COURSE UNIT TITLE

: COMPUTATIONAL STATISTICS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5067 COMPUTATIONAL STATISTICS ELECTIVE 3 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR EMEL KURUOĞLU KANDEMIR

Offered to

Statistics
STATISTICS

Course Objective

This course is about modern, computationally-intensive methods in statistics. It emphasizes the role of computation as a fundamental tool of discovery in data analysis, of statistical inference, and for development of statistical theory and methods.
The objective of the course is to introduce the students to the very powerful facilities that the scientific computer programming languages (Matlab, R, etc.) for statistical computing.

Learning Outcomes of the Course Unit

1   An understanding of fundamental ideas of computational statistics,
2   Carry out statistical analysis with the use of the programming languages,
3   Produce graphical display in the programming languages,
4   An understanding of advanced structure of statistical programming,
5   Develop the students analytical abilities and ability to present and criticize arguments.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Fundamental Topics in Computational Statistics
2 An Introduction to MATLAB and R
3 Data Types, Manipulation of Data
4 Operators, Functions
5 Exploratory Data Analysis Histogram, Boxplot, Plots, 3D Scatter Plots, QQ plot Assignment 1
6 Descriptive Statistics in MATLAB and R
7 Random Number Generation in Matlab and R. Assignment 2
8 Mid-term
9 Regression Models using by MATLAB Assignment 3
10 Regression Models using by R
11 Applications of Parametric Tests and Nonparametric Tests using by MATLAB
12 Robust Statistical Models and Data Fitting, Maximum entropy models Assignment 4
13 Applications of Computational Statistics with MATLAB and R
14 Applications of Computational Statistics with MATLAB and R

Recomended or Required Reading

Textbook:
Wendy L.Martinez, Angel R.Martinez, Computational Statistics Handbook with MATLAB, CRC Press, 2002.

Supplementary Books:
1. James E. Gentle, Elements of Computational Statistics, Published by Springer-Verlag, 440 pages, 2002.
2. Günther Sawitzki, Computational Statistics: An Introduction to R, Chapman & Hall/CRC Press, Boca Raton (FL), 2009. ISBN: 978-1-4200-8678-2.
3. Joaquim P. Marques de Sá, Applied Statistics Using SPSS, STATISTICA, MATLAB and R, Springer, 2007.
4. S.R. Otto and J.P. Denier, An Introduction to Programming and Numerical Methods in MATLAB, Springer, 2005.

Planned Learning Activities and Teaching Methods

The course consists of lecture and homework.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + ASG * 0.40 + FIN * 0.30
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + MAKRASG * 0.40 + MAKRRST * 0.30


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams, presentation and homework.

Language of Instruction

English

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 graduate policy at http://web.fbe.deu.edu.tr

Contact Details for the Lecturer(s)

Assist. Prof.Dr. Emel KURUOĞLU
e-posta: emel.kuruoglu@deu.edu.tr
Tel: 0232 301 95 10

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparation for midterm exam 1 48 48
Preparation for final exam 1 48 48
Preparations before/after weekly lectures 12 1 12
Preparing assignments 4 6 24
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 178

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.134
LO.225444
LO.3444
LO.444
LO.5444

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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