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Information
| Code | ISB541 |
| Name | Regression Theory - I |
| Term | 2024-2025 Academic Year |
| Term | Fall |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MAHMUDE REVAN ÖZKALE |
| Course Instructor |
Prof. Dr. MAHMUDE REVAN ÖZKALE
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
To enable students with the ability to do models for multiple regression models and perform the adequacy analysis
Course Content
Multiple linear regression, model adequacy checking, correcting model inadequacies, diagnostics for leverages and influence, polynomial regression models
Course Precondition
none
Resources
Montgomery, D. C., Peck, E. A., Vining, G. G. (2001), Introduction to Linear Regression Analysis, 3rd edition, John Wiely and Sons Inc.
Notes
lecture notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Learn to fit muliple regression model |
| LO02 | Check model adequacy |
| LO03 | Corrects model adequacy and apply transformations |
| LO04 | Identifies leverage and influential observations |
| LO05 | Knows poynomial regression |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Have in-depth theoretical and practical knowledge about Probability and Statistics | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | They have the knowledge to make doctoral plans in the field of statistics. | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Has comprehensive knowledge about analysis and modeling methods used in statistics. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Has comprehensive knowledge of methods used in statistics. | 5 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Make scientific research on Mathematics, Probability and Statistics. | 3 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Indicates statistical problems, develops methods to solve. | 4 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Apply innovative methods to analyze statistical problems. | 3 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Designs and applies the problems faced in the field of analytical modeling and experimental researches. | 3 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Access to information and do research about the source. | 4 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Develops solution approaches in complex situations and takes responsibility. | 5 |
| PLO11 | Bilgi - Kuramsal, Olgusal | Has the confidence to take responsibility. | 2 |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | They demonstrate being aware of the new and developing practices. | 5 |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | He/She constantly renews himself/herself in statistics and related fields. | 4 |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Communicate in Turkish and English verbally and in writing. | |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Transmits the processes and results of their studies clearly in written and oral form in national and international environments. | 4 |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | It considers the social, scientific and ethical values in the collection, processing, use, interpretation and announcement stages of data and in all professional activities. | 3 |
| PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Uses the hardware and software required for statistical applications. | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Multiple regression models, least squares estimates of regression coefficients and properties | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 2 | Estimation of the variance of the error, maximum likelihood estiamtion, coefficient of determination, testing the significance of regression | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 3 | Hypothesis testing on the individual regression coefficients, test of general linear hypothesis | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Confidence interval in multiple regression, prediciton of new observations | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 5 | Extrapolation, standardization of regression coefficients | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 6 | Model adequacy checking, residual analysis | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Methods for scaling the residuals, residual graphics | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Lack of fit analysis of regression model | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Transformations and weighteing to correct model inadequacies | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Analitical methods to identify the transformations | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Generalized and weighted least squares | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Detection for influential nad leverage observations | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Polynomial models in one variable | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 15 | Polynomial models in to or more variables | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Data Spliting | Reading the related references | Ölçme Yöntemleri: Performans Değerlendirmesi |
| 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 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||