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COURSE INFORMATON
Course Title Code Semester L+P Hour Credits ECTS
Multivariate Statistical Analysis * ISB   424 8 3 3 5

 Prerequisites and co-requisites Yok Recommended Optional Programme Components None

Language of Instruction Turkish
Course Level First Cycle Programmes (Bachelor's Degree)
Course Type
Course Coordinator Assoc.Prof.Dr. Gülesen ÜSTÜNDAĞ ŞİRAY
Instructors
 Doç.Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY 1. Öğretim Grup:A Doç.Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY 2. Öğretim Grup:A

Assistants
Goals
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
Content
Principal component analysis, factor analysis, canonical correlation analysis, discriminant analysis, cluster analysis, multidimensional scaling

Learning Outcomes
-

Course's Contribution To Program
NoProgram Learning OutcomesContribution
12345
1
Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics
X
2
Emphasize the importance of Statistics in life
X
3
Define basic principles and concepts in the field of Law and Economics
4
Produce numeric and statistical solutions in order to overcome the problems
X
5
Use proper methods and techniques to gather and/or to arrange the data
X
6
Utilize computer systems and softwares
X
7
Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events
X
8
Apply the statistical analyze methods
X
9
Make statistical inference(estimation, hypothesis tests etc.)
X
10
Generate solutions for the problems in other disciplines by using statistical techniques
X
11
Discover the visual, database and web programming techniques and posses the ability of writing programme
X
12
Construct a model and analyze it by using statistical packages
X
13
Distinguish the difference between the statistical methods
X
14
Be aware of the interaction between the disciplines related to statistics
X
15
Make oral and visual presentation for the results of statistical methods
X
16
Have capability on effective and productive work in a group and individually
17
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
18
Develop scientific and ethical values in the fields of statistics-and scientific data collection
X

Course Content
WeekTopicsStudy Materials _ocw_rs_drs_yontem
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
16-17 Final exam Review the topics discussed in the lecture notes and sources