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Course Description Course Name : Multivariate Statistical Analysis Course Code : İSB423 Course Type : Compulsory Level of Course : First Cycle Year of Study : 4 Course Semester : Fall (16 Weeks) ECTS : 5 Name of Lecturer(s) : Asst.Prof.Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY Learning Outcomes of the Course : Know the basic concepts of multivariate statistics Know the purpose of using of multivariate statistics Determine the mean vector, variance-covariance and correlation matrices for multivariate data Learn the probability density function, marginal probability density function, conditional distribution and statistical independency for multivariate distributions Obtain the moment generating function, marginal probability density function, conditional probability density function and parameter estimates for multivariate normal distribution Test the hypothesis about the multivariate data Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : To construct the necessary theoretical background for multivariate statistical analysis. Course Contents : Basic concepts of multivariate statistics, multivariate normal distribution, testing hypothesis about the multivariete data Language of Instruction : Turkish Work Place : Faculty of Arts and Sciences Annex Classrooms   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Basic concepts of multivariate statistics Source reading Lecture, Problem-solving, Question & Answer 2 Matrix theory for multivariate statistical analysis Source reading Lecture, Problem-solving, Question & Answer 3 Matrix theory for multivariate statistical analysis Source reading Lecture, Problem-solving, Question & Answer 4 Mean vector, variance-covariance matrix, correlation matrix Source reading Lecture, Problem-solving, Question & Answer 5 Probability density function, marginal probability density function, conditional distribution and statistical independency for multivariate distributions Source reading Lecture, Problem-solving, Question & Answer 6 Probability density function, characteristic functions, moments, moment generating function and parameter estimates Source reading Lecture, Problem-solving, Question & Answer 7 Maximum likelihood estimators for population parameters Source reading Lecture, Problem-solving, Question & Answer 8 Mid-term exam Review the topics discussed in the lecture notes and sources Written exam 9 Marginal normal distribution, conditional normal distribution Source reading Lecture, Problem-solving, Question & Answer 10 Distribution of linear relations, independency of subvector variables Source reading Lecture, Problem-solving, Question & Answer 11 Obtaining the parameters given the density function Source reading Lecture, Problem-solving, Question & Answer 12 Multivariate test methods (likelihood ratiı test) Source reading Lecture, Problem-solving, Question & Answer 13 Multivariate test methods (composition-intersection test) Source reading Lecture, Problem-solving, Question & Answer 14 Test on mean vectors Source reading Lecture, Problem-solving, Question & Answer 15 Test on covariance matrices, Testing hypothesis with statistical package programs. Source reading Lecture, Problem-solving, Question & Answer, Using statistical package program 16/17 Final exam Review the topics discussed in the lecture notes and sources Written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Tatlıdil, Hüseyin (2002). Uygulamalı Çok Değişkenli İstatistiksel Analiz, Ankara  Srivastava, M.S. (2002) Methods of Multivariate Statistics, John Wiley & Sons, Inc., Publication.  Rencher, A.C. (2002). Methods of Multivariate Analysis (2nd Edition), John Wiley & Sons, Inc., Publication.  Kalaycı, Şeref (2010). SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri, Asil Yayın Dağıtım, Ankara. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 60     Homeworks/Projects/Others 3 40 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 Utilize computer systems and softwares 2 2 Apply the statistical analyze methods 5 3 Make statistical inference(estimation, hypothesis tests etc.) 5 4 Generate solutions for the problems in other disciplines by using statistical techniques 5 5 Discover the visual, database and web programming techniques and posses the ability of writing programme 0 6 Construct a model and analyze it by using statistical packages 4 7 Distinguish the difference between the statistical methods 4 8 Be aware of the interaction between the disciplines related to statistics 4 9 Make oral and visual presentation for the results of statistical methods 4 10 Have capability on effective and productive work in a group and individually 0 11 Develop scientific and ethical values in the fields of statistics-and scientific data collection 0 12 Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 5 13 Emphasize the importance of Statistics in life 5 14 Define basic principles and concepts in the field of Law and Economics 0 15 Produce numeric and statistical solutions in order to overcome the problems 4 16 Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events 3 17 Use proper methods and techniques to gather and/or to arrange the data 3 18 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 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 3 42 Assesment Related Works     Homeworks, Projects, Others 3 7 21     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 15 15 Total Workload: 130 Total Workload / 25 (h): 5.2 ECTS Credit: 5