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Course Description Course Name : Advanced Regression Analysis Course Code : EM 320 Course Type : Compulsory Level of Course : First Cycle Year of Study : 3 Course Semester : Spring (16 Weeks) ECTS : 4 Name of Lecturer(s) : Asst.Prof.Dr. GÜLSEN KIRAL Learning Outcomes of the Course : Investigates the relationships between variables Establishes the model based on the relationships between variables Uses the model make estimates and analyzes Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : none Aim(s) of Course : During the period of education and training to create the infrastructure necessary theoretical training and Analysis of the data can face the public and private sector Course Contents : Relationships between variables, correlation analysis, simple linear regression, multiple regression, regression models, the validity and reliability of curvilinear regression, linear regression model assumptions and assumptions deviate from the states Language of Instruction : Turkish Work Place : Clarooms (3. Blok) Comp. Lab. (2. Blok)   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Introduction to regression analysis, regression analysis, and the purposes of the definition, regression analysis, data types, Regression and Correlation Analysis Read the chapter on the textbook. lectures 2 Simple Linear Regression, Regression coefficients of the OLS (Ordinary Least Squares Method) and the estimated Read the chapter on the textbook. lectures 3 the standard error of the regression model and coefficients, significance tests and confidence intervals, analysis of variance Read the chapter on the textbook. lectures 4 The correlation coefficient, the coefficient of determination, and their significance tests Read the chapter on the textbook. lectures 5 Random error term (residues-residues) assumptions about, examining the assumption of normality of the error term Read the chapter on the textbook. lectures 6 To investigate the validity and reliability of the coefficients, elasticity coefficients Read the chapter on the textbook. lectures 7 Multiple coefficient of determination, for the validity of the regression model analysis of variance, Nonlinear simple and multiple regression models Read the chapter on the textbook. lectures 8 Midterm Exam 9 .autocorrelation,Random error term (residues-residues) assumptions about, examining the assumption of normality of the error term Read the chapter on the textbook. lectures 10 Autocorrelation problem identification and solutions. Multicollinearity problem Read the chapter on the textbook. lectures 11 Constant variance assumption (Homoskedasite), variable variance (Heterodskedasite) state of constant variance revealed problems and solutions Read the chapter on the textbook. lectures 12 problems and solutions of linear multicollinearity, example Read the chapter on the textbook. lectures 13 Multiple linear regression models, alternative methods of selection of variables to be included in the model. Dummy variable models Read the chapter on the textbook. lectures 14 Minitab and SPlus applications in solving regression models. Dummy dependent variable models Read the chapter on the text and computer books. lectures and com. app. 15 Revision 16/17 Final exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Uygulamalı Regresyon ve Korelasyon Analizi, Neyran Orhunbilge. İ.Ü. İŞLETME FAKÜLTESİ .Avcıol Basım Yayın / Ders Kitapları Dizisi     Samprit Chatterjee, Ali S. Hadi Bertham Price (2000) “Regression Analysis by Example”  Miller, I. and M. Miller (2004). Mathematical Statistics with Applications , Pearson Education.  Mendenhall, W. and T. Sincich (1996). A Second Course in statistics: Regression Analysis , Prentice Hall.  Rawlings, John O. (1988). Applied Regression Analysis: A Research Tool , Wadsworth & Brooks. 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 1 2 Explains Econometric concepts 3 3 Estimates the model consistently and analyzes & interprets its results 0 4 Acquires basic Mathematics, Statistics and Operation Research concepts 3 5 Equipped with the foundations of Economics, and develops Economic models 4 6 Describes the necessary concepts of Business 3 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 2 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 2 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 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 3 18 Improves himself/herself constantly by defining educational requirements considering interests and talents in scientific, cultural, art and social fields besides career development 1 * 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 7 14     Mid-term Exams (Written, Oral, etc.) 1 7 7     Final Exam 1 7 7 Total Workload: 112 Total Workload / 25 (h): 4.48 ECTS Credit: 4