DEGREE PROGRAMS  Associate's Degree (Short Cycle)  Bachelor’s Degree (First Cycle)  Master’s Degree (Second Cycle)

Course Description Course Name : Statistical Inference Course Code : İSB352 Course Type : Compulsory Level of Course : First Cycle Year of Study : 3 Course Semester : Spring (16 Weeks) ECTS : 5 Name of Lecturer(s) : Assoc.Prof.Dr. DENİZ ÜNAL Learning Outcomes of the Course : Have knowledge about choosing estimator for the population parameters Have knowledge about simple and compound hypothesis Have knowledge about finding test statistics learn the statistical inference for parameters Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : Statistics, Distributions, parameter estimation methods, hypothesis testing and applications, confidence intervals Course Contents : statistics and distributions, sampling and statistics,Sampling distribution function and some related statistics, sampling density function, sampling percentage, Hypothesis testing, simple and compound hypothesis, test function, Neymann-Pearson lemma , application of Neymann-Pearson lemma, Bayes tests, power functions, UMPT, p-value 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 statistics and distributions, sampling and statistics Studying related sources Description, discussion and problem solving 2 Sampling distribution function and some related statistics, sampling density function, sampling percentage Studying related sources Description, discussion and problem solving 3 Skor function and fisher information. Data reductions Studying related sources Description, discussion and problem solving 4 Completeness, likelihood principle Studying related sources Description, discussion and problem solving 5 Parameter estimation methods (ML, moments, OLS, Bayes) Studying related sources Description, discussion and problem solving 6 Hypothesis testing, simple and compound hypothesis, test function Studying related sources Description, discussion and problem solving 7 Likelihood ratio test Studying related sources Description, discussion and problem solving 8 Mid-term exam Studying related sources Writing exam 9 Neymann-Pearson lemmaı , application of Neymann-Pearson lemma Studying related sources Description, discussion and problem solving 10 Bayes tests, power functions, UMPT, p-value Studying related sources Description, discussion and problem solving 11 Application of Hypothesis testing Studying related sources Description, discussion and problem solving 12 Application of Hypothesis testing Studying related sources Description, discussion and problem solving 13 Confidence intervals, point estimation Studying related sources Description, discussion and problem solving 14 Bayes confidence intervals, approximate confidence intervals Studying related sources Description, discussion and problem solving 15 Confidence intervals using Pivot Studying related sources Description, discussion and problem solving 16/17 Final exam Studying related sources Writing exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  1. Lehmann, E.L. and Romano, J.R. (2005) Testıng Statıstıcal Hypotheses, Springer Science?Business Media, LLC, ISBN 0-387-98864-5, Third Edition 2. Öztürk, F., Akdi, Y., Aydoğdu, H. ve Karabulut, İ. (2006), Parametre tahmini ve hipotez testi, Bıçaklar Kitabevi.  1. Casella, G. and Berger, R.L. (2002). Statistical Inference. Duxbury, Second Edition. 2. Miller, I and Miller, M. (2004). John E. Fredund’s Mathematical Statistics with Applications , Pearson Prentice Hall, Seventh Edition. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 90     Homeworks/Projects/Others 3 10 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 1 2 Apply the statistical analyze methods 5 3 Make statistical inference(estimation, hypothesis tests etc.) 5 4 Generate solutions for the problems in other disciplines by using statistical techniques 4 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 5 8 Be aware of the interaction between the disciplines related to statistics 2 9 Make oral and visual presentation for the results of statistical methods 2 10 Have capability on effective and productive work in a group and individually 0 11 Develop scientific and ethical values in the fields of statistics-and scientific data collection 0 12 Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 5 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 5 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 0 * 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 1 14 Assesment Related Works     Homeworks, Projects, Others 3 10 30     Mid-term Exams (Written, Oral, etc.) 1 15 15     Final Exam 1 25 25 Total Workload: 126 Total Workload / 25 (h): 5.04 ECTS Credit: 5