Course Objectives

• To understand and be able to interpret basic statistical analyses used in research and in the media. 
  
• To learn how to perform exploratory data analysis on a data set.
• To learn how to estimate population means, percentages, and regression line slopes while accounting for chance error.
• To understand the role of study design in statistical conclusions. 
  

Logistics

• Lecture Times and Location:  Tues. and Thurs., 1:15 - 2:30 PM,  in 128 Physics Building. 
  
• Lab Times and Location: Mon. 8:30 - 9:45 AM, 10:05 - 11:20 AM, 11:40 - 12:55 PM, 1:15 - 2:30 PM.  All in 01 Old Chemistry Building.
• Instructor: Professor Jerry Reiter,   112A Old Chemistry Building,   668-5227,   jerry@stat.duke.edu 
  
• Instructor's Office Hours:   Monday, 10:00 - 11:00 AM.  Tuesday,  2:30 PM -- 3:30 PM.  Wednesday, 2:00 - 3:00 PM.  Thursday, 2:30 - 3:30 PM.   Other hours  available by appointment.
• Teaching Assistants:  Rob Tolliver (gradudate student in sociology) and Sophie Si (graduate student in statistics)
  
• TAs' Office Hours:  All TA office hours are held in the Statistical Education and Consulting Center.  You can attend any of the office hours held in the Center by TAs can answer questions about Statistics 101.   The schedule of office hours is found at the SECC home page.

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 required 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.  You don't need to purchase a calculator that can do graphing or has statistical functions.

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	20 %
Midterm exam 1	17.5 %
Midterm exam 2	17.5 %
In-class problems	15 %
Lab work	10 %
Final project	10 %
Homework problems	10 %

There are no make-ups for exams, in-class problems, labs, or homework 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

Homework  problems:

Homework problems will be posted on the Stat 101 course web site on Blackboard.   You submit answers to Blackboard, which grades your responses.  You do not have to turn in any papers.  Answers are due before the beginning of the specified class.  After that class starts, the problems will not be graded and count as zeros.  You are permitted to work with others on the problems but must submit your anwers individually.  

The homework problems include questions on material covered in previous lectures.  These usually are problems from the text book or problems composed by the instructor.  They also may include questions on the readings assigned for the upcoming class.  These questions have two primary functions: 1) they allow you to practice essential statistical skills; and, 2) they reward you with grading points for keeping your reading current.  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.  The instructor will present lectures assuming that you have read the material for that class.

When you submit your answers, Blackboard will confirm that you have submitted them but not reveal answers.  Solution sheets will be posted after the class ends, so that you can check your results. 

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 and other problems posted by the instructor.  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 beginning of class on the Tuesday after the lab period.   Late lab reports will be accepted until Wednesday at noon 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 Mondays; 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.

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 on a consistent schedule.    The homeworks and in-class problems provide a structured mechanism for doing so.  I recommend starting the problems at least two days before they are due, so that you have sufficient time to come to office hours with questions.  For particularly difficult concepts, I recommend working problems beyond those assigned, so that you get additional practice.  Then, visit your instructor and TAs to review answers.

Most sections in the text are followed by a set of exercises.  I recommend working two or three of these problems as you are reading.  This allows you to gauge what you did and did not understand on first reading. so that you can re-read if necessary.  After reading, go back and do a good chunk of the remaining exercises.  There are review problems at the end of most chapters.  I recommend working a few problems each week from previous review exercises to maintain and solidify your understanding.  Answers to the exercises are in the back of the book, and you can check with the TAs in the Statistical Education Center about answers to review problems.

To maximize your chances of success in Statistics 101, I recommend that you spend at least 6 hours per week outside of the classroom working on problems.  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 sub-optimal strategy, because you won't spend enough time to develop a thorough understanding of the material.    There are some useful handouts describing strategies for studying for quantitative courses on the web site for Duke's Academic Skills Instructional Program (at the site, select "Math and Quantitative Studies").   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).

Finally, visit the TAs and instructor when you get stuck or even when you figure something out and want to share your victory.  Almost everyone who does well in this course asks for help at some point in the semester.  Think of us as allies in your efforts to learn statistics.  Nothing makes us happier than you understanding all the material!
 

Academic honesty

You are expected to abide by Duke's Community Standard for all work for this course.  Violations of the Standard will result in a failing grade for this courses 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 homework 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 and turn in his or her own written report.    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.