STAT304 MATHEMATICAL STATISTICS II
| Course Code: | 2460304 |
| METU Credit (Theoretical-Laboratory hours/week): | 4 (3.00 - 2.00) |
| ECTS Credit: | 8.0 |
| Department: | Statistics |
| Language of Instruction: | English |
| Level of Study: | Undergraduate |
| Course Coordinator: | |
| Offered Semester: | Spring Semesters. |
Course Objectives
This course will place heavy emphasis on theoretical and conceptual aspects of Mathematical Statistics. Lectures will explain the theoretical origins and practical implications of statistical formulae.
Course Content
Region (interval) estimation. Hypathesis testing. Optimality properties for hypothesis testing. Likelihood ratio tests. Sequential tests.
Course Learning Outcomes
Knowledge:
1. Basic theoretical knowledge about fundamental principles for statistical inference
2. knowledge about construction of interval estimators, and hypothesis testing;
3. The evaluation of these estimators and tests.
4. Insight in how to construct optimal estimators and tests.
5. Difference between Frequentist and Bayesian inference.
6. Knowledge of obtaining Bayesian estimators.
Skills:
1. Ability to develop theoretical arguments..
2. Obtaining a deeper understanding and a considerable extension to the statistical inference theory in the bachelor courses.
3.Ability to perform point estimation, hypothesis testing and interval estimation under a large variety of discrete and continuous probability models.
4. Ability to evaluate the properties of these estimators and tests, for both finite sample sizes and
asymptotically as the sample size tends to infinity.
5. Ability to differentiate frequentisty and Bayesian inference.
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | Applying the knowledge of statistics, mathematics and computer to statistical problems and developing analytical solutions. | ✔ | |||
| 2 | Defining, modeling and solving real life problems that involve uncertainty, and interpreting results. | ✔ | |||
| 3 | To decide on the data collection technique, and apply it through experiment, observation, questionnaire or simulation. | ✔ | |||
| 4 | Analysing small and big volumes of data and interpreting results. | ✔ | |||
| 5 | Utilizing up-to-date techniques, computer hardware and software required for statistical applications; developing software programs and numerical solutions for specific problems when necessary. | ✔ | |||
| 6 | Taking part in intradisciplinary and interdisciplinary teamwork, using time efficiently, taking leadership responsibilities and being entrepreneurial. | ✔ | |||
| 7 | Taking responsibility in individual work and offering authentic solutions. | ✔ | |||
| 8 | Following contemporary developments and publications in statistical science, conducting research, being open to novelty and thinking critically. | ✔ | |||
| 9 | Efficiently communicating in Turkish and English to define and analyze statistical problems and to interpret the results. | ✔ | |||
| 10 | Having a professional and ethical sense of responsibility. | ✔ | |||
| 11 | Developing computational solutions to statistical problems that cannot be solved analytically. | ✔ | |||
| 12 | Having theoretical background and developing new theories in statistics, building relations between theoretical and practical knowledge. | ✔ | |||
| 13 | Serving the society with the expertise in the field. | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution