ISB414 Logistic Regression Analysis

5 ECTS - 3-0 Duration (T+A)- 8. Semester- 3 National Credit

Information

Code ISB414
Name Logistic Regression Analysis
Term 2024-2025 Academic Year
Semester 8. Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 5 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. MAHMUDE REVAN ÖZKALE
Course Instructor
1


Course Goal / Objective

The aim of this course is to construct the necessary theoretical and applied background in undergraduate teaching, to analyze the binary response data that can be faced at the public and private sectors, to gain the knowledge, skills, and practicality for interpreting the results of the analysis.

Course Content

This course covers fitting a binary logistik regression model and interpret the results.

Course Precondition

none

Resources

Hosmer, D. W., Lemeshow, S. (2000), Applied Logistic Regression, 2nd edition, John Wiely and Sons Inc.

Notes

R.H. Myers, D.C. Montgomery, G.G. Vining, T. J. Robinson (2010) Generalized Linear Models with Applications in Engineerig and the Sciences. 2nd edition. John Wiely and Sons Inc.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Create the binary logistic regression model
LO02 Estimate the model parameters
LO03 Apply confidence intervals and hypothesis tests about the parameters
LO04 Perform binary logistic regression analysis by using the statistical package program
LO05 Interprets the classification tables
LO06 Indentifies the extreme observations
LO07 Interprets the diagostic statistics
LO08 Interprets the receiver operating curves


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Explain the essence fundamentals and concepts in the field of Statistics
PLO02 Bilgi - Kuramsal, Olgusal Emphasize the importance of Statistics in life 4
PLO03 Bilgi - Kuramsal, Olgusal Define basic principles and concepts in the field of Law and Economics
PLO04 Bilgi - Kuramsal, Olgusal Produce numeric and statistical solutions in order to overcome the problems
PLO05 Bilgi - Kuramsal, Olgusal Use proper methods and techniques to gather and/or to arrange the data 4
PLO06 Bilgi - Kuramsal, Olgusal Utilize computer programs and builds models, solves problems, does analyses and comments about problems concerning randomization
PLO07 Bilgi - Kuramsal, Olgusal Apply the statistical analyze methods 4
PLO08 Bilgi - Kuramsal, Olgusal Make statistical inference (estimation, hypothesis tests etc.)
PLO09 Bilgi - Kuramsal, Olgusal Generate solutions for the problems in other disciplines by using statistical techniques and gain insight
PLO10 Bilgi - Kuramsal, Olgusal Discover the visual, database and web programming techniques and posses the ability of writing programs
PLO11 Beceriler - Bilişsel, Uygulamalı Distinguish the difference between the statistical methods
PLO12 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Make oral and visual presentation for the results of statistical methods 3
PLO13 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Have capability on effective and productive work in a group and individually 2
PLO14 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs
PLO15 Yetkinlikler - Öğrenme Yetkinliği Develop scientific and ethical values in the fields of statistics-and scientific data collection


Week Plan

Week Topic Preparation Methods
1 Binary logistic regression Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım
2 Logit, odds ratio, relative risk Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım
3 Multiple logistic regression and fiting a model, mariginal effect Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
4 Maximum likelihood and Newton-Raphson method Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
5 Confidence intervals Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
6 Goodness of fit Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
7 Lack of fit tests Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
8 Mid-Term Exam Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav
9 Classification tables Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
10 Regression diagnostic and outliers Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
11 ROC curve Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
12 Sensitivity, specificity and related topics Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
13 Modeling strategies Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
14 Modeling strategies for assessing interaction and confounding Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
15 Application of logisitc regression with different sampling models Source reading and use of statistical package program Öğretim Yöntemleri:
Anlatım, Problem Çözme
16 Application on outlier detection in logistic regression Review the topics discussed in the lecture notes and sources Öğretim Yöntemleri:
Alıştırma ve Uygulama
17 Term Exams Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 3 42
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 12 12
Final Exam 1 18 18
Total Workload (Hour) 114
Total Workload / 25 (h) 4,56
ECTS 5 ECTS

Update Time: 12.06.2024 12:08