COURSE UNIT TITLE

: REGRESSION ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 3031 REGRESSION ANALYSIS COMPULSORY 4 0 0 6

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR NESLIHAN DEMIREL

Offered to

Statistics
Statistics(Evening)

Course Objective

Regression analysis is the most important multivariate statistical methods which can be used for prediction. In this course, regression models which determines cause-and-result relation between variables will be builded and used for prediction and interpretation.

Learning Outcomes of the Course Unit

1   Define the concepts of dependent and independent variable(s)
2   Obtain estimators of the parameters in simple linear regression model using the method of least squares
3   Estimate the variance of the regression model
4   Calculate the coefficient of correlation and coefficient of determination
5   Describe the assumptions of simple and multiple linear regression model
6   Obtain analysis of variance table.
7   Apply variable selection techniques such as stepwise regression, forward selection, backward elimination and all possible best subset
8   Make a prediction using regression model

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Overview of Regression Analysis, Regression Applications, Collecting the Data for Regression
2 Introduction to Simple Linear Regression Analysis, Least Square Method
3 Prediction of Variance
4 Correlation Coefficient, Coefficient of Determination
5 Using the Model for Prediction
6 Polynomial regression model and parameter estimation
7 Introduction to Multiple Linear Regression Analysis, Assumptions of the Model, Fit of the Model: Least Square Method
8 Midterm Exam
9 Prediction of Variance, Inference for Beta parameters
10 Coefficient of Multiple Determination, Using the Model for Prediction
11 Methods for Variable Selection: Stepwise Regression
12 Methods for Variable Selection: Forward Selection- Backward Elimination
13 All Possible Best Subset
14 Building the Model, First Order Models, Second Order Models

Recomended or Required Reading

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

Supplementary Book(s):
Gamgam, H., & Altunkaynak, B. (2015). Regresyon Analizi. En Küçük Kareler-Değişen Seçme-Regresyon Tanıları, Seçkin Yayıncılık, Istanbul.

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

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2013). Doğrusal regresyon analizine giriş. Nobel Akademik Yayıncılık.

Planned Learning Activities and Teaching Methods

Lecture, class discussion, homeworks

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 * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.30 + RST * 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of midterm exam, final exam and quiz

Language of Instruction

Turkish

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. You can find the undergraduate policy at http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

Dokuz Eylul University, Faculty of Sciences, Department of Statistics, Kaynaklar Campus, 35390, Buca-Izmir
Assoc. Prof. Neslihan DEMIREL
e-mail: neslihan.ortabas@deu.edu.tr
Tel: 0232 301 85 73

Office Hours

Send an e-mail for meeting request.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 4 56
Preparations before/after weekly lectures 14 1 14
Preparation for final exam 1 30 30
Preparation for midterm exam 1 20 20
Preparing assignments 1 25 25
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 149

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.1535324
LO.2535324
LO.3535324
LO.4535324
LO.5535324
LO.6535324
LO.7535324
LO.8535324

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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