Syllabus, grading policies, office hours, and general information
Logistics
Readings
The primary text is:
Freedman, D., Pisani, R., and Purves, R. (2007). Statistics (fourth edition). W. W. Norton & Company, Inc.
We also will be reading from a course pack put together by Professor Reiter. It is available for purchase from the Duke bookstore.
Computing
We will use the statistical software package JMP-IN in labs and for the final project. You can download JMP-IN on to your personal computer for free from the OIT software web site. It is also available on all public PCs across campus.
Calculator
You need a basic calculator or access to one on a computer. You don't need to purchase a calculator that can do graphing or has statistical functions.
Some advice for success in Statistics 101
DO AS MANY PROBLEMS FROM THE TEXT BOOK AS POSSIBLE!!!
The best way to learn statistics, or any quantitative subject, is to work problems. Although you don't have to turn in problems from the book, working them on your own on a consistent schedule will greatly improve your understanding of the material. Also, the web problems, in-class problems, and exams (discussed below) are based on the concepts covered by the problems in the text, so that a solid understanding of these problems increases the chance you will earn a high grade in the course.
Most sections in the text are followed by a set of exercises. I recommend working these problems as you are reading. There are also review exercises at the end of most chapters. I recommend doing these a few days after reading the chapter to solidify your understanding. Answers to the exercises are in the back of the book, and answers to review problems are available in the Statistical Education and Consulting Center.
Students who have success in Statistics 101 typically put in at least 6 hours of work per week outside of the classroom. I recommend setting up a realistic study schedule in which you spread your work over the week. Leaving all your statistics studying to one night is a bad strategy, because you won't spend enough time to develop a thorough understanding of the material. There is a very useful handout describing strategies for studying for quantitative courses on the web site for Duke's Academic Skills Instructional Program (at the site, select "General Academic Skills handouts" and then "Problem-solving courses"). It's packed with good tips, especially for those who don't have much experience studying for quantitative courses at Duke.
I strongly encourage you to form a study group and work problems together. Evidence shows that students who work in groups in quantitative courses learn more and enjoy the course more than those who work alone (see the studies by Richard Light at Harvard University).
You may find that you want more help than can be provided in office hours and help labs. If so, we recommend that you obtain a statistics tutor from the Peer Tutoring Program (PTP). This is a free service.
Finally, visit the TAs and instructor when you get stuck or even
when you figure something out and want to share your
victory. Think of us as allies in your efforts to learn
statistics. Nothing makes us happier than you understanding all
the material!
Graded work
Graded work for the course will consist of problem sets, lab work, a final project, two midterms, and a final exam. Your final grade will be determined as follows:
| Final exam | 30 % |
| Midterm exam 1 | 15 % |
| Midterm exam 2 | 15 % |
| In-class problems |
15 % |
| Lab work |
10 % |
| Final project |
10 % |
| Web problems | 5 % |
There are no make-ups for exams, in-class problems, labs, or web
problems, except for a medical or familial emergency or previous
approval of the instructor. See the instructor in advance of
relevant due dates to discuss possible alternatives.
Cumulative numerical averages of 90 - 100 are guaranteed at least an A-. Cumulative numerical averages of 80 - 89 are guaranteed at least a B- . Cumulative numerical averages of 70 - 79 are guaranteed at least a C-. Cumulative numerical averages of 60 - 69 are guaranteed at least a D -. These ranges may be lowered, but they will not be raised (e.g., if everyone has averages in the 90s, everyone gets at least an A-). The exact ranges for letter grades will be determined after the final exam. The more evidence there is that the class has mastered the material, the more generous the curve will be.
Descriptions of graded work
Web problems:
Before each class, you should read the assigned pages from the text or supplemental materials. After you read the assigned pages, answer the web problems about the readings for that class. These problems are accessible on our course web site on Blackboard. You are permitted to look through the readings for answers. After class starts, the problems will not be graded and count as zeros.
These questions have three primary functions: 1) they allow you to
practice some essential statistical skills; 2) they guide you to think
about certain aspects of the readings that you may have missed upon
first reading, and 3) when answered correctly, they reward you with
grading points for working exercises and keeping your reading
current. Working exercises and keeping your reading current
is essential for getting the most out of lectures, because we use
material from the assigned pages when discussing examples and concepts.
In-class problems:
You will receive one or two problems to complete in class, covering material from previous lectures. The problems are similar in spirit to the Exercises and Review Problems in the text book. The in-class problems provide a measuring stick for what you know and do not know before the exams. They also reward you for doing practice problems in the text and understanding the material.
The dates of all in-class problems are announced at least one week ahead of time. Roughly, you can expect one problem set per week. If you miss an in-class problem set because you were not in class, it counts as a zero, unless you have a pre-approved excuse from the instructor. All requests for excuses must be received by the instructor before the class begins; requests received after class begins will not be granted.
Lab assignments:
Each week, there are weekly data analysis problems completed in
lab. Labs provide hands on experience analyzing data under the
guidance of the TAs. The labs teach you how to apply the skills
discussed in lectures and readings.
You are graded on lab reports that must be turned in by the end of the assigned lab period. Late lab reports will be accepted but be penalized by 50% of the maximum score for the lab assignment. Missed lab assignments cannot be made up without penalty unless pre-approved by the instructor (not the TAs). Labs should be completed in your assigned lab section, unless you are given permission by the instructor or TAs to complete the lab in another section. This is necessary because space in the labs is at a premium. You are permitted to begin the lab before it is due (although not in 01 Old Chem on Thursdays; we need the space).
Final Project:
Web link to instructions for the final project to be presented in poster sessions at the end of the semester in lab sections.
Exams:
Web
link to instructions for Midterm Exam 1 on probability,
exploratory data analysis, and study design.
Web link to instructions for Midterm Exam 2 on statistical
inference.
Web
link to instructions for the Final Exam, which covers the entire
semester.
Academic honesty
You are expected to abide by Duke's Community Standard for all work for this course. Violations of the Standard will be reprimanded by failure of this course and will be reported to the Dean of Students for adjudication. Ignorance of what constitutes academic dishonesty is not a justifiable excuse for violations.
For the web problems, you may work with a study group with others
but must submit your own answers on Blackboard. For in-class
problems and exams, you are required to work alone and for only the
specified time period. For labs, you are allowed and encouraged to
help each other, but each person must complete the lab
independently. On the final project, you work and
submit results in groups.
Procedures if you suspect your work has been graded incorrectly
Every effort will be made to mark your work accurately. You should be credited with all the points you've worked hard to earn! However, sometimes grading mistakes happen. If you believe that an error has been made on an in-class problem or exam, return the paper to the instructor immediately, stating your claim in writing.
The following claims will be considered for re-grading:
(i) points are not totaled correctly;
(ii) the grader did not see a correct answer that is on
your paper;
(iii) your answer is the same as the correct answer, but in a
different form (e.g., you wrote a correct answer as 1/3 and the grader
was looking for .333);
(iv) your answer to a free response question is essentially
correct but stated slightly differently than the grader's
interpretation.
The following claims will not be considered for re-grading:
(v) arguments about the number of points lost;
(vi) arguments about question wording.
Considering re-grades takes up valuable time and resources that TAs
and the instructor would rather spend helping you understand
material. Please be considerate and only bring claims of type (i),
(ii), (iii), or (iv) to our attention.