| Course Description |
|
| Course Name |
: |
Regression theory |
|
| Course Code |
: |
IEM 729 |
|
| Course Type |
: |
Optional |
|
| Level of Course |
: |
Second Cycle |
|
| Year of Study |
: |
1 |
|
| Course Semester |
: |
Fall (16 Weeks) |
|
| ECTS |
: |
6 |
|
| Name of Lecturer(s) |
: |
Asst.Prof.Dr. GÜLSEN KIRAL
|
|
| Learning Outcomes of the Course |
: |
Investigate the relationships between variables
estimate the model based on the relationship between variables
From the established models, make analyzes and estimate.
|
|
| Mode of Delivery |
: |
Face-to-Face |
|
| Prerequisites and Co-Prerequisites |
: |
None |
|
| Recommended Optional Programme Components |
: |
None |
|
| Aim(s) of Course |
: |
The aim of the course is to create the necessary theoretical basis during the period of education, to train the students to be able to analyse the data that can be faced in the public and private sector and interpret the reults obtained. |
|
| Course Contents |
: |
The course covers the relationship between variables, correlation analysis, simple linear regression, multiple regression, regression models, the validity and reliability of curvilinear regression, linear regression model assumptions and assumptions deviate from the states. |
|
| Language of Instruction |
: |
Turkish |
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| Work Place |
: |
Clarooms (1. Blok)
Comp. Lab. (1. Blok) |
|
|
Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Introduction to regression analysis, regression analysis, definition and the purposes of regression analysis, data types, Regression and Correlation Analysis |
Reading the relevant sections of the source book |
Lecture and computer application |
|
2 |
Simple Linear Regression, the estimation of Regression coefficients with the OLS (Ordinary Least Squares Method) |
Reading the relevant sections of the source book |
Lecture and computer application |
|
3 |
the standard error of the regression model and coefficients, significance tests and confidence intervals, analysis of variance |
Reading the relevant sections of the source book |
Lecture and computer application |
|
4 |
The correlation coefficient, the coefficient of determination, and their significance tests |
Reading the relevant sections of the source book |
Lecture and computer application |
|
5 |
Random error term (residues-residues) assumptions about, examining the assumption of normality of the error term |
Reading the relevant sections of the source book |
Lecture and computer application |
|
6 |
To investigate the validity and reliability of the coefficients, elasticity coefficients, Multiple coefficient of determination, |
Reading the relevant sections of the source book |
Lecture and computer application |
|
7 |
Midterm exam |
|
|
|
8 |
For the validity of the regression model analysis of varianceSimple and multiple regression models of non-linear autocorrelation |
Reading the relevant sections of the source book |
Lecture and computer application |
|
9 |
Assumptions about Random error term (residues-residues) , examining the assumption of normality of the error term |
Reading the relevant sections of the source book |
Lecture and computer application |
|
10 |
Autocorrelation problem identification and solutions. Multicollinearity problem |
Reading the relevant sections of the source book |
Lecture and computer application |
|
11 |
Constant variance assumption (Homoskedasite), variable variance (Heterodskedasite) state of constant variance revealed problems and solutions |
Reading the relevant sections of the source book |
Lecture and computer application |
|
12 |
problems and solutions of linear multicollinearity, example |
Reading the relevant sections of the source book |
Lecture and computer application |
|
13 |
Multiple linear regression models, alternative methods of selection of variables to be included in the model. Dummy variable models |
Reading the relevant sections of the source book |
Lecture and computer application |
|
14 |
Minitab and SPlus applications in solving regression models. Dummy dependent variable models |
Reading the relevant sections of the source book |
Lecture and computer application |
|
15 |
Homework presentation |
|
|
|
16/17 |
Final exam |
|
|
|
|
| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Explains Econometric concepts |
2 |
|
2 |
Equipped with the foundations of Economics, develops Economic models |
1 |
|
3 |
Models problems using the knowledge of Mathematics, Statistics, and Econometrics |
4 |
|
4 |
Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems |
4 |
|
5 |
Collects, edits, and analyzes data |
4 |
|
6 |
Uses advanced software packages concerning Econometrics, Statistics, and Operation Research |
4 |
|
7 |
Develops the ability to use different resources in an area which has not been studied in the scope of academic rules, synthesizes the information gathered, and gives effective presentations |
4 |
|
8 |
Speaks Turkish and at least one other foreign language in accordance with the requirements of academic and business life. |
4 |
|
9 |
Questions traditional approaches and their implementation and develops alternative study programs when required |
3 |
|
10 |
Recognizes and implements social, scientific, and professional ethic values |
1 |
|
11 |
Gives a consistent estimate for the model and analyzes and interprets its results |
4 |
|
12 |
Takes responsibility individually and/or as a member of a team; leads a team and works effectively |
2 |
|
13 |
Defines the concepts of statistics, operations research and mathematics. |
3 |
|
14 |
Knowing the necessity of life-long learning, follows the latest developments in the field of study and improves himself continiously |
2 |
|
15 |
Follows the current issues, and interprets the data about economic and social events. |
3 |
|
16 |
Understands and interprets the feelings, thoughts and behaviours of people and expresses himself/herself orally and in written form efficiently |
2 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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