Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

Description of Individual Course Units

Course Unit CodeCourse Unit TitleType of CourseYearSemesterECTS
İST461 Logistic Regression Analysis 927006 4 7 4
Level of Course Unit
First Cycle
Objectives of the Course
In statistical applications, a number of regression methods have been developed in order to establish a relationship between the independent variable and the resultant variable and to generalize it. Logistic regression is one of these methods. It is aimed to determine the relation between two or more variables with cause-and-effect relation between them and to make predictions or estimations about the subject by using logistic regression analysis when the result variable has two or more values which are intermittent.
Name of Lecturer(s)
Doç. Dr. Taner TUNÇ
Learning Outcomes
  1. In every discipline, risk calculations can be made for real events. Possible risks can be determined in advance and significant economic contributions can be achieved
  2. can make accurate disease diagnosis or correct case modelling
Mode of Delivery
Formal Education
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Recommended or Required Reading
1-Logistik Regression, David G. Kleinbaum-Mitchel Klein, Springer2-Paket Programlar İle İstatistiksel Veri Analizi, Prof.Dr. Kazım Özdamar, Kaan Kitabevi
Planned Learning Activities and Teaching Methods
Language of Instruction
Turkish
Work Placement(s)
None
Course Contents
Weekly Detailed Course Contents
Week Theoretical Practice Laboratory
1.Definition of logistic regression and its application areas
2.matrix algebra
3.parameter estimation methods.
4.estimation of logistic regression parameters
5.Significance of the logistic regression model and testing of coefficients. ODDS ratio
6.Significance of the logistic regression model and testing of coefficients. ODDS ratio
7.Significance of the logistic regression model and testing of coefficients. ODDS ratio
8.midterm examination
9.statistical package usage and Interpret package program output
10.statistical package usage and Interpret package program output
11.statistical package usage and Interpret package program output
12.Practice on real life data sets
13.Practice on real life data sets
14.Practice on real life data sets
15.
16.
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight (%)
Midterm Examination1100
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight (%)
Final Examination1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Workload Calculation
ActivitiesQuantityTime(hours)Total Workload(hours)
Midterm Examination122
Final Examination144
Attending Lectures13226
Practice4624
Problem Solving5210
Project Preparation5420
Homework3412
SUM98