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Course Description Course Name : Advanced Regression Analysis II Course Code : EM 406 Course Type : Compulsory Level of Course : First Cycle Year of Study : 4 Course Semester : Spring (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. GÜLSEN KIRAL Res.Asst. FELA ÖZBEY Learning Outcomes of the Course : Examines the data find the best way to obtain the best model Checks the model assumptions Learns hypothesis testing and confidence intervals for the parameters of the model Finds the best model for the data sets using the statistical package programs Performs statistical reviews about the proposed model Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : To create the infrastructure necessary theoretical training during the period of education .Training and Analysis of the data can face the public and private sector Course Contents : Multiple Linear Regression, Estimation of New observations, Standardized Regression Coefficients, Methods in Determining the Eligibility of the model, Polynomial regression models, Adequacy of methods for Regression Model Variable Selection methods Basic component regression Ridge regression The variable selection methods, Logistic regression Language of Instruction : Turkish Work Place : Clasrooms (3. Blok) Computer lab. (2. Blok)   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Multiple Linear Regression, read the relevant chapter from the book lectures 2 Estimation of New observations, read the relevant chapter from the book lectures 3 Standardized Regression Coefficients, read the relevant chapter from the book lectures 4 Methods in Determining the Eligibility of the model, read the relevant chapter from the book lectures 5 Polynomial regression models, read the relevant chapter from the book lectures 6 Adequacy of methods for Regression Model read the relevant chapter from the book lectures 7 repetition and computer application 8 midterm exam 9 Variable Selection methods read the relevant chapter from the book lectures 10 Basic component regression read the relevant chapter from the book lectures 11 Ridge regression read the relevant chapter from the book lectures 12 The variable selection methods, read the relevant chapter from the book lectures 13 Logistic regression read the relevant chapter from the book lectures 14 repetition and computer application 15 homework presentation 16/17 final exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Samprit C. Hadi A. Regression Analysis by Example Bertham Price  Miller, I. and M. Miller (2004). Mathematical Statistics with Applications , Pearson Education.  Reha Alprar 2003 .”Uygulamalı Çok Değişkenli İstatistiksel Yöntemlere Giriş 1 “  Mendenhall, W. and T. Sincich (1996). A Second Course in statistics: Regression Analysis , Prentice Hall  Uygulamalı Regresyon ve Korelasyon Analizi, Neyran Orhunbilge. İ.Ü. İŞLETME FAKÜLTESİ .Avcıol Basım Yayın / Ders Kitapları Dizisi 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 2 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 Models problems with Mathematics, Statistics, and Econometrics 3 2 Explains Econometric concepts 3 3 Estimates the model consistently and analyzes & interprets its results 3 4 Acquires basic Mathematics, Statistics and Operation Research concepts 4 5 Equipped with the foundations of Economics, and develops Economic models 2 6 Describes the necessary concepts of Business 1 7 Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems 3 8 Collects, edits, and analyzes data 4 9 Uses a package program of Econometrics, Statistics, and Operation Research 4 10 Effectively works, take responsibility, and the leadership individually or as a member of a team 3 11 Awareness towards life-long learning and follow-up of the new information and knowledge in the field of study 3 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 3 13 Uses Turkish and at least one other foreign language, academically and in the business context 3 14 Good understanding, interpretation, efficient written and oral expression of the people involved 3 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 3 17 Follows actuality, and interprets the data about economic and social events 3 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 3 42 Assesment Related Works     Homeworks, Projects, Others 2 18 36     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 12 12 Total Workload: 142 Total Workload / 25 (h): 5.68 ECTS Credit: 6