| Course Description |
|
| Course Name |
: |
Multivariate Analysis With SPSS Applications |
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| Course Code |
: |
IEM 730 |
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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 |
: |
Spring (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 |
: |
Forms the data matrix of the multivariate data in a software package .
Discriminates the proper statistical test for the data.
Analyzes and interprets data sets suitable for dimension reduction.
Analyzes and interprets data sets suitable for classification.
Designs an original article following scientific research rules.
Reports and interprets the results of application.
|
|
| 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 analyze and interpret the proper method with a software package after the necessery steps are checked. |
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| Course Contents |
: |
The course covers discriminating multivariate methods as dimension reduction and classification techniques, and then applying and interpreting selected method to the data set with a software package . |
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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 |
Developing the data matrix in package program |
Reading the related chapter from the reference book |
Lecture and Package program |
|
2 |
Preparing the data before analysing |
Reading the related chapter from the reference book |
Lecture and using software package |
|
3 |
Preparing the data with multivariate charts and standardized procedures |
Reading the related chapter from the reference book |
Lecture and using software package |
|
4 |
Realization of the assumptions and steps of logistic regression analysis |
Reading the related chapter from the reference book |
Lecture and using software package |
|
5 |
Analysing and discussing the results of logistic regression on hypothetic and real data sets |
Reading the related chapter from the reference book |
Lecture and using software package |
|
6 |
Discriminant analysis: assumptions, steps of procedure and interpreting findings |
Reading the related chapter from the reference book |
Lecture and using software package |
|
7 |
Analysing and discussing the results of discriminant analysis on hypothetic and real data sets |
Reading the related chapter from the reference book |
Lecture and using software package |
|
8 |
Midterm exam |
|
|
|
9 |
Cluster analysis: assumptions, steps of procedure and interpreting findings |
Reading the related chapter from the reference book |
Lecture and using software package |
|
10 |
Analysing and discussing the results of cluster analysis on hypothetic and real data sets |
Reading the related chapter from the reference book |
Lecture and using software package |
|
11 |
Principal component analysis: assumptions, steps of procedure and interpreting findings |
Reading the related chapter from the reference book |
Lecture and using software package |
|
12 |
Analysing and discussing the results of principal component analysis on hypothetic and real data sets |
Reading the related chapter from the reference book |
Lecture and using software package |
|
13 |
Factor analysis: assumptions, steps of procedure and interpreting findings |
Reading the related chapter from the reference book |
Lecture and using software package |
|
14 |
Analysing and discussing the results of factor analysis on hypothetic and real data sets |
Reading the related chapter from the reference book |
Lecture and using software package |
|
15 |
Applications on a package program for all statistical methods being considered |
Reading the related chapter from the reference book |
Lecture and using software package |
|
16/17 |
Final exam |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Explains Econometric concepts |
4 |
|
2 |
Equipped with the foundations of Economics, develops Economic models |
3 |
|
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 |
5 |
|
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. |
3 |
|
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 |
4 |
|
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 |
3 |
|
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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