COURSE UNIT TITLE

: ALTERNATIVE REGRESSION METHODS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5024 ALTERNATIVE REGRESSION METHODS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR NESLIHAN DEMIREL

Offered to

Statistics
STATISTICS

Course Objective

To teach the basic principles and the theory of regression methods needed when the assumptions for ordinary linear regression are not satisfied, and to make the students to be able to apply the methods on data sets by statistical computer packages.

Learning Outcomes of the Course Unit

1   Building logistic regression model for binary and multiple outcome variables.
2   Building nonparametric regression model.
3   Defining the goals of robust regression
4   Building robust regression model.
5   Building Ridge Regression

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Logistic Regression Model
2 Parameter estimation for Logistic Regression Model for Binary Outcome Variable,Preparing Individual Assignments
3 The Interpretation of parameters, Goodness of Fit Test for Logistic Regression Model for Binary Outcome Variable
4 Logistic Regression Model for Multiple Outcome Variable, Logistic Regression Model based on the Reference Category,Preparing Individual Assignments
5 Introduction to Nonparametric Regresion
6 Relaxing Regression Assumptions,Preparing Individual Assignments
7 Monotone Regression
8 Smoothers
9 Need for Robust regression, Types of outliers, Goals of Robust Regression
10 Proposed High Breakdown Point estimators
11 Least Trimmed Square regression
12 Introduction to Ridge Regression, determining the parameter k
13 Ridge Regression For Prediction
14 Inferences in Ridge Regression

Recomended or Required Reading

Textbook:
Thomas P.Rryan Modern Regression Methods, by, Wiley series, 1996

Planned Learning Activities and Teaching Methods

Lecture and Homeworks

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG 1 ASSIGNMENT 1
2 ASG 2 ASSIGNMENT 2
3 ASG 3 ASSIGNMENT 3
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE ASG 1 + ASG 2 + ASG 3/3 * 0.40 + FIN * 0.60
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) ASG 1 + ASG 2 + ASG 3/3 * 0.40 + RST * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of homework assignments and final exam.

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 undergraduate policy at http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

DEU Fen Fakültesi Istatistik Bölümü
e-mail: neslihan.ortabas @deu.edu.tr
Tel: 0232 301 85 73

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 3 42
Preparing assignments 3 20 60
Preparation for final exam 1 45 45
Final 1 2 2
TOTAL WORKLOAD (hours) 191

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.155555
LO.255555
LO.3555
LO.455555
LO.555555

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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