Joy Jordan,
Associate Professor of Statistics, 410 Briggs Hall
PHONE: 832-6894, EMAIL: joy.jordan@lawrence.edu, WEB: www.lawrence.edu/fast/jordanj/
Please note the
URL for my homepage. On this page is a link to the Math 445 web page, where I
will post, for example, homework assignments and handouts. I check email
regularly (2-3 times a day), but not obsessively. If you need to contact me urgently (e.g., you have a family emergency,
you want to make an appointment as soon as possible), then please call me.
Modern
Mathematical Statistics with Applications, Devore and Berk, 2007, Duxbury
(a copy of the textbook is on 2-hour reserve at the library)
Additional
reading (e.g., copied sections of other texts, journal articles) will be
provided throughout the term; this
reading is as important as the textbook reading.
Monday: 3:00 – 4:30, Tuesday: 2:30 –3:30, 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 in.) Please ask if you need help, and I will do all I
can to assist you. 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.
A tentative
course schedule is included with this syllabus. We are scheduled to cover the
material in Chapters 1, 6–12 of the textbook, as well as additional reading. We
may cover more or less material, though, depending on how the course unfolds. I want to be flexible with the schedule in
case changes are needed to best encourage learning. (I’ll keep you posted
on major changes to the schedule.)
Class
Participation/Discussion
Class
discussion is part of this course, and it will be factored into your final
grade. Throughout the term, additional reading will be assigned, and you are responsible
for coming to class prepared to effectively and critically discuss this
material. Occasionally an individual student may be asked to explain a
problem-solution to the whole class. Note that participation also includes
asking thoughtful questions in class and office hours, and answering my queries
in class (regardless of whether you have the “right” answer).
You will turn
in regular homework assignments; these problems (or a subset of the problems)
will be graded. There will be two types of homework problems: 1) problems on
which you are free to work together and 2) work-alone problems. On the
work-together problems, your write-up should be your own (you can talk with
other students about the problems, but you must write up the solutions
individually—that is, you can write scratch work from your study sessions, but
you must use your own words and explanations when writing up your final
solution). On the work-alone problems, you should not talk with other students
at all (except perhaps to clarify what a question is asking)—please see me in
office hours with questions about the work-alone problems. On the assignments,
I will clearly differentiate between the two types of problems.
Your grade will
depend on both the content and exposition of your answers; write up the
problems as carefully as you did—or should have done—in Math 310. That is, be sure your logic is clear, your
solution reads smoothly (even if using symbols), and that one of your peers
could read and understand your solution without asking any additional
questions.
I take the
Lawrence Honor Code very seriously, and recently I’ve been deeply disappointed
to hear of more frequent violations of this code. I think the Honor Code is a
special quality of the Lawrence experience; a quality that translates
beautifully into a life-long behavior. If you are feeling particularly stressed
in this class, come talk with me, but don’t violate the Honor Code—I will
pursue any case I feel is an honor code violation.
There will one in-class exam during the term and a
final exam. The first exam is on Wednesday, February 10 and the
final exam is Tuesday, March 16 at 8:30 a.m.
Computer
Lab (Thursdays 9:50 – 11:00, Briggs 421)
Your final
grade is based on a weighting of class participation (10%), work-together
homework (25%), work-alone homework (25%), and exams (first exam – 20%, second
exam – 20%). The letter grades will be assigned as follows, corresponding to
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- |
Because I love
statistics so much, I will encourage you to work hard to learn the material.
