ISB424 Multivariate Statistical Analysis

5 ECTS - 3-0 Duration (T+A)- 8. Semester- 3 National Credit

Information

Code ISB424
Name Multivariate Statistical Analysis
Term 2024-2025 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
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY
Course Instructor
1 2
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

UC Irvine Machine Learning Repository-https://archive.ics.uci.edu/


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 Explain the purpose of the principal component analysis and factor analysis.
LO06 Explain the relationship of the principal component analysis and factor analysis.
LO07 Know measures of similarity and dissimilarity using cluster analysis.
LO08 Explain the concept of correlation and why we use the canonical correlation analysis with examples.
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 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, Tartışma, Problem Çözme
2 Properties of the principal components, determine the number of principal components, examples Source reading Öğretim Yöntemleri:
Anlatım, Tartışma, Problem Çözme, Alıştırma ve Uygulama
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, Alıştırma ve Uygulama
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
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

Update Time: 12.06.2024 11:09