good faith effort trying to work problems before looking at the solutions.
3. Textbook . For ANOVA, I believe it is important that you become familiar with at least one textbook. It really doesn’t matter which text it is as long as the text focuses on applications. The ability to read a text in this area will help you when you have to construct a design or implement an analysis that is unfamiliar to you. There are many topics we will not cover in this course but they may be topics you will need in your work. While you can certainly do very well in the course without ever opening a book, and I do not actually “test” you on your ability to read a text,
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I strongly encourage you to read sections in the book that are related to the lecture notes, and to do this reading on a regular basis.
4. Exams : Two exams and a final (comprehensive) will be given. Exams are open book and notes so it is not necessary to memorize formulas or definitions. However, I do expect that you have spent considerable time working with the formulas and notation so that you have very rapid recall, and that you have organized the information so that you can find it quickly (either in your lecture notes or on summary/synthesis notes you have prepared). The exams provide motivation for you to review and synthesize course content, and they also provide feedback on where additional work is necessary. Exams will be cumulative to some extent because ideas presented early in the course will continue to be used as we proceed through more advanced topics.
5. Practice tests (usually 2 practice tests for each exam). These are exams from previous years covering the same content. I consider these an important learning resource that you should use after you have thoroughly prepared for an exam. That is, the practice tests should be taken as if they were a real exam after you have completed your preparation for the exam. Practice exam questions are usually more challenging than homework questions and often extend the basic ideas of the course.
6. Optional review session before each exam . These are times where you can ask questions about anything (related to the course!), but typically questions focus on practice tests with a few questions related to clarification of lecture notes. I do not formally prepare a review lecture and simply respond to your questions. However, often I use your questions as a jumping off point to review key ideas or topics I suspect you might find confusing.
7. Office hours : 2 hours per week, also available for appointments and email correspondence.
Most office hour and email correspondence is about homework problems.
8. Statistical Software. MINITAB will be the statistical program used for computing, although computing will not be a major component of the course requirements. The two primary alternatives (in terms of number of users) are SAS and R, with SPSS, Stata, and others following.
SAS is a very reliable, powerful, and carefully supported package. It is capable of doing all the basic analyses covered in this course, and it also is capable of most advanced analyses you will ever need to do. R is free, and it is excellent for “cutting edge” analyses developed by individual researchers. The more computing packages you are familiar with, the greater your ability to select the most convenient, reliable package for a given problem. MINITAB is simple to use and many of you may be familiar with MINITAB from a previous course. MINITAB does not have some of the more advanced analysis techniques present in R or SAS so eventually you may need to migrate beyond MINITAB. But the time investment to teach R and SAS concurrent with APM
620 is prohibitive given the size of the class and lack of GA support.
9. Blackboard – We will use Blackboard primarily as a file management system to provide you access to example data and optional readings. Lecture notes and homework will be provided to you as handouts in class (lecture notes will also be available on Blackboard). Every student who is enrolled in the class has access to Blackboard ( https://blackboard.syr.edu/ ). To send email to the class, I use Blackboard which has your @syr.edu email. Please make sure your email is forwarded from your @syr.edu email if that is not the email address you monitor regularly.
10. “Group Learning” (Working with Other Students)
. Collaboration with other students is allowed and encouraged on homework. Students who learn well by working with others usually informally and voluntarily meet to study or review together.
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1. Attend lecture (not necessary to have read material beforehand); write additional notes on lecture handouts as you need; try to stay awake; try to ignore your phone and other communication devices.
2. Review past lecture notes before each lecture . While it is not necessary to read ahead to prepare for a lecture, it is critical to review past lecture notes regularly. Re-work any numerical examples from lecture, make sure definitions and notation are familiar; make up your own glossary of terms and notation to help your short-term recall. Statistics has a lot in common with learning a language – you have to practice and eventually after enough practice, the notation and terminology become familiar.
3. Read your text if there is a section relevant to the lecture notes; work through examples in the text.
4. Work homework problems , check solutions for any difficulties, correct your mistakes, and if you have questions, write them down and submit to me; “I don’t even know where to begin” is sometimes a valid question for a homework problem.
5. 7-10 days before an exam, start reviewing past material, focusing on details but also thinking some about the “big picture” (i.e. what ideas go together, why are certain methods advantageous, what general theory exists to guide what we do in practice).
6. After completing your review, work the practice tests as if they were a real exam and check solutions; revisit any topics that you do not understood, ask questions in office hours or via email; also review homework problems and solutions for the portion of the course covered by the exam.
7. Attend the review session to ask questions and to hear questions other students may have.
8. After the exam has been returned, look over any problem areas, and ask questions if you still are confused about some of the problems.
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