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 corequisites: 
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 reallife 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 reallife 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 reallife 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: OneSample 
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 (%) 

Midterms: 
7 
40 
Other interm 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 (Facetoface 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 midterm exam: 
10 
1 

Midterm: 
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)



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