# GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
NONPARAMETRIC STATISTICS/İST4003
 Course Title: NONPARAMETRIC STATISTICS Credits 4 ECTS 5 Course Semester 7 Type of The Course Compulsory
COURSE INFORMATION
-- (CATALOG CONTENT)
-- (TEXTBOOK)
-- (SUPPLEMENTARY TEXTBOOK)
-- (PREREQUISITES AND CO-REQUISITES)
-- LANGUAGE OF INSTRUCTION
Turkish
-- COURSE OBJECTIVES
-- COURSE LEARNING OUTCOMES
The statistical textbooks which include latest information about statistics, equipment and other resources supported by scientific approach on undergr
Statisticians by using knowledge and skills acquired at bachelor degree level model, analyze, and interpret datasets.
Statisticians identify and analyze the problems with current developments in statistic and also develop solutions based upon researches and proofs.
Statisticians apply theoretical and practical knowledge acquired in Statistics at bachelor degree level to the current problems.
Statisticians have the ability to use computer software and computing technology at the certain level required by statistics field.
Statisticians take responsibility at disciplinary and interdisciplinary studies as an individual or a team member.
Statisticians must have knowledge and ability to follow development in the field of Statistics, and must develop life long-learning attitudes.
By using a foreign language, statistician can keep track of every statistical information, and communicate with colleagues.
Applying the statistical knowledge in the professional sense, statistician has social, scientific, and ethical values.
A statistician must have the ability to social sensitivity and socialization.During the process of inference, a statistician uses time efficiently wit

-- MODE OF DELIVERY
The modes of delivery of this course are Face to face and Lab. application.
 --WEEKLY SCHEDULE 1. Week Application area of nonparametric tests 2. Week Order statistics and rank-order statistics. 3. Week Chi-square and one sample Kolmogorov-Smirnov tests for goodness of fit. 4. Week Binom test, one sample sign test and application in Lab 5. Week Wilcoxon signed-ranks test and one sample run test and application in Lab 6. Week Median test, Mann-Whitney test and application in Lab. 7. Week Two samples Kolmogorov-Smirnov test and Wald-Wolfowitz run test 8. Week Fisher exact test, sign test for two dependent samples and application in Lab. MIDTERM EXAM 9. Week Wilcoxon signed-ranks test and Mc-Nemar test for two dependent samples 10. Week Mood test and Siegel-Tukey test for the equality of two dispersion parameters 11. Week Kruskal-Wallis , multiple comparisons and application in Lab. 12. Week Friedmans test and multiple comparisons. 13. Week Cochrans Q test and Spearman rank correlation coefficient. 14. Week Kendall Tau coefficient and application in Lab. 15. Week FINAL EXAM 16. Week
-- TEACHING and LEARNING METHODS
-- ASSESSMENT CRITERIA
 Quantity Total Weighting (%) Midterm Exams 1 30 Assignment 2 10 Application 0 0 Projects 0 0 Practice 0 0 Quiz 0 0 Percent of In-term Studies 40 Percentage of Final Exam to Total Score 60