Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

Description of Individual Course Units

Course Unit CodeCourse Unit TitleType of CourseYearSemesterECTS
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
Recommended Optional Programme Components
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Language of Instruction
Work Placement(s)
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
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight (%)
Midterm Examination10
Makeup Examination10
Problem Solving810
End Of Term (or Year) Learning ActivitiesQuantityWeight (%)
Final Examination1100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
Workload Calculation
ActivitiesQuantityTime(hours)Total Workload(hours)
Midterm Examination111
Final Examination111
Makeup Examination111
Attending Lectures14342
Problem Solving6530