ISB321 Regression Analysis

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

Information

Code ISB321
Name Regression Analysis
Term 2024-2025 Academic Year
Semester 5. 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 Prof. Dr. MAHMUDE REVAN ÖZKALE (A Group) (Ins. in Charge)


Course Goal / Objective

The aim of this course is to construct the necessary theoretical background in undergraduate teaching, to analyze the 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 parameter estimation and hypothesis testing in simple linear regression model. To detect outliers and influential observations.

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

Aydın, D. (2014), Uygulamalı Regresyon Analizi Kavramlar ve Hesaplamaları, Nobel Akademik Yayıncılık, Ankara


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Create the regression model
LO02 Estimate the model parameters
LO03 Apply confidence intervals and hypothesis tests about the parameters
LO04 Learn how to use the ANOVA table
LO05 Obtain the most appropriate model by examining the data
LO06 Check model assumptions
LO07 Create ANOVA table in multiple regression
LO08 Perform regression analysis by using the statistical package program


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 2
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 1
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 Conditional expected value, the concept of regression and model building Source reading Öğretim Yöntemleri:
Anlatım
2 The creation of a simple linear regression model, the least squares estimators for the parameters, centered model Source reading Öğretim Yöntemleri:
Anlatım
3 Properties of least squares estimators of parameters Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
4 Estimation error variance and examination of the properties of the fitted regression model Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
5 Maximum likelihood estimation of error variance and regression parameters Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
6 Tests of hypotheses about the parameters, test for significance of regression Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
7 Preparation and explanation of how to use the ANOVA table, examination of the coefficient of determination Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
8 Mid-Term Exam Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav
9 Interval estimation of parameters, the interval estimation of the mean response, prediction of new observations Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
10 Regression through the origin, examination of the assumptions of the model (residual analysis), investigation of heteroskedasticity, normal probability graphics Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
11 Introduction to outliers and influential observations and examination of their effects on the the least squares estimators Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
12 Fitting multiple regression model, matrix notation and estimation of the regression parameters Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
13 Examining the distributional properties of least squares estimators of regression parameters, and the error variance Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
14 The creation of multiple regression ANOVA table and tests of hypotheses about the parameters of the regression Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
15 Application on the creation of multiple regression ANOVA table and tests of hypotheses about the parameters of the regression Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
16 Determination of the influential observations in multiple regression Source reading Ölçme Yöntemleri:
Yazılı Sınav
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 1 6 6
Mid-term Exams (Written, Oral, etc.) 1 12 12
Final Exam 1 18 18
Total Workload (Hour) 120
Total Workload / 25 (h) 4,80
ECTS 5 ECTS

Update Time: 12.06.2024 11:48