Math 217: Applied Statistical Methods – Winter Term, 2009

“The best thing about being a statistician is that you get to play in everyone’s backyard.”

“Far better an approximate answer to the right question, which is often vague, than the exact answer to the wrong question, which can always be made precise.”

– John Tukey (1915 – 2000)

Course Goals

In general, a statistics course teaches you to both descriptively and inferentially explain the variation that occurs in real data. Upon completion of this course, you should know how to (among other things)

· Graphically and numerically describe single variables and relationships between variables in a data set

· Via regression, model linear relationships between variables, make inference about the relationship in the population (based on the sample), and diagnose the model to ensure it is trustworthy

· Understand experimental design and analyze standard experimental designs via one-variable and two-variable analysis of variance

· Analyze categorical data via two-way tables, inferential procedures, and logistic regression

· Use bootstrapping to find the sampling distributions of statistics for which we don’t have mathematical theory and understand the idea of permutation tests

· Understand when and how to apply nonparametric procedures

• Understand the general concept of inference, as well as its limitations (including all conditions that must be met)
• Serve capably as a statistical consultant, asking important questions, gathering appropriate information, and following-up with additional questions

Contact Information

Professor: Joy Jordan

Office: 410 Briggs Hall

Phone: 832-6894

E-mail: joy.jordan@lawrence.edu

Web page: www.lawrence.edu/fast/jordanj/

Please note the URL for my homepage. On this page is a link to the Math 217 web page, where I will post homework assignments, solutions, handouts, etc. You should visit this website regularly. Also note that I check email fairly regularly throughout the day (typically 3 times), but if you have an emergency or a message that is urgent, then you should definitely call, not email.

Required Textbook

Introduction to the Practice of Statistics, 5^th Edition, Moore and McCabe, 2006, W.H. Freeman and Company

The textbook has a helpful companion website (a link to this site is included on the course web page).

Important Notes: There is a new (6^th) edition of this textbook (put out last spring), but in an effort to keep costs down for students, we are using the 5^th edition of the book (that is, used copies should be easy to find). Also, a copy of the textbook is on 2-hour reserve at the library (under Mr. Clemons – Math 107).

Office Hours

Monday: 3:30 – 4:30, Tuesday: 11:00 – 12:00, Wednesday: 11:30 – 12:30, Thursday: 1:30 – 3:00

If these times do not work with your particular class schedule, I am happy to make individual appointments for other times. (You need not make an appointment during regular office hours—just come on by.) Please ask if you need help, and I will do all I can to assist you, but remember that you need to ask (I can’t read your mind J). That said, I expect you to come to office hours prepared (e.g., having done the reading, knowing the definitions) and not simply looking for easy answers. Besides office hours, anytime my door is open, feel free to come in and ask questions. If my door is closed, I am either out of the office, or I’m working and prefer not to be disturbed.

Exams

There will one in-class exam during the term and a final exam. The first exam is on Wednesday, February 4 and the final exam is Tuesday, March 17 at 8:30 a.m. These exams may include additional take-home parts.

Homework

You will turn in regular homework assignments (including textbook problems and problems stemming from computer lab); these problems (or a subset of the problems) will be graded. Your grade will depend on both the content and exposition of your answers (write up the problems carefully). You can talk with other students when you initially think about the problems, but you must write-up your solutions completely on your own. (For example, if you work as a group and one student writes a solution on a white board, then other students can look at the answer and discuss the ideas, but cannot simply copy the solution word-for-word from the board.) When you sign the honor code on each assignment, you are attesting that your written solutions are in your own words.

Class Participation/Discussion

Class discussion is an important part of this course and it will be assessed (see final-grade weighting below). Throughout the term, additional reading will be assigned, and you are responsible for coming to class prepared to effectively and critically discuss this material. Class participation also includes asking thoughtful questions in class and office hours. (An additional handout will be given on suggestions for effective class discussion.)

Computer Lab (Thursdays 9:50 – 11:00, Briggs 421)

The weekly computer lab should be thought of as an extension of the lecture, and new material will sometimes be presented in lab. The lab will also be used to investigate and interpret real data (using statistical software, Minitab). Hopefully it will be an aid to your understanding of the material.

Service-Learning/Consulting Project

An important part of becoming a practicing statistician is to learn the art and skill of consulting (working with a client in a different field and assisting with the analysis of the client’s data). In order to analyze the data, you first must completely understand the context of the questions, the data collection methods, and the exact research questions to be answered. This involves a lot of questioning and answering with the client. It also involves working with the client’s raw data and putting it in a form that will work for the analysis (as well as “cleaning” the data, in case there are any typos). This is the grungy work that most textbooks don’t tell you about. Then you must carefully and thoroughly analyze the data, while also being able to explain the results in layperson’s terms. Individually and as a class, you will work on a service-learning/consulting project in the second part of the term. Here is the initial inquiry I received from our client: “My name is Mary Kohrell, and I'm an Associate Professor of Community Development with the Calumet County UW Extension office in Calumet County, based in Chilton. I am currently working on a research project that involves analyzing people's behaviors and practices regarding the safe disposal of leftover medicines in 4 northeastern WI counties. My survey was conducted in the summer of 2008 and data has all been entered. The Survey Research Center at UW River Falls helped implement the survey. The survey data needs some fairly straightforward analysis, and I'm wondering if there is a Lawrence student who is in a position to complete the analysis as part of an internship, class project, or independent study. I have no funds to pay for any of the analysis.”

