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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| STATISTICS PR. | |
| Code | ISB414 |
| Name | Logistic Regression Analysis |
| Term | 2017-2018 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 |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MAHMUDE REVAN ÖZKALE ATICIOĞLU |
| Course Instructor |
Prof. Dr. MAHMUDE REVAN ÖZKALE ATICIOĞLU
(Bahar)
(A Group)
(Ins. in Charge)
|
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
Yok
Resources
Notes
Hosmer, D. W., Lemeshow, S. (2000), Applied Logistic Regression, 2nd edition, John Wiely & 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 | |
| 2 | Logit, odds ratio, relative risk | Source reading and use of statistical package program | |
| 3 | Multiple logistic regression and fiting a model, mariginal effect | Source reading and use of statistical package program | |
| 4 | Maximum likelihood and Newton-Raphson method | Source reading and use of statistical package program | |
| 5 | Confidence intervals | Source reading and use of statistical package program | |
| 6 | Goodness of fit | Source reading and use of statistical package program | |
| 7 | Lack of fit tests | Source reading and use of statistical package program | |
| 8 | Midterm exam | Review the topics discussed in the lecture notes and sources | |
| 9 | Classification tables | Source reading and use of statistical package program | |
| 10 | Regression diagnostic and outliers | Source reading and use of statistical package program | |
| 11 | ROC curve | Source reading and use of statistical package program | |
| 12 | Sensitivity, specificity and related topics | Source reading and use of statistical package program | |
| 13 | Modeling strategies | Source reading and use of statistical package program | |
| 14 | Modeling strategies for assessing interaction and confounding | Source reading and use of statistical package program | |
| 15 | Application of logisitc regression with different sampling models | Source reading and use of statistical package program | |
| 16 | Final exam | Review the topics discussed in the lecture notes and sources | |
| 17 | Final exam | Review the topics discussed in the lecture notes and sources |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
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 | ||