Description of Individual Course Units
 Course Unit Code Course Unit Title Type of Course Unit Year of Study Semester Number of ECTS Credits İST404 APPLICATIONS OF MULTIVARIATE STATISTICS Compulsory 4 8 7
Level of Course Unit
First Cycle
Objectives of the Course
To make the students learn the Multivariate Statistical Analysis Methods and apply them in computer environment
Name of Lecturer(s)
Assoc. Prof. Dr. Hakan Savaş SAZAK
Learning Outcomes
 1 To know which Multivariate Statistical Analysis is appropriate for a problem in a research project 2 To know how to conduct the required operations on data and prepare the required tables and graphics using MS Excel software 3 To be able to determine whether Univariate or Multivariate data are normally distributed 4 To be able to detect possible outliers in Multivariate data 5 To be able to conduct the application of ANOVA and MANOVA in computer environment 6 To be able to conduct the application of Multivariate Regression in computer environment 7 To be able to conduct Principal Components and Factor Analysis in computer environment 8 To be able to conduct Classification and Discriminant Analysis in computer environment 9 To be able to conduct Cluster Analysis in computer environment 10 To be able to conduct Multidimensional Scaling in computer environment
Mode of Delivery
Face to Face
Prerequisites and co-requisities
None
Recommended Optional Programme Components
It is advised that students took the course Multivariate Statistics
Course Contents
To know which Multivariate Statistical Analysis is appropriate for a problem in a research project ; To know how to conduct the required operations on data and prepare the required tables and graphics using MS Excel software; To be able to determine whether Univariate or Multivariate data are normally distributed; To be able to detect possible outliers in Multivariate data; To be able to conduct the application of ANOVA and MANOVA in computer environment; To be able to conduct the application of Multivariate Regression in computer environment; To be able to conduct Principal Components and Factor Analysis in computer environment; To be able to conduct Classification and Discriminant Analysis in computer environment; To be able to conduct Cluster Analysis in computer environment; To be able to conduct Multidimensional Scaling in computer environment
Weekly Detailed Course Contents
 Week Theoretical Practice Laboratory 1 Introduction of Multivariate Statistical Analysis Methods 2 Introduction of MS Excel 3 Preparation of Tables, creation of various statistical functions and graphs using MS Excel 4 Construction of Q-Q graph and detection of possible outliers using MS Excel 5 Obtaining the mean vector, the variance-covariance matrix and the Mahalanobis distance of data using MS Excel 6 Check of Multivariate normality and Box-Cox transformation using MS Excel 7 Conducting tests based on means and constructing confidence intervals using MS Excel 8 Midterm Exam 9 MANOVA 10 Multivariate Regression 11 Principal Components Analysis 12 Factor Analysis 13 Classification and Discriminant Analysis 14 Cluster Analysis 15 Multidimensional Scaling 16 Final Exam
Applied Multivariate Statistical Analysis (R. A. Johnson & D. W. Wichern, Fifth Edition, 2002) Özdamar, K., Paket Programlar ile İstatistiksel Veri Analizi Tatlıdil, H., Uygulamalı Çok Değişkenli İstatistiksel Analiz
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
 Term (or Year) Learning Activities Quantity Weight Midterm Examination 1 100 SUM 100 End Of Term (or Year) Learning Activities Quantity Weight Final Sınavı 1 100 SUM 100 Term (or Year) Learning Activities 40 End Of Term (or Year) Learning Activities 60 SUM 100
Language of Instruction
English
Work Placement(s)
None