Grading

Your final grade is based on a weighting of class participation (10%), homework assignments (25%), service-learning project and presentation (15%), and exams (first exam – 25%, second/final exam – 25%). The letter grades will be assigned as follows, corresponding to Lawrence’s GPA system (note: the cutoff is the lowest percentage that receives that letter grade):

Cutoff	Grade
93.75	A
90.00	A-
86.25	B+
83.75	B
80.00	B-
76.25	C+
73.75	C
70.00	C-
66.25	D+
63.75	D
60.00	D-

Life Balance

Because I love statistics so much, I will encourage you to work hard to learn the material. But please realize that your self-worth is not associated with your letter grade on a particular homework or exam (or even with your final course grade). You are all good people, regardless of your official class performance on tasks. Furthermore, I think as a society in general, and at Lawrence in particular, we are over-scheduled and allow precious little downtime and quiet reflection. I encourage you to think carefully about the intensity and number of courses, activities, and obligations in your life, and to seek balance as much as possible. (I’m happy to talk with you more about this—that is, we can discuss life as well as statistics. And you can read more about my thoughts on my blog: http://joyofstatistics.blogspot.com/)

Tentative Course Schedule (with corresponding textbook reading)

Date	General Material	Reading
M 1/5	Class discussion (review of what students remember), overview of course, sampling-distribution activity	Review any of Chapters 1-7 that seem foggy
W 1/7	Class discussion of data collection (additional reading), review of Central Limit Theorem and the t-distribution	Chapter 3, Sections 5.2, 7.1 – 7.2
R 1/8	Lab: Review of one-variable graphics and numerical summaries	Chapter 1
F 1/9	Review of simple linear regression and introduction of inference in regression	Chapter 2, Sections 10.1 – 10.2
M 1/12	Inference in simple linear regression	Section 10.1 – 10.2
W 1/14	Finish simple linear regression and class discussion of conditions for inference (additional reading)	Section 10.1 – 10.2
R 1/15	Lab: Simple linear regression
F 1/16	Multiple regression	Sections 11.1 – 11.2
M 1/19	No class – Martin Luther King Jr. Day
W 1/21	Multiple regression (possible supplemental material)	Sections 11.1 – 11.2
R 1/22	Lab: Multiple regression
F 1/23	One-way analysis of variance (ANOVA)	Section 12.1
M 1/26	One-way ANOVA and multiple comparisons	Sections 12.1 – 12.2
W 1/28	Two-way ANOVA	Section13.1
R 1/29	Lab: Two-way ANOVA lecture	Sections 13.1 – 13.2
F 1/30	Lab: One-way and two-way ANOVA
M 2/2	Class discussion of article (additional reading) and review	Reread Chapters 2, 10 – 13
W 2/4	In-class Exam (Chapters 2, 10 – 13)
R 2/5	No lab
F 2/6	Introduction of service-learning project and data analysis of two-way tables	Section 9.1
M 2/9	Class discussion of questions to ask during consulting and inference for two-way tables	Sections 9.2 – 9.3
W 2/11	Consultation with Mary Kohrell on service-learning project
R 2/12	No lab – Reading Period
F 2/13	No class – Reading Period	Catch up on reading, homework, and project
M 2/16	Finish inference in two-way tables and start logistic regression	Sections 9.2 – 9.3, 16.1
W 2/18	Logistic regression (possible supplemental material)	Sections 16.1 – 16.2
R 2/19	Lab: Data analysis and inference in two-way tables; logistic regression
F 2/20	Class discussion of service-learning project and of article (additional reading)
M 2/23	Bootstrap methods	Sections 14.1 – 14.2
W 2/25	Bootstrap methods	Sections 14.1 – 14.2
R 2/26	Lab: Bootstrap methods
F 2/27	Permutation tests and class time to discuss final paper	Section 14.5
M 3/2	Class discussion of article (additional reading) and nonparametric tests	Section 15.1
W 3/4	Nonparametric tests	Sections 15.2 – 15.3
R 3/5	Lab: Nonparametric tests
F 3/6	Finish nonparametric tests and start review examples/case-studies	Section 15.3
M 3/9	Discussion of final paper and presentation
W 3/11	Final presentation for service-learning project (and discussion of any additional analysis needed)
R 3/12	Lab: Only if needed to finish any material or do additional analysis for service-learning project
F 3/13	Debrief on service-learning project and review examples/case-studies
T 3/17	Exam 3 (Chapters 9 – 16), 8:30am	Reread Chapters 9 – 16