COURSE UNIT TITLE

: MULTIVARIATE STATISTICAL ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 4037 MULTIVARIATE STATISTICAL ANALYSIS COMPULSORY 2 2 0 6

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR ESIN FIRUZAN

Offered to

Statistics
Statistics(Evening)

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 (rank, determinant, eigenvalu, eigenvector etc.),
2   Obtaining multivariate descriptive statistics (mean vector, variance-covariance matrix, correlation matrix etc.),
3   Interpreting three or more dimensional graphs,
4   Applying Principal Component Analysis,
5   Applying Factor Analysis,
6   Applying Discriminant Analysis for two multivariate normal populations
7   Cluster Analysis
8   Multi Dimensional Scaling

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 1.Week Types of variables, Data Matrices and Vectors, Data Subscripts
2 2.Week The Multivariate Normal Probability Density Function, Bivariate Normal Distributions
3 3.Week Mean Vectors and Variance-Covariance, Correlation and Standardized data matrices
4 4.Week Three-Dimensional Data Plots, Plots of Higher Dimensional Data
5 5.Week Multivariate Normal Distribution Contour Plot
6 6.Week Eigenvalues and eigenvectors, Geometric Descriptions
7 7.Week Principal Components Analysis (PCA)
8 8.Week PCA with prcomp, PCA with PCA, PCA with SVD
9 9.Week Objectives of Factor Analysis, Factor Analysis Equations
10 10.Week Choosing the Appropriate Number of Factors, Rotating Factors
11 11.Week Discriminant Analysis for Multivariate Normal Distribution
12 12.Week Cost Functions and Prior Probabilities, A General Discriminant Rule (Two Populations)
13 13.Week Cluster Analysis
14 14.Week Multi DImensional Scaling

Recomended or Required Reading

Textbook(s):
Johnson, R.A. ve Wichern, D.W., 2007, Applied Multivariate Statistical Analysis, 6th Edn, Pearson International Edition

Supplementary Book(s):
1. Johnson, D.E. (1998) Applied Multivariate Methods for Data Analysts, Duxbury

Planned Learning Activities and Teaching Methods

Lecture format, built around the textbook readings and computer applications with numerous examples chosen to illustrate theoretical concepts. Lots of drill with emphasis on practice. Questions are encouraged and discussion of material stressed.
Lecture, project and presentation.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRJ PROJECT
3 FIN FINAL EXAM
4 FCGR FINAL COURSE GRADE (RESIT) VZ * 0.30 + PRJ * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) VZ * 0.30 + PRJ * 0.30 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of project, presentation and exams

Language of Instruction

Turkish

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the undergraduate policy at http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

DEU Fen Fakültesi Istatistik Bölümü
e-mail: esin.firuzan@deu.edu.tr
Tel: 0232 301 85 61

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Tutorials 13 2 26
Preparations before/after weekly lectures 12 1 12
Preparation for midterm exam 1 24 24
Preparation for final exam 1 33 33
Preparing assignments 1 30 30
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 155

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.1544
LO.2544
LO.3544
LO.4554443455
LO.5554443455
LO.6554443455
LO.7
LO.8

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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