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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| STATISTICS PR. | |
| Code | ISB428 |
| Name | Multivariate Statistical Analysis 2 |
| Term | 2026-2027 Academic Year |
| Semester | 8. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The goal is 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.
Course Content
The content of this course is principal components analysis, factor analysis, canonical correlation analysis, discriminant analysis, cluster analysis, multidimensional scaling.
Course Precondition
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Resources
1. Alvin C. Rencher. Methods of Multivariate Analysis (2nd Edition) Wiley series in probability and mathematical statistics 2. Using Multivariate Statistics (6th Edition). Barbara G. Tabachnick , Linda S. Fidell. Pearson Education, Boston. 3. Uygulamalı Çok değişkenli İstatistiksel Analiz. Hüseyin Tatlıdil
Notes
UC Irvine Machine Learning Repository-https://archive.ics.uci.edu/
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Determines the structure of the data consisting of a very large number of variables. |
| LO02 | Converts the data consisting of a very large number of variables into a form as simple as possible. |
| LO03 | Makes the principal component analysis. |
| LO04 | Makes the factor analysis. |
| LO05 | Explains the purpose of the principal component analysis and factor analysis. |
| LO06 | Explains the relationship of the principal component analysis and factor analysis. |
| LO07 | Knows measures of similarity and dissimilarity using cluster analysis. |
| LO08 | Explains the concept of correlation and why we use the canonical correlation analysis with examples. |
| LO09 | Makes the discriminant analysis in case of two or more than two groups. |
| LO10 | Uses the multidimensional scaling procedures. |
| LO11 | Performs the principal component analysis, factor analysis, cluster analysis, canonical correlation analysis and multidimensional scaling by using statistical package programs (SPSS and Minitab) |
| LO12 | Evaluates which analysis is appropriate for the multivariate data. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain the essence fundamentals and concepts in the field of Statistics | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Emphasize the importance of Statistics in life | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Define basic principles and concepts in the field of Law and Economics | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Produce numeric and statistical solutions in order to overcome the problems | |
| PLO05 | Bilgi - Kuramsal, Olgusal | Use proper methods and techniques to gather and/or to arrange the data | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Utilize computer programs and builds models, solves problems, does analyses and comments about problems concerning randomization | |
| PLO07 | Bilgi - Kuramsal, Olgusal | Apply the statistical analyze methods | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Make statistical inference (estimation, hypothesis tests etc.) | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Generate solutions for the problems in other disciplines by using statistical techniques and gain insight | |
| PLO10 | Bilgi - Kuramsal, Olgusal | Discover the visual, database and web programming techniques and posses the ability of writing programs | |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Distinguish the difference between the statistical methods | |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Make oral and visual presentation for the results of statistical methods | |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have capability on effective and productive work in a group and individually | |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | 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 | |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | Develop scientific and ethical values in the fields of statistics-and scientific data collection |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Principal component analysis, requirement of the principal component analysis, obtaining the principal component analysis | Source reading | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Problem Çözme |
| 2 | Properties of the principal components, determine the number of principal components, examples | Source reading | Öğretim Yöntemleri: Anlatım, Tartışma, Problem Çözme |
| 3 | Factor analysis, purpose of factor analysis, the relationship of factor analysis to principle component analysis | Source reading | Öğretim Yöntemleri: Anlatım, Tartışma, Problem Çözme |
| 4 | Principle factor method, factor rotation | Source reading | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 5 | Principal component analysis and factor analysis by using SPSS and Minitab package programs | Source reading | Öğretim Yöntemleri: Anlatım, Tartışma, Problem Çözme |
| 6 | Canonical correlation analysis, purpose of canonical correlation analysis, obtaining the canonical correlations | Source reading | Öğretim Yöntemleri: Anlatım, Tartışma, Problem Çözme |
| 7 | Test of significance for canonical correlations, examples, canonical correlation analysis by using SPSS and Minitab package programs | Source reading | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Discriminant analysis, discriminant analysis for two groups | Source reading | Öğretim Yöntemleri: Anlatım, Tartışma, Problem Çözme |
| 10 | Discriminant analysis for more than two groups, examples, discriminant analysis by using SPSS and Minitab package programs | Source reading | Öğretim Yöntemleri: Tartışma, Problem Çözme, Alıştırma ve Uygulama, Anlatım |
| 11 | Measures of similarity and dissimilarity | Source reading | Öğretim Yöntemleri: Anlatım, Tartışma, Problem Çözme |
| 12 | Cluster analyis, clustering methods, examples | Source reading | Öğretim Yöntemleri: Anlatım, Tartışma, Problem Çözme |
| 13 | Cluster analyis by using SPSS and Minitab package programs | Source reading | Öğretim Yöntemleri: Tartışma, Problem Çözme, Alıştırma ve Uygulama |
| 14 | Multidimensional scaling prodecures | Source reading | Öğretim Yöntemleri: Anlatım, Tartışma, Problem Çözme |
| 15 | Comparing the multidimensional scaling prodecures with each other, comparing the multidimensional scaling prodecures to the principal component analysis, examples | Source reading | Öğretim Yöntemleri: Anlatım, Tartışma, Problem Çözme, Alıştırma ve Uygulama |
| 16 | Term Exams | Review the topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Review the topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | 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 | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 114 | ||
| Total Workload / 25 (h) | 4,56 | ||
| ECTS | 5 ECTS | ||