COURSE UNIT TITLE

: SPECIAL TOPICS IN REGRESSION ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 3132 SPECIAL TOPICS IN REGRESSION ANALYSIS ELECTIVE 3 0 0 5

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR NESLIHAN DEMIREL

Offered to

Statistics
Statistics(Evening)

Course Objective

This lecture aims to gain sufficient knowledge on the special topics in regression analysis and to use statistical software package.

Learning Outcomes of the Course Unit

1   Apply methods of selection of regressors (Stepwise regression, forward selection, backward elimination and all best subsets) using by statistical package.
2   Interpret the residual analysis plots of the multiple linear regression model.
3   Make transformation on the dependent variable.
4   Obtain a model with nonlinear terms in the regression analysis.
5   Apply ridge, lasso, principal componenets regression analysis.
6   Apply elastic net regression analysis.
7   Prepare the project about special topics in regression analysis.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Review for Simle and Multiple Linear Regression
2 Selection of Regressors using by statistical package: Stepwise Regression, Forward Selection, Backward Elimination and All Possible Best Subset
3 Diagnostic of Influential Observations
4 Residual Analysis Plots
5 Transformations in Multiple Regression
6 Ridge Regression
7 Lasso Regression
8 Polynomial Regression
9 Principal Components Regression
10 Elastic Net Regression
11 Project Presentation
12 Project Presentation
13 Project Presentation
14 Project Presentation

Recomended or Required Reading

Textbook(s):
D. Birkes and Y. Dodge, Alternative Methods of Regression, John Wiley & Sons, 1993.
Supplementary Book(s):
1. P.R. Thomas, Modern Regression Methods, Wiley Series, 1996.
2. J.Neter, M.H Kutner, C.J. Nachttsheim, and W. Wasserman, Applied Linear Statistical Models, Irwin, USA, 1996.

Planned Learning Activities and Teaching Methods

Lecture, class discussion, homeworks, project.

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 exams and homeworks.

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
Prof. Dr. Neslihan DEMIREL
e-posta:neslihan.ortabas@deu.edu.tr
Tel: 0232 301 86 00

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 1 14
Preparation for midterm exam 1 10 10
Preparation for final exam 1 20 20
Project Preparation 2 15 30
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 120

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.1543524
LO.2543524
LO.3543524
LO.4543524
LO.5543524
LO.6543524
LO.7543524

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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