| Course Description |
|
| Course Name |
: |
Multivariate Statistical Analysis Techniques |
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| Course Code |
: |
IEM 740 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Fall (16 Weeks) |
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| ECTS |
: |
6 |
|
| Name of Lecturer(s) |
: |
Asst.Prof.Dr. EBRU ÖZGÜR GÜLER
|
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| Learning Outcomes of the Course |
: |
Explains multivariate data.
Knows dimension reduction techniques and explains their assumptions.
Knows classification methods and explains their assumptions.
Discriminates the most proper analysis technique for the data considered.
Uses a software package to do analysis for each technique and interprets obtained results.
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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 |
: |
The aim of this course is to enable the students to build the data matrix for multivariate analysis, to choose the most suitable method for the data, and to apply of the proper technique once the assumptions are verified. |
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| Course Contents |
: |
The course content covers the building of multivariate data which includes understanding, preparing and transforming the data, comparing the methods related to dimension reduction and classification, assumptions and applications of multivariate techniques. |
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| Language of Instruction |
: |
Turkish |
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| Work Place |
: |
Faculty classrooms |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Motivation: A Review of references and introductory matrix algebra |
|
Lecture |
|
2 |
Building data matrix for multivariate analysis and the descriptive statistics |
Reading the related chapter in the reference book |
Lecture |
|
3 |
Multivariate graphics, standardization and multivariate normal distribution |
Reading the related chapter in the reference book |
Lecture |
|
4 |
Examination of outliers and missing data in multivariate analysis, distance and similiarity measures |
Reading the related chapter in the reference book |
Lecture |
|
5 |
Multivariate hypothesis tests |
Reading the related chapter in the reference book |
Lecture |
|
6 |
Dimension Reduction: Factor analysis and its assumptions |
Reading the related chapter in the reference book |
Lecture and using statistical software packages |
|
7 |
Dimension Reduction (cont): Factor analysis and its assumptions |
Reading the related chapter in the reference book |
Lecture and using statistical software packages |
|
8 |
Midterm exam |
|
|
|
9 |
Classification: Clustering analysis and its assumptions |
Reading the related chapter in the reference book |
Lecture and using statistical software packages |
|
10 |
Classification (cont): Clustering analysis and its assumptions |
Reading the related chapter in the reference book |
Lecture and using statistical software packages |
|
11 |
Classification: Assumptions of discriminant analysis |
Reading the related chapter in the reference book |
Lecture and using statistical software packages |
|
12 |
Classification (cont): Applications of discriminant analysis |
Reading the related chapter in the reference book |
Lecture and using statistical software packages |
|
13 |
Classification: Assumptions of logistic regression |
Reading the related chapter in the reference book |
Lecture and using statistical software packages |
|
14 |
Classification (cont): Applications of logistic regression |
Reading the related chapter in the reference book |
Lecture and using statistical software packages |
|
15 |
General review and computer applications |
Reading the related chapter in the reference book |
Lecture and using statistical software packages |
|
16/17 |
Final exam |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Explains Econometric concepts |
1 |
|
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 |
5 |
|
5 |
Collects, edits, and analyzes data |
4 |
|
6 |
Uses advanced software packages concerning Econometrics, Statistics, and Operation Research |
3 |
|
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 |
3 |
|
8 |
Speaks Turkish and at least one other foreign language in accordance with the requirements of academic and business life. |
3 |
|
9 |
Questions traditional approaches and their implementation and develops alternative study programs when required |
2 |
|
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 |
3 |
|
13 |
Defines the concepts of statistics, operations research and mathematics. |
4 |
|
14 |
Knowing the necessity of life-long learning, follows the latest developments in the field of study and improves himself continiously |
1 |
|
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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