EMÜ738 - MATHEMATICAL STATISTICS FOR ENGINEERS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| MATHEMATICAL STATISTICS FOR ENGINEERS | EMÜ738 | Any Semester/Year | 3 | 0 | 3 | 10 |
| Prequisites | ||||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Problem Solving Project Design/Management Other: Lecture, question and answer, problem solving, homeworks, project, individual study. | |||||
| Instructor (s) | To be determined by the department | |||||
| Course objective | The objective of this course is to introduce statistical distributions, to learn basic concepts of statistical inference, and to develop a theoretical understanding of statistical estimation and tests of hypotheses. | |||||
| Learning outcomes |
| |||||
| Course Content | Moments, moment generating functions Law of large numbers, DeMoivre-Laplace limit theorem, central limit theorem Discrete and continuous distributions Order Statistics Point estimation methods and properties of estimations Tests of hypotheses | |||||
| References | Larsen, R.J. ve Marx, M.L. (2017). Introduction to Mathematical Statistics and Its Applications (5th Edition), Pearson. Miller, I. ve Miller, M. (2006). John Freud?dan Matematiksel İstatistik, Altıncı Basımdan çeviri (Ed: Ümit Şenesen), Literatür Yayıncılık. Erdem, İ. (2012). Matematiksel İstatistik, Seçkin Yayıncılık. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Moments, moment generating functions |
| Week 2 | Discrete and continuous distributions |
| Week 3 | Discrete and continuous distributions |
| Week 4 | Order Statistics |
| Week 5 | Order Statistics |
| Week 6 | Point estimation methods |
| Week 7 | Properties of estimations, Interval estimation |
| Week 8 | MIDTERM |
| Week 9 | Tests of hypotheses (Test statistic, critical region, critical value, P-value, type I and type II error, level of significance, level of confidence, power of a test) |
| Week 10 | Tests of hypotheses (Test statistic, critical region, critical value, P-value, type I and type II error, level of significance, level of confidence, power of a test) |
| Week 11 | Large sample test for the binomial parameter p |
| Week 12 | Normal olmayan veriler için karar kuralları, Neyman-Pearson teoremi, genelleştirilmiş en çok olabilirlik |
| Week 13 | Chi square distribution, t distribution, F distribution |
| Week 14 | Goodness of fit |
| Week 15 | Study for the Final Exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 5 | 25 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 25 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 6 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 1 | 13 | 13 |
| Project | 1 | 40 | 40 |
| Homework assignment | 5 | 7 | 35 |
| Midterms (Study duration) | 1 | 25 | 25 |
| Final Exam (Study duration) | 1 | 40 | 40 |
| Total Workload | 37 | 133 | 265 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Reach the necessary knowledge and methods required within the scope of industrial engineering through scientific research. Utilize these knowledge and methods upon evaluation and synthesis and implement them | X | ||||
| 2. Follow the innovations, developments and literature on an international basis in the field of industrial engineering; have the competency to convert the research activities into scientific national and international publications and to contribute to the national and international science and technology literature. | X | ||||
| 3. Perform a comprehesive analysis of the decision making problems; with a critical view evaluate the operations research and data based methodologies to model and solve these problems; implement after the synthesis or the development of these methods. | X | ||||
| 4. Perceive independently, design, plan, manage, monitor and conclude the research and development study process in the field of industrial engineering. | X | ||||
| 5. Are aware of the academic responsbilities; describe the scientific, technological, economic, social, environmental and cultural impacts of the applications of Industrial Engineering; based on necessity, work individually or as a team member taking the scientific and institutional ethical values. | X | ||||
| 6. Evaluate critically, report and present the results of the advanced research stuies and projects carried out in the field of industrial engineering | X | ||||
| 7. Have the competency of the advanced use of software and information technologies required for Industrial Engineering | X | ||||
| 8. Design, model, develop and improve large scale systems. | X | ||||
| 9. Raise the awareness of the decision makers through public quotation of the scientific, technological, social and cultural developments in the field of Industrial Engineering with a sense of scientific impartiality. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest