Syllabus ( MATH 401 )
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Basic information
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| Course title: |
Mathematical Statistics |
| Course code: |
MATH 401 |
| Lecturer: |
Prof. Dr. Nuri ÇELİK
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| ECTS credits: |
6 |
| GTU credits: |
3 (3+0+0) |
| Year, Semester: |
4, Fall |
| Level of course: |
First Cycle (Undergraduate) |
| Type of course: |
Elective
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| Language of instruction: |
English
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| Mode of delivery: |
Face to face
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| Pre- and co-requisites: |
Math 308 |
| Professional practice: |
No |
| Purpose of the course: |
To establish a solid foundation in mathematical statistics and to equip students with the knowledge and skills to apply probability theory to problems in mathematical statistics. |
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Learning outcomes
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Upon successful completion of this course, students will be able to:
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construct a solid background and demostrate the basic results and methods in mathematical statistics and related subjects.
Contribution to Program Outcomes
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Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
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Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
Method of assessment
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Written exam
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explain the meaning of statistical models, hypotesis testing and its main principles.
Contribution to Program Outcomes
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Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
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Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
Method of assessment
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Written exam
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Learn to do point and interval estimations.
Contribution to Program Outcomes
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Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
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Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
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Ability to work in interdisciplinary research teams effectively.
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Exhibiting professional and ethical responsibility.
Method of assessment
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Written exam
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Contents
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| Week 1: |
Introduction and motivation |
| Week 2: |
Statistical graphics. Descriptive graphics. |
| Week 3: |
Measures of center and variability. |
| Week 4: |
Basic probability. |
| Week 5: |
Discrete random variables. |
| Week 6: |
Continuous random variables. |
| Week 7: |
Multivariate probablity distributions. Midterm Exam. |
| Week 8: |
Functions of Random Variables |
| Week 9: |
Sampling distributions. |
| Week 10: |
Central Limit Theorem. |
| Week 11: |
Estimation. |
| Week 12: |
Properties of point estimators and the method of estimation. |
| Week 13: |
Hypothesis Testing: One-Sample |
| Week 14: |
Hypothesis Testing: Multi Samples |
| Week 15*: |
- |
| Week 16*: |
Final Exam. |
| Textbooks and materials: |
Wackerly, D., Mendenhall, W., & Scheaffer, R. (2007). Mathematical statistics with applications. Nelson Education.7nd ed. |
| Recommended readings: |
Mendenhall, Beaver and Beaver, Introduction to Probability and Statistics, Cengage Learning |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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Assessment
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| Method of assessment |
Week number |
Weight (%) |
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| Mid-terms: |
7 |
40 |
| Other in-term studies: |
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0 |
| Project: |
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0 |
| Homework: |
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0 |
| Quiz: |
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0 |
| Final exam: |
16 |
60 |
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Total weight: |
(%) |
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Workload
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| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
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| Courses (Face-to-face teaching): |
3 |
14 |
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| Own studies outside class: |
5 |
14 |
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| Practice, Recitation: |
0 |
0 |
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| Homework: |
0 |
0 |
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| Term project: |
0 |
0 |
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| Term project presentation: |
0 |
0 |
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| Quiz: |
0 |
0 |
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| Own study for mid-term exam: |
10 |
1 |
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| Mid-term: |
2 |
1 |
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| Personal studies for final exam: |
20 |
1 |
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| Final exam: |
2 |
1 |
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Total workload: |
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Total ECTS credits: |
* |
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* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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