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

PROFESSOR DOCTOR NESLIHAN DEMIREL

Offered to

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

Course Objective

This course will cover alternative methods such as Weighted Least Squares Regression, Ridge Regression and Lasso Regression methods when the assumptions of the multiple linear regression model are not met and this course also include logistic regression model and many popular regression and classification methods such as regression and classification trees and random forest. Students will learn how to evaluate the performance of these methods. They will use R software to implement the methods.

Learning Outcomes of the Course Unit

1   Fit a regression model for binary and continuous response and make predictions
2   Fit an alternative regression models and make predictions
3   Analyzing data using tree based methods
4   Perform diagnostics for a model
5   Evaluate performance of a model
6   Use R to build, visualize and test performance of a model

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction, Simple Linear Regression
2 Multiple Linear Regression
3 Assumptions of Multiple Linear Regression
4 Transformation
5 Weighted Least Squares Regression
6 Ridge Regression
7 Lasso Regression
8 Regression Trees
9 Random Forest Regression
10 Logistic Regression-Estimation, Diagnostics
11 Logistic regression-Model selection
12 Classification Trees
13 Random Forest Classification
14 Project Presentations

Recomended or Required Reading

Draper, N. R., & Smith, H. (1998). Applied regression analysis (Vol. 326). John Wiley & Sons.
Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models (Vol. 5). New York: McGraw-Hill Irwin.
Mendenhall, W., Sincich, T., & Boudreau, N. S. (1996). A second course in statistics: regression analysis (Vol. 5). Upper Saddle River, NJ: Prentice Hall.
James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer, New York.

Planned Learning Activities and Teaching Methods

Lecture, class discussion, homeworks.

Assessment Methods

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

Assignments, Midterm and Final exams

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 Faculty of Science, Department of Statistics
e-mail: neslihan.ortabas@deu.edu.tr
Tel: 0232 301 86 00

Office Hours

Send an e-mail: neslihan.ortabas@deu.edu.tr 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 3 42
Preparation for midterm exam 1 30 30
Preparation for final exam 1 45 45
Preparing assignments 2 15 30
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 193

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1555
LO.2555
LO.3555
LO.4555
LO.5555
LO.6

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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