COURSE UNIT TITLE

: MULTIVARIATE DATA ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BIL 2014 MULTIVARIATE DATA ANALYSIS COMPULSORY 3 0 0 5

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR ÇAĞIN KANDEMIR ÇAVAŞ

Offered to

Computer Science

Course Objective

Multivariate data occur in all branches of science. Teaching to students the multivariate statistical methods which are often met in real life is the objective of this course.

Learning Outcomes of the Course Unit

1   Understanding statistical concepts of linear algebra terms (determinant, eigenvalu, eigenvector etc.),
2   Obtaining multivariate descriptive statistics (mean vector, variance-covariance matrix, correlation matrix etc.),
3   Ability to analyze Hypothesis Tests and Multiple Hypothesis Tests
4   Applying Principal Component Analysis,
5   Applying Factor Analysis,
6   Applying Linear Discriminant Analysis,
7   Applying Discriminant Analysis for two multivariate normal populations.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Statistical Concepts Used in Multivariate Data Analysis
2 Introduction to Hypothesis Testing
3 Hypothesis Tests for Population Mean (One Sample)
4 Hypothesis Tests for Population Mean (Two Samples)
5 Covariance and Correlation Matrix in Multivariate Data
6 Multivariate Hypothesis Testing
7 Multivariate Hypothesis Testing
8 Midterm Exam
9 Variance-Covariance Matrix and Principal Component Analysis (PCA)
10 Estimation of Principal Components, PCA via Correlation Matrix
11 Objectives of Factor Analysis, Factor Analysis Equations
12 Choosing the appropriate number of Factors
13 Linear Discriminant Analysis
14 Linear Discriminant Analysis and Overview

Recomended or Required Reading

Textbook(s):
Anderson T. W., An Introduction To Multivariate Statistical Analysis, Wiley-Interscience, 2003.
Alpar, R., Uygulamalı Çok Değişkenli Istatistiksel Yöntemler, Detay Yayıncılık, 2011
Supplementary Book(s):
Grinn, L. G. and Fidell, L. S., Reading and Understanding More Multivariate Statistics, APA Books, Washington D. C., 2000.
Tabachnick, B. G., & Fidell, L. S., Using Multivariate Statistics, Harper Collins College Publishers, 2001

Planned Learning Activities and Teaching Methods

Lecture and presentation

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE MTE * 0.40 + FIN * 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + FIN * 0.60


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

If needed, other assessment methods can be added to the table given below.

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

cagin.kandemir@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 3 39
Preparation for midterm exam 1 15 15
Preparation for final exam 1 18 18
Preparing presentations 0 0 0
Design Project 0 0 0
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 115

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.1443
LO.2443
LO.3443
LO.4443
LO.5443
LO.6443
LO.7

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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