ISB541 Regression Theory - I

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

Information

Code ISB541
Name Regression Theory - I
Term 2022-2023 Academic Year
Term Spring
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
1


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

Update Time: 18.11.2022 06:11