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Course Description Course Name : Experimental Designs Course Code : ISB-547 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY Learning Outcomes of the Course : Explain the basic concepts of Anova and the reasons for using Anova. Construct the one-way Anova and analyze it. Check the assumptions necessary for the analysis of variance model. Estimate the unknown parameters in the analysis of variance model. Generate the expected mean square error according to the fixed effect and random effect and distinguish the difference between the two. Apply confidence intervals and hypothesis tests about the parameters . Determine the sources of differences (which experiment or experiments) in case of rejection of the null hypothesis. Construct the two-way Anova and analyze it. Construct the Latin square and Greko-Latin square designs and analyze them. Construct the nested and factorial designs and analyze them. Perform Anova by using SPSS and Minitab 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 design an experiment and apply an appropriate statistical method. Course Contents : Basic Concepts, One-way Anova, Binary and multiple comparisons, Two-way Anova, Designs of Latin Square and Greko-Latin square, Nested design, Factorial design 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 Basic concepts Reading the references Lecture, discussion 2 One-way Anova Reading the references Lecture, discussion, problem-solving 3 One-way Anova Reading the references Lecture, discussion, problem-solving, using statistical package program 4 One-way Anova Reading the references Lecture, discussion, problem-solving, using statistical package program 5 Binary and multiple comparisons Reading the references Lecture, discussion, problem-solving, using statistical package program 6 Binary and multiple comparisons Reading the references Lecture, discussion, problem-solving, using statistical package program 7 Two-way Anova (Case 1) Reading the references Lecture, discussion, problem-solving 8 Mid-Term Exam Review the topics discussed in the lecture notes and sources Written Exam 9 Two-way Anova (Cases 2 and 3) Reading the references Lecture, discussion, problem-solving, using statistical package program 10 Latin square design Reading the references Lecture, discussion, problem-solving, using statistical package program 11 Greko-Latin square design Reading the references Lecture, discussion, problem-solving, using statistical package program 12 Nested design Reading the references Lecture, discussion, problem-solving, using statistical package program 13 Nested design Reading the references Lecture, discussion, problem-solving, using statistical package program 14 Factorial design Reading the references Lecture, discussion, problem-solving, using statistical package program 15 Factorial design Reading the references Lecture, discussion, problem-solving, 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)  R. H. Myers, D. C. Montgomery "Responce SurfaceMethodology", John Wiley & Sons, Inc.1995  W. Cochran, "Experimental Design" John Wiley & Sons, Inc.1996  Douglas, C., M., Design and Analysis of Experiment, Wiley, 7 edition 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 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. 3 7 Implement innovative methods for resolving problems in the field of Statistics. 3 8 Develop analytical modeling and experimental research designs to implement solutions. 0 9 Gather data in order to complete a research. 0 10 Develop approaches for solving complex problems by taking responsibility. 0 11 Take responsibility with self-confidence. 3 12 Have the awareness of new and emerging applications in the profession 4 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. 2 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. 4 * 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 20 20 Total Workload: 154 Total Workload / 25 (h): 6.16 ECTS Credit: 6