Download 1 - METU

Middle East Technical University
Department of Statistics
Spring 2008-2009
STAT 356 STATISTICAL DATA ANALYSIS
Credit: (3-2) 4
Prerequisite Courses: STAT 156, STAT 291
Instructor: Dr. Özlem İLK, 134, Department of Statistics
E-mail: [email protected]
Phone: 210 53 26
Assistant: M.Tuğba ERDEM, 141, Department of Statistics
Course Web page: http://www.metu.edu.tr/~oilk/s356.html
Course Objectives: Recent advances in computers and computer sciences enabled researchers
working in various fields to store and process huge amount of data. As a result, new
methodologies have been developed for approaching huge and complex data. This computeraided course is designed to equip the students with the tools and techniques of handling massive
data. It provides the students with the essential information for handling complex data.
Textbooks: No specific textbook.
Reference Books & Papers:
 Agresti, A., (2002) Categorical Data Analysis, 2nd edition, Wiley, New York.
 Chatfield, C., (1980) Problem Solving: A Statistician’s Guide, 2nd edition, Chapman & Hall.
 Hoaglin, D.C., Mosteller, F. and Tukey, J.W., (1983) Understanding Robust and Exploratory Data
Analysis, Wiley.
 Neter, J., Kutner, M.H., Nachtsheim, C.J. and Wasserman, W., (1996) Applied Linear Statistical
Models, 4th edition, Irwin.
 Tukey, J.W., (1977) Exploratory Data Analysis, Addison-Wesley.
 Dunham, M.H., (2002) Data mining: Introductory and advanced topics, Prentice Hall.
 Han, J. And Kamber,. M., (2000) Data mining: Concepts and techniques, Morgan Kaufmann Pub.
 Little, R.J.A and Rubin, D.B., (2002) Statistical Analysis with Missing Data, 2nd edition,
Chichester:Wiley .
Tentative Course Outline:
Introduction
Graphical and tabular representation of data
Approaches to finding the unexpected in data
Exploratory data analysis
Analysis of categorical data
Handling missing data
Elements of robust estimation
Smoothing methods
Data mining
Case Studies
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Grading Policy:
Homeworks
Midterm Exam
Final Exam
Class Reports + Class Participation
Attendance
: 15 %
: 30 %
: 40 %
: 10 %
: 5%
Do’s and Don’ts:
 You are expected to attend all classes and exams.
 No matter what the situation is, I have ZERO tolerance for plagiarizing (cheating). If one choose
to copy other’s work, s\he chooses grade over learning and is unfair to her\his classmates.
There will be legal consequences.
 Late homeworks will not be accepted.
 Please do not have last minute requests. Especially, don’t try to contact me right before exams.
 Please check your e-mails and course web page frequently. We may contact you via e-mail on
subjects concerning class.
 Please respect our schedule. For discussions and questions, come during our office hours, or
make an appointment in advance.
 No calls to my cell or home phone.
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