COURSE SYLLABUS

MULTIVARIATE STATISTICS

1	Course Title:	MULTIVARIATE STATISTICS
2	Course Code:	BIL5120
3	Type of Course:	Optional
4	Level of Course:	Second Cycle
5	Year of Study:	1
6	Semester:	2
7	ECTS Credits Allocated:	4
8	Theoretical (hour/week):	2
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	No
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Prof. Dr. AYSAN ŞENTÜRK
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	Email: aysan@uludag.edu.tr Tel: 224 2942231 Uludağ Üniversitesi Eğitim Fakültesi, A Blok, BÖTE Bölümü, 16059 Nilüfer,Bursa
18	Website:
19	Objective of the Course:	The aim of the course is the examination of the structure of the dependency between the units and units of classification by expressing a large number of variables is an expression as a smaller number of variable with dimensionality reduction methods in multivariate data set.
20	Contribution of the Course to Professional Development	Statistical methods and techniques of education is to give the students can apply to their fields.

Learning Outcomes:

1	To be able to apply the necessary techniques in order to make inferences about the relationships based on multivariate data. ;
2	To be able to know the parameters of multivariate normal distribution. ;
3	To be able to apply of multivariate test of hypotheses;
4	To be able to apply of factor analysis.;
5	To be able to apply of cluster analysis. ;
6	To be able to use logistic regression analysis for a purpose. ;
7	To be able to multivariate statistical methods use in many interdisciplinary branches of sciences. ;
8	To be able to perform analyses using statistical package programs during the application of multivariate statistical methods for data sets. ;

22	Course Content:

Week	Theoretical	Practical
1	Basic concepts areas of use for multivariate statistical analysis
2	Matrix theory for multivariate statistical analysis
3	Continious multivariate distributions
4	Multivariate normal distribution
5	Multivariate hypothesis testing
6	Principal component analysis
7	Factor analysis
8	Canonical correlation analysis
9	Discriminant analysis
10	Logistic regression analysis
11	Cluster analysis
12	Multidimensional scale
13	Comparison of principal component analysis and multidimensional scale
14	Multivariate regression analysis

23	Textbooks, References and/or Other Materials:	1. Hüseyin Tatlıdil, Uygulamalı Çok Değişkenli İstatistiksel Analiz, Ziraat Matbaacılık A. Ş. Ankara, 2002. 2. Editör: Şeref Kalaycı, SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri, Asil Yayınevi, 2010. 3. Reha Alpar, Çok Değişkenli İstatistiksel Yöntemler, Detay Yayıncılık, 2011. 4. Şener Büyüköztürk, Güçlü Şekercioğlu ve Ömay Çokluk, Sosyal Bilimler İçin Çok Değişkenli İstatistik - SPSS ve LISREL Uygulamaları, Pegem A Yayıncılık, 2012. 5. Ali Sait Albayrak, Uygulamalı Çok Değişkenli İstatistik Teknikleri, Asil Yayın Dağıtım, 2006. 6. Wolfrang Karl Hardle & Leopold Simar, Applied Multivariate Statistical Analysis, Springer-Verlag, 2012. 7. V. Serdobolskii, Multivariate Statistical Analysis: A High-Dimensional Approach, Kluwer Academic Publishers, 2010. 8. Parimal Mukhopadhyay, Multivariate Statistical Analysis, World Scientific Publishing, 2009. 9. Brian Everitt & Torsten Hothorn, An Introduction to Applied Multivariate Analysis with R, Springer, 2011. 10. Bruce L. Brown, Suzanne B. Hendrix, Dawson W. Hedges, Timothy B. Smith, Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach, John Wiley & Sons., 2012. 11. Joseph F. Hair, William C. Black, Barry J. Babin, Rolph E. Anderson, Multivariate Data Analysis, Prentice Hall, 2009.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	0	0
Quiz	0	0
Homeworks, Performances	0	0
Final Exam	1	100
Total	1	100
Contribution of Term (Year) Learning Activities to Success Grade		0
Contribution of Final Exam to Success Grade		100
Total		100
Measurement and Evaluation Techniques Used in the Course	Measurement and evaluation are made with multiple choice test questions and written questions.
Information

ECTS / WORK LOAD TABLE

CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High