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| Course Description |
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| Course Name |
: |
Multivariate Statistical Analysis |
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| Course Code |
: |
İSB424 |
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| Course Type |
: |
Compulsory |
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| Level of Course |
: |
First Cycle |
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| Year of Study |
: |
4 |
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| Course Semester |
: |
Spring (16 Weeks) |
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| ECTS |
: |
5 |
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| Name of Lecturer(s) |
: |
Asst.Prof.Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY
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| Learning Outcomes of the Course |
: |
Determine the structure of the data consisting of a very large number of variables and converting it into a form as simple as possible, decide which analysis would be appropriate to use, comment about the data and reach the right decision
Learn and make the principal component analysis
Learn and make the factor analysis
Understand the relationship of the principal component analysis to factor analysis
Know and use measures of similarity and dissimilarity using cluster analysis
Learn the concept of correlation and why we use the canonical correlation analysis and obtain the canonical correlation
Make the discriminant analysis in case of two or more than two groups
Learn and use the multidimensional scaling procedures
Perform the principal component analysis, factor analysis, cluster analysis, canonical correlation analysis and multidimensional scaling by using statistical package programs (SPSS and Minitab)
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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 |
: |
Determine the structure of the data consisting of a very large number of variables and converting it into a form as simple as possible, decide which analysis would be appropriate to use, comment about the data and reach the right decision |
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| Course Contents |
: |
Principal component analysis, factor analysis, canonical correlation analysis, discriminant analysis, cluster analysis, multidimensional scaling |
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| Language of Instruction |
: |
Turkish |
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| Work Place |
: |
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 |
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1 |
Principal component analysis, requirement of the principal component analysis, obtaining the principal component analysis |
Source reading |
Lecture, discussion, problem-solving |
|
2 |
Properties of the principal components, determine the number of principal components, examples |
Source reading |
Lecture, discussion, problem-solving |
|
3 |
Factor analysis, purpose of factor analysis, the relationship of factor analysis to principle component analysis |
Source reading |
Lecture, discussion, problem-solving |
|
4 |
Principle factor method, factor rotation |
Source reading |
Lecture, discussion, problem-solving, using statistical package program |
|
5 |
Principal component analysis and factor analysis by using SPSS and Minitab package programs |
Source reading |
Lecture, discussion, problem-solving, using statistical package program |
|
6 |
Canonical correlation analysis, purpose of canonical correlation analysis, obtaining the canonical correlations |
Source reading |
Lecture, discussion, problem-solving |
|
7 |
Test of significance for canonical correlations, examples, canonical correlation analysis by using SPSS and Minitab package programs |
Source reading |
Lecture, discussion, problem-solving, using statistical package program |
|
8 |
Mid-Term Exam |
Review the topics discussed in the lecture notes and sources |
Written exam |
|
9 |
Discriminant analysis, discriminant analysis for two groups |
Source reading |
Lecture, discussion, problem-solving |
|
10 |
Discriminant analysis for more than two groups, examples, discriminant analysis by using SPSS and Minitab package programs |
Source reading |
Lecture, discussion, problem-solving, using statistical package program |
|
11 |
Measures of similarity and dissimilarity |
Source reading |
Lecture, discussion, problem-solving |
|
12 |
Cluster analyis, clustering methods, examples |
Source reading |
Lecture, discussion, problem-solving |
|
13 |
Cluster analyis by using SPSS and Minitab package programs |
Source reading |
Discussion, problem-solving, using statistical package program |
|
14 |
Multidimensional scaling prodecures |
Source reading |
Lecture, discussion, problem-solving, |
|
15 |
Comparing the multidimensional scaling prodecures with each other, comparing the multidimensional scaling prodecures to the principal component analysis, examples |
Source reading |
Lecture, discussion, problem-solving |
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16/17 |
Final Exam |
Review the topics discussed in the lecture notes and sources |
Written Exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Tatlıdil, Hüseyin (2002). Uygulamalı Çok Değişkenli İstatistiksel Analiz, Ankara
Rencher, A.C. (2002). Methods of Multivariate Analysis (2nd Edition), John Wiley & Sons, Inc., Publication
Kalaycı, Şeref (2010). SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri, Asil Yayın Dağıtım, Ankara
Srivastava, M.S. (2002) Methods of Multivariate Statistics, John Wiley & Sons, Inc., Publication
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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 |
60 |
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Homeworks/Projects/Others |
5 |
40 |
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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 |
5 |
|
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 |
4 |
|
9 |
Make oral and visual presentation for the results of statistical methods |
4 |
|
10 |
Have capability on effective and productive work in a group and individually |
1 |
|
11 |
Develop scientific and ethical values in the fields of statistics-and scientific data collection |
1 |
|
12 |
Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics |
2 |
|
13 |
Emphasize the importance of Statistics in life |
2 |
|
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 |
3 |
|
16 |
Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events |
2 |
|
17 |
Use proper methods and techniques to gather and/or to arrange the data |
2 |
|
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 |
|
Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
5 |
5 |
25 |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
15 |
15 |
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Total Workload: | 134 |
| Total Workload / 25 (h): | 5.36 |
| ECTS Credit: | 5 |
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