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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| STATISTICS PR. | |
| Code | ISB321 |
| Name | Regression Analysis |
| Term | 2017-2018 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 |
| Label | C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MAHMUDE REVAN ÖZKALE ATICIOĞLU |
| Course Instructor |
Prof. Dr. MAHMUDE REVAN ÖZKALE ATICIOĞLU
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
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
Parameter estimation and hypothesis testing in simple linear regression model. To detect outliers and influential observations.
Course Precondition
Resources
Notes
Montgomery, D. C., Peck, E. A., Vining, G. G. (2001), Introduction to Linear Regression Analysis, 3rd edition, John Wiely & Sons Inc.
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 | |
| 2 | The creation of a simple linear regression model, the least squares estimators for the parameters, centered model | Source reading | |
| 3 | Properties of least squares estimators of parameters | Source reading | |
| 4 | Estimation error variance and examination of the properties of the fitted regression model | Source reading | |
| 5 | Maximum likelihood estimation of error variance and regression parameters | Source reading | |
| 6 | Tests of hypotheses about the parameters, test for significance of regression | Source reading | |
| 7 | Preparation and explanation of how to use the ANOVA table, examination of the coefficient of determination | Source reading | |
| 8 | Midterm exam | Review the topics discussed in the lecture notes and sources | |
| 9 | Interval estimation of parameters, the interval estimation of the mean response, prediction of new observations | Source reading | |
| 10 | Regression through the origin, examination of the assumptions of the model (residual analysis), investigation of heteroskedasticity, normal probability graphics | Source reading | |
| 11 | Introduction to outliers and influential observations and examination of their effects on the the least squares estimators | Source reading | |
| 12 | Fitting multiple regression model, matrix notation and estimation of the regression parameters | Source reading | |
| 13 | Examining the distributional properties of least squares estimators of regression parameters, and the error variance | Source reading | |
| 14 | The creation of multiple regression ANOVA table and tests of hypotheses about the parameters of the regression | Source reading | |
| 15 | Determination of the influential observations in multiple regression | Source reading | |
| 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 | 80 | 32 |
| 1. Homework | 10 | 4 |
| 2. Homework | 10 | 4 |
| 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 | 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 | ||