TÜRKÇE
ENGLISH
Information
| Code | ISB414 |
| Name | Logistic Regression Analysis |
| Term | 2023-2024 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 2 |
Course Goal / Objective
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
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
lecture notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Understand the creation of binary logistic regression model |
| LO02 | Learn to 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 Probability, Statistics and Mathematics | 4 |
| 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 | 5 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Use proper methods and techniques to gather and/or to arrange the data | 5 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Utilize computer systems and softwares | 4 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Apply the statistical analyze methods | 5 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Make statistical inference(estimation, hypothesis tests etc.) | 5 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Generate solutions for the problems in other disciplines by using statistical techniques | 4 |
| PLO11 | Bilgi - Kuramsal, Olgusal | Discover the visual, database and web programming techniques and posses the ability of writing programme | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Construct a model and analyze it by using statistical packages | 4 |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Distinguish the difference between the statistical methods | 5 |
| PLO14 | Beceriler - Bilişsel, Uygulamalı | Be aware of the interaction between the disciplines related to statistics | 3 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Make oral and visual presentation for the results of statistical methods | 3 |
| PLO16 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have capability on effective and productive work in a group and individually | 2 |
| PLO17 | 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 | |
| PLO18 | Yetkinlikler - Öğrenme Yetkinliği | Develop scientific and ethical values in the fields of statistics-and scientific data collection | 4 |
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 | ||