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| Course Description |
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| Course Name |
: |
Regression Analysis |
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| Course Code |
: |
İSB321 |
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| Course Type |
: |
Compulsory |
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| Level of Course |
: |
First Cycle |
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| Year of Study |
: |
3 |
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| Course Semester |
: |
Fall (16 Weeks) |
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| ECTS |
: |
5 |
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| Name of Lecturer(s) |
: |
Assoc.Prof.Dr. MAHMUDE REVAN ÖZKALE
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| Learning Outcomes of the Course |
: |
Understand the creation of the regression model
Learn to estimate the model parameters
Apply confidence intervals and hypothesis tests about the parameters
Prepare and learn how to use the ANOVA table
Obtain the most appropriate model by examining the data
Check model assumptions
Create ANOVA table in multiple regression
Perform regression analysis by using the statistical package program
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| Mode of Delivery |
: |
Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
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. |
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| Course Contents |
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Parameter estimation and hypothesis testing in simple linear regression model. To detect outliers and influential observations. |
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| Language of Instruction |
: |
Turkish |
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| Work Place |
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Faculty of Arts and Sciences Annex Classrooms |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Conditional expected value, the concept of regression and model building |
Source reading |
Lecture |
|
2 |
The creation of a simple linear regression model, the least squares estimators for the parameters, centered model |
Source reading |
Lecture |
|
3 |
Properties of least squares estimators of parameters |
Source reading |
Lecture, problem-solving, using statistical package program |
|
4 |
Estimation error variance and examination of the properties of the fitted regression model |
Source reading |
Lecture, problem-solving, using statistical package program |
|
5 |
Maximum likelihood estimation of error variance and regression parameters |
Source reading |
Lecture, problem-solving, using statistical package program |
|
6 |
Tests of hypotheses about the parameters, test for significance of regression |
Source reading |
Lecture, problem-solving, using statistical package program |
|
7 |
Preparation and explanation of how to use the ANOVA table, examination of the coefficient of determination |
Source reading |
Lecture, problem-solving, using statistical package program |
|
8 |
Midterm exam |
Review the topics discussed in the lecture notes and sources |
Written exam |
|
9 |
Interval estimation of parameters, the interval estimation of the mean response, prediction of new observations |
Source reading |
Lecture, problem-solving, using statistical package program |
|
10 |
Regression through the origin, examination of the assumptions of the model (residual analysis), investigation of heteroskedasticity, normal probability graphics |
Source reading |
Lecture, problem-solving, using statistical package program |
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11 |
Introduction to outliers and influential observations and examination of their effects on the the least squares estimators |
Source reading |
Lecture, problem-solving, using statistical package program |
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12 |
Fitting multiple regression model, matrix notation and estimation of the regression parameters |
Source reading |
Lecture, problem-solving, using statistical package program |
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13 |
Examining the distributional properties of least squares estimators of regression parameters, and the error variance |
Source reading |
Lecture, problem-solving, using statistical package program |
|
14 |
The creation of multiple regression ANOVA table and tests of hypotheses about the parameters of the regression |
Source reading |
Lecture, problem-solving, using statistical package program |
|
15 |
Determination of the influential observations in multiple regression |
Source reading |
Lecture, problem-solving, using statistical package program |
|
16/17 |
Final exam |
Review the topics discussed in the lecture notes and sources |
Written exam |
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|
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Montgomery, D. C., Peck, E. A., Vining, G. G. (2001), Introduction to Linear Regression Analysis, 3rd edition, John Wiely & Sons Inc.
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| |
| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
1 |
80 |
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Homeworks/Projects/Others |
4 |
20 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
|
100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Utilize computer systems and softwares |
4 |
|
2 |
Apply the statistical analyze methods |
5 |
|
3 |
Make statistical inference(estimation, hypothesis tests etc.) |
5 |
|
4 |
Generate solutions for the problems in other disciplines by using statistical techniques |
4 |
|
5 |
Discover the visual, database and web programming techniques and posses the ability of writing programme |
0 |
|
6 |
Construct a model and analyze it by using statistical packages |
5 |
|
7 |
Distinguish the difference between the statistical methods |
5 |
|
8 |
Be aware of the interaction between the disciplines related to statistics |
2 |
|
9 |
Make oral and visual presentation for the results of statistical methods |
2 |
|
10 |
Have capability on effective and productive work in a group and individually |
0 |
|
11 |
Develop scientific and ethical values in the fields of statistics-and scientific data collection |
5 |
|
12 |
Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics |
5 |
|
13 |
Emphasize the importance of Statistics in life |
5 |
|
14 |
Define basic principles and concepts in the field of Law and Economics |
0 |
|
15 |
Produce numeric and statistical solutions in order to overcome the problems |
5 |
|
16 |
Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events |
5 |
|
17 |
Use proper methods and techniques to gather and/or to arrange the data |
5 |
|
18 |
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 |
0 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
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Homeworks, Projects, Others |
4 |
8 |
32 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
5 |
5 |
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Final Exam |
1 |
10 |
10 |
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Total Workload: | 131 |
| Total Workload / 25 (h): | 5.24 |
| ECTS Credit: | 5 |
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