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Information
| Code | ISB424 |
| Name | Multivariate Statistical Analysis |
| Term | 2023-2024 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 | Lisans Dersi |
| Type | Normal |
| Label | C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY |
| Course Instructor |
Prof. Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY
(A Group)
(Ins. in Charge)
|
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
Lecture Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Determine the structure of the data consisting of a very large number of variables. |
| LO02 | Convert the data consisting of a very large number of variables into a form as simple as possible. |
| LO03 | Make the principal component analysis. |
| LO04 | Make the factor analysis. |
| LO05 | Learn the purpose of the principal component analysis and factor analysis. |
| LO06 | Comprehend the relationship of the principal component analysis and factor analysis. |
| LO07 | Know measures of similarity and dissimilarity using cluster analysis. |
| LO08 | Comprehend the concept of correlation and why we use the canonical correlation analysis. |
| LO09 | Make the discriminant analysis in case of two or more than two groups. |
| LO10 | Use the multidimensional scaling procedures. |
| LO11 | Perform the principal component analysis, factor analysis, cluster analysis, canonical correlation analysis and multidimensional scaling by using statistical package programs (SPSS and Minitab) |
| LO12 | Decide which analysis would be appropriate to use 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 Probability, Statistics and Mathematics | 3 |
| 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 | 4 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Use proper methods and techniques to gather and/or to arrange the data | 5 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Utilize computer systems and softwares | 4 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events | |
| PLO08 | Bilgi - Kuramsal, Olgusal | Apply the statistical analyze methods | 5 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Make statistical inference(estimation, hypothesis tests etc.) | 5 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Generate solutions for the problems in other disciplines by using statistical techniques | 5 |
| PLO11 | Bilgi - Kuramsal, Olgusal | Discover the visual, database and web programming techniques and posses the ability of writing programme | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Construct a model and analyze it by using statistical packages | 5 |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Distinguish the difference between the statistical methods | 5 |
| PLO14 | Beceriler - Bilişsel, Uygulamalı | Be aware of the interaction between the disciplines related to statistics | 4 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Make oral and visual presentation for the results of statistical methods | |
| PLO16 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have capability on effective and productive work in a group and individually | |
| PLO17 | 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 | |
| PLO18 | 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 | |
| 2 | Properties of the principal components, determine the number of principal components, examples | Source reading | |
| 3 | Factor analysis, purpose of factor analysis, the relationship of factor analysis to principle component analysis | Source reading | |
| 4 | Principle factor method, factor rotation | Source reading | |
| 5 | Principal component analysis and factor analysis by using SPSS and Minitab package programs | Source reading | |
| 6 | Canonical correlation analysis, purpose of canonical correlation analysis, obtaining the canonical correlations | Source reading | |
| 7 | Test of significance for canonical correlations, examples, canonical correlation analysis by using SPSS and Minitab package programs | Source reading | |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes and sources | |
| 9 | Discriminant analysis, discriminant analysis for two groups | Source reading | |
| 10 | Discriminant analysis for more than two groups, examples, discriminant analysis by using SPSS and Minitab package programs | Source reading | |
| 11 | Measures of similarity and dissimilarity | Source reading | |
| 12 | Cluster analyis, clustering methods, examples | Source reading | |
| 13 | Cluster analyis by using SPSS and Minitab package programs | Source reading | |
| 14 | Multidimensional scaling prodecures | Source reading | |
| 15 | Comparing the multidimensional scaling prodecures with each other, comparing the multidimensional scaling prodecures to the principal component analysis, examples | Source reading | |
| 16 | Term Exams | Review the topics discussed in the lecture notes and sources | |
| 17 | Term Exams | Review the topics discussed in the lecture notes and sources |
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 | ||