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 FACULTY of SCIENCE / DEPARTMENT of STATISTICS and COMPUTER SCIENCES Course Catalog http://www.ktu.edu.tr/isbb Phone: +90 0462 +90 (462) 3773112 FENF
FACULTY of SCIENCE / DEPARTMENT of STATISTICS and COMPUTER SCIENCES /

 IST4020 Multivariate Statistical Analysis 4+0+0 ECTS:6 Year / Semester Spring Semester Level of Course First Cycle Status Compulsory Department DEPARTMENT of STATISTICS and COMPUTER SCIENCES Prerequisites and co-requisites None Mode of Delivery Contact Hours 14 weeks - 4 hours of lectures per week Lecturer Dr. Öğr. Üyesi Uğur ŞEVİK Co-Lecturer PROF. DR. Türkan ERBAY DALKILIÇ, PROF. DR. Zafer KÜÇÜK Language of instruction Turkish Professional practise ( internship ) None The aim of the course: The aim of the course is to help students use techniques that will help explain the relationships between multiple variables and to help them evaluate the results obtained.
 Learning Outcomes CTPO TOA Upon successful completion of the course, the students will be able to : LO - 1 : To learn the basic concepts of multivariate statistics. 1,2,3,4 1 LO - 2 : To be able to evaluate a scientific research in terms of multivariate statistical techniques. 1,2,3,4 1 LO - 3 : To be able to decide which multivariate statistical method to use in a scientific research. 1,2,3,4 1 LO - 4 : To be able to analyze multidimensional data on computer. 1,2,3,4 1 CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome
 Contents of the Course
 Basic Matrix Knowledge, Data Matrix in Multivariate Analysis and Descriptive Statistics, Multivariate Graphs, Standardization, Multivariate Normal Distribution and Multivariate Oversights, Examination and Assignment Methods of Missing Data, Distance and Similarity Measures, Multivariate Hypothesis Tests, Factor Analysis, Cluster Analysis.
 Course Syllabus Week Subject Related Notes / Files Week 1 Basic definitions and concepts: Matrices, determinants, eigenvalues and eigenvectors Week 2 Basic definitions and concepts: Matrices, determinants, eigenvalues and eigenvectors Week 3 Data Matrix and Descriptive Statistics in Multivariate Analysis Week 4 Multivariate Graphics Week 5 Standardization, Week 6 Multivariate Normal Distribution and Multivariate Extremes. Week 7 Examination and Assignment Methods of Missing (Lost) Data. Week 8 Distance and Similarity Measures Week 9 Mid-term exam Week 10 Correlation coefficient: simple, partial, multi Week 11 Multivariate Hypothesis Tests Week 12 Factor Analysis Week 13 Factor Analysis Week 14 Cluster Analysis Week 15 Cluster Analysis Week 16 End-of-term exam
 Textbook / Material
 1 Alpar, R. 2020, Uygulamalı Çok Değişkenli İstatistiksel Yöntemler, Detay Yayıncılık 2 Alpar, R. 2020, Uygulamalı Çok Değişkenli İstatistiksel Yöntemler, Detay Yayıncılık