Syllabus ( MATH 401 )
|
Basic information
|
|
Course title: |
Mathematical Statistics |
Course code: |
MATH 401 |
Lecturer: |
Prof. Dr. Nuri ÇELİK
|
ECTS credits: |
6 |
GTU credits: |
3 (3+0+0) |
Year, Semester: |
4, Fall |
Level of course: |
First Cycle (Undergraduate) |
Type of course: |
Elective
|
Language of instruction: |
English
|
Mode of delivery: |
Face to face
|
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. |
|
|
|
Learning outcomes
|
|
Upon successful completion of this course, students will be able to:
-
construct a solid background and demostrate the basic results and methods in mathematical statistics and related subjects.
Contribution to Program Outcomes
-
Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
-
Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
Method of assessment
-
Written exam
-
explain the meaning of statistical models, hypotesis testing and its main principles.
Contribution to Program Outcomes
-
Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
-
Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
Method of assessment
-
Written exam
-
Learn to do point and interval estimations.
Contribution to Program Outcomes
-
Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
-
Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
-
Ability to work in interdisciplinary research teams effectively.
-
Exhibiting professional and ethical responsibility.
Method of assessment
-
Written exam
|
|
Contents
|
|
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 |
|
* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
|
|
|
|
Assessment
|
|
|
Method of assessment |
Week number |
Weight (%) |
|
Mid-terms: |
7 |
40 |
Other in-term studies: |
|
0 |
Project: |
|
0 |
Homework: |
|
0 |
Quiz: |
|
0 |
Final exam: |
16 |
60 |
|
Total weight: |
(%) |
|
|
|
Workload
|
|
|
Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
|
Courses (Face-to-face teaching): |
3 |
14 |
|
Own studies outside class: |
5 |
14 |
|
Practice, Recitation: |
0 |
0 |
|
Homework: |
0 |
0 |
|
Term project: |
0 |
0 |
|
Term project presentation: |
0 |
0 |
|
Quiz: |
0 |
0 |
|
Own study for mid-term exam: |
10 |
1 |
|
Mid-term: |
2 |
1 |
|
Personal studies for final exam: |
20 |
1 |
|
Final exam: |
2 |
1 |
|
|
|
Total workload: |
|
|
|
Total ECTS credits: |
* |
|
* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)
|
|
|
-->