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41 East 11 Street, 7 Floor
New York, New York 10003
Telephone: 212 992-6741
Facsimile: 212 995-4877
Web: giph.nyu.edu
Email: [email protected]
GPH-GU 2920 Biostatics: Regression and Multivariable Modeling
Class Schedule: Thursday 2:15pm-3:55pm
Class Location: 194 Mercer Street, Room 203
Semester and Year: Spring 2015
INSTRUCTORS:
Associate Professor: Mengling Liu
Phone: (212) 263-6614
Email: [email protected]
Assistant Professor: Yixin Fang
Phone: (212) 263-6527
Email: [email protected]
Office: 41 East 11th Street, Room 727G
Office Hours: 03/24-05/11; Thursday 10am-11am
Office: 41 East 11th Street, Room 727G
Office Hours: 01/26-03/14; Thursday 10am-11am
COURSE DESCRIPTION: This course offers students advanced instruction in statistical models that cover
useful quantitative tools in public health research. The course focuses on statistical techniques and data
analysis that utilize general linear regression models for continuous, categorical, or discrete outcomes
commonly seen in health and policy studies. Examples are drawn from broad areas of public health and
policy research. In this course students will gain knowledge and understanding of statistical concepts of
generalized linear models and the implementation and application of the techniques.
COURSE OBJECTIVES:
By the end of the course, students will be able to:
1. Learn about the different characteristics of continuous, categorical and discrete outcomes, and their
inherent challenges to data analysis.
2. Be introduced to and master the use of a wide range of statistical models, including linear, logistic,
and Poisson regression models.
3. Build skills in applying appropriate regression models to analyze data and interpret results using
statistical software.
4. Develop writing skills for quantitative research on public health and policy research.
5. Gain knowledge of statistical theories, specifically likelihood-based inferences, which justify
statistical practices.
PRE-REQUISITES: None for PhD students. Masters students must have completed 20 credits, have a GPA
of 3.5 or higher, and receive permission of the instructor.
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COURSE REQUIREMENTS AND EXPECTATIONS:
Course requirements consist of required readings, two problem sets and two projects. Most of the
material of the course is covered in formal lectures, and remaining is assigned as optional readings.
Students should have access to the SAS program and are encouraged to frequently practice SAS
procedures learned in the class. All problem sets must be submitted at the beginning of class on the due
day, and all projects must be submitted by email before 5pm on the due day. No late submission will be
accepted without instructor’s permission before it is due.
GRADING RUBRIC:
Item:
Problem Set #1
Percentage
20%
Project #1
30%
Problem Set #2
20%
Project #2
30%
GRADING SCALE:
A:
93-100
A-:
90-92
B+:
87-89
B:
83-86
B-:
80-82
C+:
C:
C-:
D+:
D:
F:
77-79
73-76
70-72
67-69
60-66
<60
NYU CLASSES:
NYU Classes will be used extensively throughout the semester for assignments, announcements, and
communication. NYU Classes is accessible through at https://home.nyu.edu/academics
COURSE OUTLINE:
Date
WK 1 (01/29)
WK 2 (02/05)
WK 3 (02/12)
WK 4 (02/19)
WK 5 (02/26)
WK 6 (03/05)
WK 7 (03/12)
WK 8 (03/19)
WK 9 (03/26)
WK 10 (04/02)
WK 11 (04/09)
WK 12 (04/16)
WK 13 (04/23)
WK 14 (04/30)
WK 15 (05/07)
Topics Covered
Course overview; Linear regression
Applied linear regression; Intro to SAS
Categorical data analysis
Likelihood theory of GLMs; SAS
Logistic regression
Probit regression
Two case studies; SAS applications
Spring Break (no class)
Ordinal models
Multinomial models
Poisson and negative binomial models
Survival analysis
Models for clustered data
Generalized estimating equations; SAS
Two case studies; SAS applications
Assigned Reading
Chapter 1
Litter SAS book
Handout
Chapter 2
Chapter 3
Chapter 3
Handout
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8 and Handout
Handout
Handout
Assignment
Prob #1
Proj #1
Prob #2
Proj #2
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TECHNOLOGY POLICY:
Students are encouraged to use laptop (with SAS and R installed) in the classroom.
READING/VIEWING LIST:
(1) Required: John P. Hoffmann. 2003 Generalized Linear Models: An Applied Approach. Pearson.
(2) Required: Lora Delwiche and Susan Slaughter. 2012. The Little SAS Book: A Primer, Fifth Edition, SAS.
(3) Recommended: James P. McCullagh and J. A. Nelder. 1989. Generalized Linear Models, Second
Edition, Chapman & Hall/CRC;
(4) Supplemental reading materials will be uploaded.
STATEMENT OF ACADEMIC INTEGRITY:
The NYU Global Institute of Public Health values both open inquiry and academic integrity. Students in
the program are expected to follow standards of excellence set forth by New York University. Such
standards include respect, honesty and responsibility. The GIPH does not tolerate violations to academic
integrity including:
 Plagiarism
 Cheating on an exam
 Submitting your own work toward requirements in more than one course without prior
approval from the instructor
 Collaborating with other students for work expected to be completed individually
 Giving your work to another student to submit as his/her own
 Purchasing or using papers or work online or from a commercial firm and presenting it as your
own work
Students are expected to familiarize themselves with the GIPH and University’s policy on academic
integrity as they will be expected to adhere to such policies at all times – as a student and an alumni of
New York University.
Plagiarism
Plagiarism, whether intended or not, is not tolerated in the GIPH. Plagiarism involves presenting ideas
and/or words without acknowledging the source and includes any of the following acts:
 Using a phrase, sentence, or passage from another writer's work without using quotation marks
 Paraphrasing a passage from another writer's work without attribution
 Presenting facts, ideas, or written text gathered or downloaded from the Internet as your own
 Submitting another student's work with your name on it
 Submitting your own work toward requirements in more than one course without prior
approval from the instructor
 Purchasing a paper or "research" from a term paper mill.
Students in the GIPH and GIPH courses are responsible for understanding what constitutes plagiarism.
Students are encouraged to discuss specific questions with faculty instructors and to utilize the many
resources available at New York University.
Disciplinary Sanctions
When a professor suspects cheating, plagiarism, and/or other forms of academic dishonesty,
appropriate disciplinary action is as follows:
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
The Professor will meet with the student to discuss, and present evidence for the particular
violation, giving the student opportunity to refute or deny the charge(s).
 If the Professor confirms that violation(s), he/she, in consultation with the Program Director may
take any of the following actions:
o Allow the student to redo the assignment
o Lower the grade for the work in question
o Assign a grade of F for the work in question
o Assign a grade of F for the course
o Recommend dismissal
Once an action(s) is taken, the Professor will inform the Program Director, and inform the student in
writing, instructing the student to schedule an appointment with theAssociate Dean for Academic
Affairs, as a final step. The student has the right to appeal the action taken in accordance with the GIPH
Student Complaint Procedure.
STUDENTS WITH DISABILITIES:
Students with disabilities should contact the Moses Center for Students with Disabilities regarding the
resources available to them, and to determine what classroom accommodations should be made
available. More information about the Moses Center can be found here. must appear on the syllabus.
Information about the center can be found here: https://www.nyu.edu/life/safety-healthwellness/students-with-disabilities.html. Students requesting accommodation must obtain a letter from
the Moses Center to provide to me as early in the semester as possible.
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