Spring 2013


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Instructor:                John D. Pesek, Jr.

                                    227 Townsend Hall

Phone:  (302)831-1319




Time and Place:       Mondays and Tuesdays 6:00-7:15pm – Room 132 Townsend Hall


Office Hours:           By appointment


Texts:                        Required: An Introduction to Categorical Data Analysis, 2nd Edition by Alan Agresti, Wiley 2007.

                                    Optional: The Little SAS Book: A Primer, 5th Edition, by Lora A Delwiche and Susan J. Slaughter, SAS Institute Inc., 2012.


Objectives:               The purpose of this course is to learn how to analyze, interpret and assess the validity of logistic regression models in various applied contexts such as medicine and marketing. The course will use primarily procedures in the SAS system to do the data analysis.


UNIVERSITY OF DELAWARE GENERAL EDUCATION GOALS: The objectives of this course align with the following General Education Goals (bolded):


Attain effective skills in (a) oral and (b) written communication, (c) quantitative reasoning, and (d) the use of information technology – By doing homework assignments, supportive reading and lab exercises. These involve analysis and computation, both by hand and using SAS software.

Learn to think critically to solve problems – By doing homework assignments and class exercises. These involve analysis and computation, both by hand and using SAS software.

Be able to work and learn both independently and
collaboratively – 
By doing independent homework assignments and class exercises in collaboration with a partner. These involve analysis and computation, both by hand and using SAS software.

Course Requirements:


1.    In case class is cancelled because of bad weather or other contingencies, make up classes may be necessary. It may also be necessary to reschedule exams.

2.    On Tuesdays, March 5, 2013 and April 16, 2013 there will be inclass examinations. There will also be a final at a date to be announced.
Important! The final and inclass exams must be taken at the scheduled time unless prior arrangements are made. There must be adequate cause in the judgment of the instructor.

Note: Circumstances such as weather may cause rescheduling of the exams.

Note: Plane reservations leaving before the final date are not considered adequate cause. 

Note: Attendance at a wedding is not adequate cause for taking exams at other times. This is regardless of whether you are getting married, you are a member of the wedding party, or you are just in attendance.

Note:  Having more than one final on the same day is also not adequate cause. 
Note: There are many legitimate reasons such as illness and family emergency.  If you have one of these, please contact the instructor.

Note: According to university policy, students with either learning and/or physical disabilities are entitled to special consideration. To qualify for this consideration, the instructor must be given official notification of the disability from the appropriate university unit.

Both the exams and the final will be cumulative.  Some questions on the exam will require the demonstration of lessons learned in class and in the homework while some will also require the resourceful use of that knowledge. When preparing for exams, it is important to remember that in addition to using the skills acquired in class, it is also necessary to be able to decide which skills are needed for a particular problem.

I often give out practice exams. These are usually the previous year's exams. You should note the standard caution that the same topics may not be covered on the exams as the practice exams.

3.    There are also a number of required assignments.  Each assignment is due on a specific date to be announced. Students are responsible for providing a readable and understandable presentation of results. Without an appropriate excuse an assignment turned in after the due date will receive a grade of zero. In general assignments will be submitted using Sakai.  

4.    We will follow parts of the text fairly closely. Students are expected to keep up on reading the text and will be asked questions about what they read in class. There will be weekly study assignments as well.

5.    Class attendance is very important.  Students are held responsible for all work covered and for meeting course deadlines.

Note: Missing class in order to complete work for another class or to study for an exam in another class is not a proper excuse.

6.    During class students are expected to be respectful of each other and the instructor.  Questions are welcome if they are germane to the current topic. Other questions are welcome when asked in private.  In this class data will come from a variety of disciplines.  It is natural to be most interested in data from your own field of study.  However, respect and courtesy toward data from other disciplines is expected.

7.    Academic honesty is expected at all times (See the Student Guide to University Policies for complete information on the Code of Student Conduct  at ).

1.    In general students are expected to know and follow the university's policy on responsible computer use.

Grading Procedure:


The final course grade will be based upon the student’s performance on the assignments and the exams.  The assignments will count 20-30 percent of the grade, the exams 70-80 percent of the grade.

Important information for listeners (official auditors). At the University of Delaware you may take a course as a listener. Then you are not required to take exams or turn in homework although you may do so. However, you ARE required to attend class. If a student has listener status and has a large number of unexcused absences, the instructor may give a grade of LW (for listener withdrawn) instead of L (for listener).  I have decided to enforce this policy. While an LW will not affect your GPA or your graduation, prospective employers are often concerned to see withdrawn courses on your transcript and it is best to avoid them. 


Requests for score changes:


If you feel either an assignment or an exam deserves a higher score, you may make a written request.  The written request must be made on a separate sheet of paper and the assignment or exam must be attached.  If you are still not satisfied, you may make an appointment with the instructor to discuss the matter.  For Sakai assignments, written requests should be made through the Sakai message system.  In this case there will be no need to include the assignment since I will have retained a copy.




Students are encouraged to visit the instructor in his office.  Students may make appointments or drop by (In the last case the instructor may not always be available). Students are also encouraged to communicate with the instructor using E-mail or the Sakai message system.



Announcements about the course will be made by e-mail.  Students are expected to pay close attention to e-mail messages from the instructor.




In general handouts will be available on Sakai in “pdf” format. Students are expected to download and print copies to have available in class.  Some handouts may still be provided in class. To access the handouts, go to the URL  



Tentative list of topics (Some may not be covered):


z  Categorical Response Data

z  Nominal/Ordinal Scales

z  Review of Binomial and Multinomial Distributions

z  Inference for a proportion

z  Inference for discrete data

z  Contingency Tables

k    Joint, Marginal and Continuous Distributions

k    Independence

k    Comparing proportions in two by two tables

z  Odds ratios

z  Tests of independence

z  Tests of independence for ordinal data

z  Exact inference for small samples

z  Association in three-way tables

z  Generalized linear models

k    In general

k    Binary data – logit and probit

k    Others Poisson and Negative binomial

z  Statistical Inference and model checking

z  Logistic regression

k    Interpretation

k    Odds ratios

k    Inference

k    Dummy variables

k    Model selection

k    Model checking

k    Sample size and power

z  Multicategory logit models

k    Multinomial

k    Ordinal(cumulative logits)

z  Log linear models

z  Matched pairs

z  Correlated and clustered responses

z  Mixed logistic regression models