COURSE UNIT TITLE

: REGRESSION ANALYSIS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5075 REGRESSION ANALYSIS 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

PROFESSOR DOCTOR NESLIHAN DEMIREL

Offered to

Statistics (English)
STATISTICS (ENGLISH)
Statistics (English)

Course Objective

The objective is to study general models in regression analysis and discus the assumptions in simple and multiple regression, measure model adequacy in regression, residual analysis, multicollinearity, heteroskedasticity, nonnormality and nonlinearity, to select best regression model, to make variable selection and to give an introduction to nonlinear regression.

Learning Outcomes of the Course Unit

1   Simple Linear Regression and Correlation
2   Residual Analysis
3   Multiple Regression
4   Measures of Model Adequacy
5   Polynominal Regression
6   Problems in Multiple Regression
7   The Model Building Problem
8   Model Evaluation

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Statistics
2 Exploratory Data Analysis
3 Simple Linear Regression and Correlation
4 Interval Estimation in Simple Linear Regression
5 Residual Analysis
6 Multiple Regression
7 Confidence Intervals in Multiple Linear Regression
8 Measures of Model Adequacy
9 Polynominal Regression
10 Problems in Multiple Regression
11 The Model Building Problem-Stepwise Regression Methods
12 The Model Building Problem-All Possible Subsets Method
13 Model Evaluation
14 Project Presentation

Recomended or Required Reading

Textbook:
Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models (Vol. 5). New York: McGraw-Hill Irwin.

Supplementary Book(s):
Draper, N. R., & Smith, H. (1998). Applied regression analysis (Vol. 326). John Wiley & Sons.

Mendenhall, W., Sincich, T., & Boudreau, N. S. (1996). A second course in statistics: regression analysis (Vol. 5). Upper Saddle River, NJ: Prentice Hall.


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 and problem solving.

Assessment Methods

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

It is necessary that attendance to the 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.

Contact Details for the Lecturer(s)

DEU Fen Fakültesi Istatistik Bölümü
Prof. Neslihan DEMIREL
e-mail: neslihan.ortabas@deu.edu.tr
Tel: 0232 301 86 00

Office Hours

Please send an e-mail for a meeting request.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 2 28
Preparation for midterm exam 1 30 30
Preparation for final exam 1 40 40
Project Preparation 1 30 30
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 174

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.14555
LO.24555
LO.34555
LO.44555
LO.54555
LO.64555
LO.74555
LO.84555