Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

# Description of Individual Course Units

 Course Unit Code Course Unit Title Type of Course Year Semester ECTS BİS601 Statistical Inference 927001 1 2 6
Level of Course Unit
Second Cycle
Objectives of the Course
The theory of statistics deals in principle with the general concepts underlying all aspects of suchwork and from this perspective the formal theory of statistical inference is but a part of that full theory. Much of the theory is concerned with indicating the uncertainty involved in the conclusions of statistical analyses, and with assessing the relative merits of different methods of analysis, and it is important even at a very applied level to have some understanding of the strengths and limitations of such discussions.
Name of Lecturer(s)
Learning Outcomes
1. Distribution Functions and Probability Functions, Some Important Discrete Random variables, Some Important Continuous Random Variables
2. Expectation, Properties of Expectations, Moment Generating Functions, Checking Assumptions
3. Fundamental Concepts in Inference . Point Estimation, Confidence Sets, The Method of Moments, Maximum Likelihood . Properties of Maximum Likelihood Estimators
4. The Bayesian Method, Bayes Estimators, Bayesian Testing, Bootstrap Variance Estimation
Mode of Delivery
Formal Education
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Language of Instruction
Work Placement(s)
None
Course Contents
Weekly Detailed Course Contents
 Week Theoretical Practice Laboratory 1. Role of formal theory of inference 2. Formulation of objectives, Point estimation 3. Two broad approaches to statistical inference 4. Some concepts and simple applications, Likelihood, Sufficiency, Exponential family, Simple frequentist discussion 5. Significance tests, Simple significance test, One- and two-sided tests, Relation with acceptance and rejection, 6. Formulation of alternatives and test statistics, Relation with interval estimation, Interpretation of significance tests 7. Some more general frequentist developments 8. Bayesian testing, Personalistic probability 9. Statistical implementation of Bayesian analysis 10. Asymptotic theory, Multidimensional parameter, Nuisance parameters 11. Aspects of maximum likelihood, information matrix 12. Modified likelihoods 13. Some applications 14. Some applications 15.
Assessment Methods and Criteria
 Term (or Year) Learning Activities Quantity Weight (%) Midterm Examination 1 0 Makeup Examination 1 0 Practice 14 60 Tutorial 5 10 Problem Solving 8 10 Discussion 6 10 Question-Answer 6 5 Homework 2 5 SUM 1070 End Of Term (or Year) Learning Activities Quantity Weight (%) Final Examination 1 100 SUM 100 Term (or Year) Learning Activities 40 End Of Term (or Year) Learning Activities 60 SUM 100
Workload Calculation
 Activities Quantity Time(hours) Total Workload(hours) Midterm Examination 1 1 1 Final Examination 1 1 1 Makeup Examination 1 1 1 Attending Lectures 14 3 42 Practice 14 3 42 Problem Solving 6 5 30 Discussion 6 4 24 Homework 2 5 10 SUM 151