| Course Title | Code | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|
| Probability and Statistics | BMM 203 | 3. Semester | 3 + 0 | 3.0 | 4.0 |
| Prerequisites | None |
| Language of Instruction | Turkish |
| Course Level | Undergraduate |
| Course Type | |
| Mode of delivery | Face-to-Face Education |
| Course Coordinator |
Assist. Prof. Dr. Yaşar ŞEN |
| Instructors |
Yaşar ŞEN |
| Assistants | |
| Goals | To provide an understanding of randomness, to give some concepts of probability theory and to create a probability language for introduction to statistical theory. |
| Course Content | Some tools for modeling problems involving randomness and discrete probability distributions and their applications. |
| Learning Outcomes |
- Explains and applies basic methods of counting. - Explains binomial theory. - Explains concepts and ideas related to probability. - Solves problems related to conditional probability using Bayes theorem. - Explains probability distributions and their properties using computer simulations. - Uses information and communication technologies in modeling probability situations. - Demonstrates a positive attitude towards probability. - Appreciates the importance of probability knowledge in real life. |
| Week | Topics | Learning Methods |
|---|---|---|
| 1. Week | History of Probability, Introduction to Probability. Counting Methods, | Course Hours |
| 2. Week | Probability Axioms, Conditional Probability, Independent Events | Course Hours |
| 3. Week | Random Variables, Joint Probability Distributions | Course Hours |
| 4. Week | Mathematical Expextation of random variable | Course Hours |
| 5. Week | Variance and Covariance of Random Variables | Course Hours |
| 6. Week | Means and Variances of Linear Combinations of Random Variables | Course Hours |
| 7. Week | Moments and Moment-Generating Functions | Course Hours |
| 8. Week | Chebyshev's Inequality and the Law of Large Numbers | |
| 9. Week | Midterm exam, and Chebyshev's Inequality and the Law of Large Numbers | Course Hours |
| 10. Week | Important Discrete Distribution Functions | Course Hours |
| 11. Week | Hypergeometric Distribution | Course Hours |
| 12. Week | Poisson Distribution | Course Hours |
| 13. Week | Continuous Uniform Distribution | |
| 14. Week | Normal Distribution, Sampling Theory | Course Hours |
| Probabilty and Statistic, Morris H. DeGroot, 1986. • Applied Probabilty and Statistic, Mario Lefebvre, 2006. • A Modern Introduction to Probability and Statistics, Frederik Michel Dekking, Cornelis Kraaikamp, Hendrik Paul Lopuha¨a, Ludolf Erwin Meester, 2005. • A course in Probability and Statistics, Charles J. Stone, 1996. • INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS, Sheldon M. Ross, 2004. • Probability & Statistics for Engineers & Scientists, Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, 2012. • Theory and Problems of Probability and Statistics, Murray R. Spiegel, 1998. • Teori ve Problemlerle Olasılık, Seymour Lipschutz, Schaum Serisi, 1974. 1email |
| Larson, H. J. (1982). Introduction to Probability Theory and Statistical Inference, John Wiley&Sons. 2. Akdeniz, F. (2007). Olasılık ve İstatistik, Nobel Kitabevi. 3. Öztürk, F. (1993). Matematiksel İstatistik, Ankara Üniversitesi Fen Fakültesi Yayınları, No.10. |
| Program Requirements | Contribution Level | DK1 | DK2 | DK3 | DK4 | DK5 | DK6 | DK7 | DK8 | Measurement Method |
|---|
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| Course's Level of contribution | None | Very Low | Low | Fair | High | Very High |
| Method of assessment/evaluation | Written exam | Oral Exams | Assignment/Project | Laboratory work | Presentation/Seminar |
| Event | Quantity | Duration (Hour) | Total Workload (Hour) |
|---|---|---|---|
| Course Hours | 14 | 3 | 42 |
| Midterm 1 | 1 | 1 | 1 |
| Practice End-Of-Term | 1 | 1 | 1 |
| Total Workload | 44 | ||
| ECTS Credit of the Course | 4.0 | ||
