COURSE UNIT TITLE

: ROBUST ANALYSIS OF VARIANCE METHODS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 4186 ROBUST ANALYSIS OF VARIANCE METHODS ELECTIVE 3 0 0 5

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ABDULLAH FIRAT ÖZDEMIR

Offered to

Statistics
Statistics(Evening)

Course Objective

Robust methods based on robust estimators and bootstrap methodology will be examined. ANOVA-F test and similar nonparametric methods will be compared by using statistical programming language R.

Learning Outcomes of the Course Unit

1   Able to Use One-way ANOVA F-test
2   Able to evaluate the assumptions of ANOVA F-test.
3   Learning the basic tools and definitions of statistical programming language R.
4   Using basic bootstrap methods
5   Applying methods that are robust against heterogeneity of variances problem
6   Applying methods that are robust against heterogeneity of variances and nonnormality.
7   Applying multiple comparison methods with robust estimators.
8   Performing simulation studies by using R.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Why do researchers need robust inferential methods
2 Evaluating the Normality and homogeneity of variances assumptions and basic definitions of statistical programming language R.
3 ANOVA F-test and the results of violations of Normality and homogeneity of variances assumptions.
4 Welch Test and Brown-Forsythe Test
5 B2 Testi and g&h distribution
6 Midterm Exam
7 Welch test with trimmed means
8 Welch test with trimmed means and bootstrap-t
9 B_tk^2 Test and Comparing medians of k groups.
10 Schrader-Hetmansperger Method, Rust-Fligner Method
11 Multiple Comparison with median and trimmed mean, Extending the Yuen method with trimmed mean
12 Multiple comparisons with bootstrap-t and percentile bootstrap and Rom test.

Recomended or Required Reading

Rand R.Wilcox, Introduction to Robust Estimation and Hypothesis Testing , Elsevier, 2012.

Rand R. Wilcox, Applying Contemporary Statistical Techniques , Academic Press, 2003.

Planned Learning Activities and Teaching Methods

Lecture and problem solving.

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

None

Assessment Criteria

Evaluation of 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)

Assoc. Prof. Dr. A. Fırat Özdemir
DEU Fen Fakültesi Istatistik Bölümü
e-posta: firat.ozdemir@deu.edu.tr
Tel: 0232 301 8552

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 11 3 33
Preparations before/after weekly lectures 11 2 22
Preparation for midterm exam 1 25 25
Preparation for final exam 1 40 40
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 124

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.15555
LO.25555
LO.35555
LO.45555
LO.55555
LO.65555
LO.75555
LO.85555

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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