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Course Description Course Name : Nonparametric Statistics Course Code : İSB302 Course Type : Compulsory Level of Course : First Cycle Year of Study : 3 Course Semester : Spring (16 Weeks) ECTS : 5 Name of Lecturer(s) : Prof.Dr. SADULLAH SAKALLIOĞLU Learning Outcomes of the Course : Understand the difference between parametric and nonparametric statistical methods, Know which method you can use Apply sign test and Wilcoxon signed rank test Apply median test and Mann-Whitney U test Apply Mood test and Moses test have an understanding of related two sample test Know k independent sample test and approximation to chi-square statistics Know Friedman´s S test and approximation to chi-square test Know goodness of fit tests Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : Selecting the appropriate tests to test hypotheses and gain the ability to apply non-parametric tests Course Contents : Basic concepts, the difference between parametric and nonparametric statistics, one sample tests: Chi-square test, Kolmagorov-Smirnov test, related two sample tests: McNemar test, sign test, Wilcoxon matched-pairs signed-ranks test, Walsh test, independent two samples tests: Fisher’s exact probability test, median test, Mann-Whitney U test, Kolmagorov-Smirnov two samples test, Moses test, correlation coefficients: Sperman’s rank correlation coefficient, Kendal’s Tau correlation coefficient, Friedman’s S test, Cochran’s Q test. Language of Instruction : Turkish Work Place : Faculty of Arts and Sciences Annex Classrooms   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Basic Concepts Source reading Lecture, discussion and problem-solving 2 the difference between parametric and nonparametric statistics Source reading Lecture, discussion and problem-solving 3 Levels of measurement Source reading Lecture, discussion and problem-solving 4 one sample tests: sign test, Wilcoxon signed rank test Source reading Lecture, discussion and problem-solving 5 independent two samples tests: median test, Mann-Whitney U test Source reading Lecture, discussion and problem-solving 6 mood test, Moses test, Source reading Lecture, discussion and problem-solving 7 related two sample tests: sign test, Wilcoxon matched-pairs signed-ranks test, Source reading Lecture, discussion and problem-solving 8 Mid-term exam Rewview the topics discussed in the lecture notes and sources Written exam 9 Chi - square tests Source reading Lecture, discussion and problem-solving 10 Kruskal-Wallis test Source reading Lecture, discussion and problem-solving 11 sampling distribution of H statistics and chi - square statistics Source reading Lecture, discussion and problem-solving 12 Friedman’ın S Test Source reading Lecture, discussion and problem-solving 13 sampling distribution of S statistics and chi - square Source reading Lecture, discussion and problem-solving 14 Goodness of Fit Tests Source reading Lecture, discussion and problem-solving 15 correlation coefficients: Sperman’s rank correlation coefficient, Kendal’s Tau correlation coefficient, Source reading Lecture, discussion and problem-solving 16/17 Final exam Rewview the topics discussed in the lecture notes and sources Written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Hamza Gamgam(1998), Parametrik Olmayan İstatistiksel Teknikler, İkinci Baskı, Gazi Yayınları, Ankara. Required Course Material(s)  Sidney Siegel (Çeviren:Yurdal Topsever) (1977), Davranış Bilimlerinde Parametrik Olmayan İstatistikler, Ankara Üniversitesi Basımevi, Ankara.  W. J. Conover (1998). Practical Nonparametric Statistics, Wiley; 3rd edition.   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 80     Homeworks/Projects/Others 6 20 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 Utilize computer systems and softwares 0 2 Apply the statistical analyze methods 5 3 Make statistical inference(estimation, hypothesis tests etc.) 4 4 Generate solutions for the problems in other disciplines by using statistical techniques 0 5 Discover the visual, database and web programming techniques and posses the ability of writing programme 0 6 Construct a model and analyze it by using statistical packages 0 7 Distinguish the difference between the statistical methods 4 8 Be aware of the interaction between the disciplines related to statistics 0 9 Make oral and visual presentation for the results of statistical methods 0 10 Have capability on effective and productive work in a group and individually 3 11 Develop scientific and ethical values in the fields of statistics-and scientific data collection 5 12 Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 3 13 Emphasize the importance of Statistics in life 4 14 Define basic principles and concepts in the field of Law and Economics 0 15 Produce numeric and statistical solutions in order to overcome the problems 5 16 Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events 4 17 Use proper methods and techniques to gather and/or to arrange the data 0 18 Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 3 * 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 6 3 18     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 10 10 Total Workload: 122 Total Workload / 25 (h): 4.88 ECTS Credit: 5