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Course Description Course Name : Regression Theory - I Course Code : ISB-541 Course Type : Compulsory Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Assoc.Prof.Dr. MAHMUDE REVAN ÖZKALE Learning Outcomes of the Course : Apply the multiple linear regression model Make inference in multiple linear regression Explain model assumptions Control the model adequacy Do residual analysis Apply required methods in case of model adequacy Identify influential observations Explain polynomial regression model Analyze multiple regression model by using statistical package programs 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 do models for multiple regression models and perform the adequacy analysis Course Contents : Multiple linear regression, model adequacy checking, correcting model inadequacies, diagnostics for leverages and influence, polynomial regression models Language of Instruction : Turkish Work Place : Department Seminar Room   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Multiple regression models, least squares estimates of regression coefficients and properties Reading the related references Lecture, discussion 2 Estimation of the variance of the error, maximum likelihood estiamtion, coefficient of determination, testing the significance of regression Reading the related references Lecture, discussion and using the statistical package programs 3 Hypothesis testing on the individual regression coefficients, test of general linear hypothesis Reading the related references Lecture, discussion and using the statistical package programs 4 Confidence interval in multiple regression, prediciton of new observations Reading the related references Lecture, discussion and using the statistical package programs 5 Extrapolation, standardization of regression coefficients Reading the related references Lecture, discussion 6 Model adequacy checking, residual analysis Reading the related references Lecture, discussion and using the statistical package programs 7 Methods for scaling the residuals, residual graphics Reading the related references Lecture, discussion and using the statistical package programs 8 Mid-term exam Review the topics discussed in the lecture notes and sources Written exam 9 Lack of fit analysis of regression model Reading the related references Lecture, discussion and using the statistical package programs 10 Transformations and weighteing to correct model inadequacies Reading the related references Lecture, discussion and using the statistical package programs 11 Analitical methods to identify the transformations Reading the related references Lecture, discussion and using the statistical package programs 12 Generalized and weighted least squares Reading the related references Lecture, discussion and using the statistical package programs 13 Detection for influential nad leverage observations Reading the related references Lecture, discussion and using the statistical package programs 14 Polynomial models in one variable Reading the related references Lecture, discussion and using the statistical package programs 15 Polynomial models in to or more variables Reading the related references Lecture, discussion 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)  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)   Myers R. H. (1990), Classical and Modern Regression with Applications, Duxbury Press.   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 4 56 Assesment Related Works     Homeworks, Projects, Others 5 5 25     Mid-term Exams (Written, Oral, etc.) 1 5 5     Final Exam 1 10 10 Total Workload: 138 Total Workload / 25 (h): 5.52 ECTS Credit: 6