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Course Description Course Name : Regression Theory - II Course Code : ISB-542 Course Type : Compulsory Level of Course : Second Cycle Year of Study : 1 Course Semester : Spring (16 Weeks) ECTS : 6 Name of Lecturer(s) : Assoc.Prof.Dr. MAHMUDE REVAN ÖZKALE Learning Outcomes of the Course : Apply indicator variable regression models Apply variable selection methods Evaluate multicollinearity Use methods to deal with multicollinearity Detect autocorrelation Have estimation in autocorelated regression models Form nonlinear regression models Use logistic regression model with binary response variable Create the best model that fits the data Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : To enable students with the ability to apply the best model according to the explanatory and response variables and implement different estimation methods Course Contents : Indicator variable models, variable selection and model building, multicollinearity, nonlinear regresaion, logistic regression Language of Instruction : Turkish Work Place : Department Senimar Room   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 General concept of indicator variables and comments on the use of indicator variables Reading the related references Lecture, discussion and using the statistical package programs 2 Indicator variable with more than two factors, more than one indicator variable Reading the related references Lecture, discussion 3 Regression approach to analysis of variance Reading the related references Lecture, discussion 4 Model building problem, consequences of model misspecification, criteria for evaluating subset regression models Reading the related references Lecture, discussion 5 All posible regression models Reading the related references Lecture, discussion and using the statistical package programs 6 Sources and effects of multicollinearity Reading the related references Lecture, discussion and using the statistical package programs 7 Detection of multicollinearity Reading the related references Lecture, discussion 8 Midterm exam Review the topics discussed in the lecture notes and sources Written exam 9 Ridge regression Reading the related references Lecture, discussion 10 Relationship between the ridge regresiion and other estimators, ridge regression and variable selection Reading the related references Lecture, discussion and using the statistical package programs 11 Generalized ridge regression, principal components regression Reading the related references Lecture, discussion and using the statistical package programs 12 Nonlinear regression models, nonlinear least squares, transformation to linear model Reading the related references Lecture, discussion 13 Parameter estimation in a nonlinear system Reading the related references Lecture, discussion 14 Logistic regression, Poisson regression Reading the related references Lecture, discussion and using the statistical package programs 15 Regession models with autocorrelated errors Reading the related references Lecture, discussion and using the statistical package programs 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)  1. Montgomery, D. C., Peck, E. A., Vining, G. G. (2001), Introduction to Linear Regression Analysis, 3rd edition, John Wiely & Sons Inc. Required Course Material(s)  1. Myers R. H. (1990), Classical and Modern Regression with Applications, Duxbury Press   2. Chatterjee, S., Hadi, A. S., Price, B. (2000), Regression Analysis by Example, John Wiley & Sons Inc.   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 60     Homeworks/Projects/Others 5 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 Possess advanced level of theoretical and applicable knowledge in the field of Probability and Statistics. 5 2 Conduct scientific research on Mathematics, Probability and Statistics. 5 3 Possess information, skills and competencies necessary to pursue a PhD degree in the field of Statistics. 5 4 Possess comprehensive information on the analysis and modeling methods used in Statistics. 5 5 Present the methods used in analysis and modeling in the field of Statistics. 5 6 Discuss the problems in the field of Statistics. 5 7 Implement innovative methods for resolving problems in the field of Statistics. 4 8 Develop analytical modeling and experimental research designs to implement solutions. 5 9 Gather data in order to complete a research. 3 10 Develop approaches for solving complex problems by taking responsibility. 4 11 Take responsibility with self-confidence. 3 12 Have the awareness of new and emerging applications in the profession 2 13 Present the results of their studies at national and international environments clearly in oral or written form. 0 14 Oversee the scientific and ethical values during data collection, analysis, interpretation and announcment of the findings. 5 15 Update his/her knowledge and skills in statistics and related fields continously 0 16 Communicate effectively in oral and written form both in Turkish and English. 0 17 Use hardware and software required for statistical applications. 5 * 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 5 8 40     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 10 10 Total Workload: 144 Total Workload / 25 (h): 5.76 ECTS Credit: 6