(You thought I loved probability, but I really, really love statistics!) Please
realize, though, 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
Date
|
General Material
|
Textbook
|
|
M
1/4 |
General
sampling distributions and the distribution of the sample mean (Central Limit
Theorem) |
Sections
6.1 – 6.2 |
|
W
1/6 |
Central
Limit Theorem and distribution of a linear combination |
Sections
6.2 – 6.3 |
|
R
1/7 |
Lab: Simple data analysis—One-variable
numerical summaries and graphics |
Chapter
1 |
|
F
1/8 |
Finish
Central Limit Theorem; class discussion of data collection |
Sections
6.2 – 6.3 |
|
M
1/11 |
Chi-square
distribution and t distributions |
Section
6.4 |
|
W
1/13 |
T
and F distributions; Estimation and properties of estimators |
Section
7.1 |
|
R
1/14 |
Lab: Properties of
estimators; introduction to the bootstrap |
Section
7.1 |
|
F
1/15 |
Methods
of estimation: method of moments and maximum-likelihood estimation |
Section
7.2 |
|
M
1/18 |
No class – Martin
Luther King Jr. Day |
|
|
W
1/20 |
Maximum-likelihood
estimation |
Section
7.2 |
|
R
1/21 |
Lab:
Estimation activity |
|
|
F
1/22 |
Finish
point estimation and begin confidence-interval estimation |
Sections
8.1 – 8.2 |
|
M
1/25 |
General
confidence intervals and t confidence intervals |
Sections
8.1 – 8.3 |
|
W
1/27 |
Prediction
intervals; class discussion of articles (bootstrap and confidence interval
for a population proportion) |
Sections
8.1 – 8.3 |
|
R
1/28 |
Lab: Introduction to R
and bootstrap confidence intervals |
Section
8.5 |
|
F
1/29 |
Finish
confidence intervals; begin hypothesis testing for a population mean
|
Sections
9.1 – 9.2, 9.4 |
|
M
2/1 |
Hypothesis
testing for a population mean (including power calculations)
|
Sections
9.1 – 9.2, 9.4 |
|
W
2/3 |
Finish power;
relationship between confidence intervals and hypothesis testing; t-test
|
Sections
9.1 – 9.2, 9.4 |
|
R
2/4 |
Lab: To be announced
|
|
|
F
2/5 |
T-test; hypothesis testing for a population proportion; practical
significance
|
Sections
9.1 – 9.4 |
|
M
2/8 |
Class discussion of journal article
(assumptions for inference); exam review
|
Reread
Chapters 1, 6 – 9 |
|
W
2/10 |
Exam 1(Chapters 1, 6
– 9) |
|
|
R
2/11 |
No Lab – Reading
Period |
|
|
F
2/12 |
No class – Reading
Period |
|
|
M
2/15 |
Two-sample
problems (confidence intervals and hypothesis tests)
|
Sections
10.1 – 10.2 |
|
W
2/17 |
Two-sample
and paired-data problems |
Sections
10.1 – 10.4 |
|
R
2/18 |
Lab: Two-sample and
paired-data bootstrap confidence intervals |
Section
10.6 |
|
F
2/19 |
Finish
two-sample problems |
|
|
M
2/22 |
One-factor
ANOVA |
Sections
11.1 |
|
W
2/24 |
One-factor
ANOVA |
Sections
11.1 – 11.3 |
|
T
2/25 |
Lab: Two-sample problems
using Minitab; one-factor ANOVA |
|
|
F
2/26 |
One-factor ANOVA
|
Sections
11.1 – 11.3 |
|
M
3/1 |
Two-factor
ANOVA |
Section
11.4 – 11.5 |
|
W
3/3 |
Two-factor
ANOVA
|
Section
11.4 – 11.5 |
|
T
3/4 |
Lab: Two-factor ANOVA; class discussion of article (ANOVA)
|
|
|
F
3/5 |
Simple linear
regression model
|
Sections12.1
– 12.2 |
|
M
3/8 |
Properties of and inference about regression
coefficients |
Section
12.3 |
|
W
3/10 |
Confidence and prediction intervals; regression
diagnostics |
Sections
12.4, 12.6 |
|
T
3/11 |
Lab: Regression (simple
linear regression and quick look at multiple regression) |
|
|
F
3/12 |
Quick summary of non-parametric tests and tests
on categorical data; class discussion of article (data mining); review |
Reread
Chapters 10 – 12 |
|
T
3/16 |
Exam
2 (Chapters 10 – 12, and relevant previous material) – 8:30am |
|