STAT 501 | Course Introduction and Application Information - Applied Mathematics and Statistics – With Thesis

Course Name

Theory of Statistics

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
STAT 501	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	Problem Solving Q&A
National Occupation Classification	-
Course Coordinator	Prof. Dr. İsmihan BAYRAMOĞLU
Course Lecturer(s)	Prof. Dr. İsmihan BAYRAMOĞLU
Assistant(s)	Araş. Gör. Merve KAHRAMAN ARİMAN

Course Objectives	This course aims to provide basic theory and applications of mathematical statistics.
Learning Outcomes	The students who succeeded in this course; will be able to work with order statistics. will be able to find moment estimators and maximum likelihood estimators. will be able to identify the best unbiased estimators. will be able to suggest the test. will be able to calculate confidence intervals.
Course Description	The topics covered in this course include basic notations and definitions of statistics, data reduction, point estimation, methods of evaluating estimators, and hypothesis testing.
Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation
1	Introduction to basic concepts of statistics. Sampling from infinite population, simple random sample, stratified sample, cluster sampling, systematic sample.	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 5.1.
2	Properties of a random sample.	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 5.2.
3	Sampling from the Normal distribution.	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 5.3.
4	Order statistics.	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 5.4.
5	Principles of Data Reduction	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 6.
6	Point Estimation	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 7.1.
7	Maximum Likelihood Estimators	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 7.2.
8	Regression Analysis: Maximum Likelihood Estimators of Regression Parameters.	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 11.3 & 12.2.3.
9	Midterm
10	Methods of Evaluating Estimators	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 7.3.
11	Consistency of Estimators	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 7.3.
12	Nonparametric Estimation, hypothesis testing	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 8.
13	Nonparametric Estimation, hypothesis testing	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 8.
14	Likelihood ratio test	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128. section 8.2.1.
15	Semester Review
16	Final Exam

Course Notes/Textbooks	“Statistical Inference” by G. Casella, R. L. Berger, Duxbury Press, 2nd Edition, 2001. ISBN-13:9780534243128.
Suggested Readings/Materials	“Mathematical Statistics” by J. Shao, Springer, 2nd Edition, 2003. ISBN-13: 978-0387953823

Semester Activities	Number	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques	-	-
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm	1	40
Final Exam	1	60
Total

Weighting of Semester Activities on the Final Grade	1	40
Weighting of End-of-Semester Activities on the Final Grade	1	60
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	6	84
Field Work			0
Quizzes / Studio Critiques	-	-	0
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	37	37
Final Exam	1	56	56
		Total	225

#

Program Competencies/Outcomes

* Contribution Level

1

2

3

4

5

1

To be able to demonstrate independent and critical thinking in Applied Mathematics and Statistics.

-

X

2

To be able to define problems in Applied Mathematics/Statistics and verify whether they are mathematically/statistically consistent.

-

X

-

3

To be able to analyse and solve real life problems using applied methods and interdisciplinary approach of Mathematics/Statistics.

-

4

To be able to independently conduct, conclude, and report on specialized research in Applied Mathematics and Statistics.

-

X

-

5

To be able to efficiently use national and international resources, for staying updated in the field, communicating with colleagues, and following the related literature.

-

X

-

6

To be able to develop proficiency in using computer software widely utilized in the fields of Applied Mathematics and Statistics.

-

7

To be able to evaluate solution processes efficiently using mathematical reasoning and modeling in order to contribute to the solutions of social and scientific problems.

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8

To be able to synthesize theoretical frameworks with practical applications through mathematical and statistical methods.

-

9

To be able to develop strategies, policies and plans for problems and research areas in Applied Mathematics/Statistics in order to interpret the results and translate them into practice.

-

X

-

10

To be able to translate key topics, events, and phenomena in Applied Mathematics and Statistics into the context of other scientific disciplines.

-

11

To be able to engage in lifelong learning by continuously updating and improving knowledge and skills in Applied Mathematics and Statistics.

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STAT 501 | Course Introduction and Application Information

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

EVALUATION SYSTEM

ECTS / WORKLOAD TABLE

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

IZMIR UNIVERSITY OF ECONOMICS GÜZELBAHÇE CAMPUS

GLOBAL CAREER

CONTRIBUTION TO SCIENCE

VALUING PEOPLE

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