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Course Description Course Name : Multivariate Analysis Course Code : EM 319 Course Type : Compulsory Level of Course : First Cycle Year of Study : 3 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. EBRU ÖZGÜR GÜLER Learning Outcomes of the Course : Explains multivariate data Builds the data matrix Knows dimension reduction techniques and explains their assumptions Knows classification methods and explains their assumptions Discriminates the most proper analysis technique for the data considered Uses a package program to do analyzes for each technique and interprets obtained results Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : The aim of this course is the building of the data matrix for multivariate analysis, choosing the most suitable method for the data, and application of the proper technique once the assumptions are verified. Course Contents : The course content covers the building of multivariate data, which includes understanding, preparing and transforming of the data, comparing the methods related to dimension reduction and classification, assumptions and applications of multivariate techniques. Language of Instruction : Turkish Work Place : Faculty classrooms   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Motivation: A Review of references and introductory matrix algebra Lecture 2 Multivariate graphics, standardization and multivariate normal distribution Related chapters of reference books Lecture 3 Multivariate graphics, standardization and multivariate normal distribution Related chapters of reference books Lecture 4 Examination of outliers and missing data in multivariate analysis, distance and similiarity measures Related chapters of reference books Lecture 5 Multivariate hypothesis tests Related chapters of reference books Lecture 6 Dimension Reduction: Factor analysis and its assumptions Related chapters of reference books Lecture and package program 7 Dimension Reduction (cont): Factor analysis and its assumptions Homework #1: Application of factor analysis Lecture and package program 8 Midterm 9 Classification: Clustering analysis and its assumptions Related chapters of reference books Lecture and package program 10 Classification (cont): Clustering analysis and its assumptions Related chapters of reference books Lecture and package program 11 Classification: Assumptions of discriminant analysis Related chapters of reference books Lecture and package program 12 Classification (cont): Applications of discriminant analysis Related chapters of reference books Lecture and package program 13 Classification: Assumptions of logistic regression Related chapters of reference books Lecture and package program 14 Classification (cont): Applications of logistic regression Related chapters of reference books Lecture and package program 15 General review and computer applications Homework #2: Application of classyfying methods Lecture and package program 16/17 Final Exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  ALPAR, Reha (2011), Uygulamalı Çok Değişkenli İstatistiksel Yöntemler. Detay Yayıncılık, 3. Baskı.  Şener BÜYÜKÖZTÜRK, Güçlü ŞEKERCİOĞLU, Ömay ÇOKLUK (2012), Sosyal Bilimler İçin Çok Değişkenli İstatistik: SPSS ve Lisrel Uygulamaları. Pegem Akademik Yayıncılık, 2. Baskı. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 80     Homeworks/Projects/Others 2 20 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Models problems with Mathematics, Statistics, and Econometrics 4 2 Explains Econometric concepts 3 3 Estimates the model consistently and analyzes & interprets its results 5 4 Acquires basic Mathematics, Statistics and Operation Research concepts 4 5 Equipped with the foundations of Economics, and develops Economic models 0 6 Describes the necessary concepts of Business 0 7 Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems 4 8 Collects, edits, and analyzes data 5 9 Uses a package program of Econometrics, Statistics, and Operation Research 3 10 Effectively works, take responsibility, and the leadership individually or as a member of a team 1 11 Awareness towards life-long learning and follow-up of the new information and knowledge in the field of study 1 12 Develops the ability of using different resources in the form of academic rules, synthesis the information gathered, and effective presentation in an area which has not been studied 2 13 Uses Turkish and at least one other foreign language, academically and in the business context 1 14 Good understanding, interpretation, efficient written and oral expression of the people involved 1 15 Questions traditional approaches and their implementation while developing alternative study programs when required 3 16 Recognizes and implements social, scientific, and professional ethic values 1 17 Follows actuality, and interprets the data about economic and social events 1 18 Improves himself/herself constantly by defining educational requirements considering interests and talents in scientific, cultural, art and social fields besides career development 0 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 4 56 Assesment Related Works     Homeworks, Projects, Others 2 12 24     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 12 12 Total Workload: 144 Total Workload / 25 (h): 5.76 ECTS Credit: 6