Syllabus Application
ENS 505
Methods of Statistical Inference
Faculty
Faculty of Engineering and Natural Sciences
Semester
Spring 2025-2026
Course
ENS 505 -
Methods of Statistical Inference
Time/Place
Time
Week Day
Place
Date
15:40-17:30
Wed
FENS-L027
Feb 16-May 22, 2026
10:40-11:30
Thu
FENS-L047
Feb 16-May 22, 2026
Level of course
Masters
Course Credits
SU Credit:3, ECTS:10
Prerequisites
-
Corequisites
-
Course Type
Lecture
Instructor(s) Information
Sinan Yıldırım
Course Information
Catalog Course Description
The main objective of this course is to review the basic concepts of the theory of statistics and further develop an understanding of some fundamental applied statistical methods. The emphasis is on applications of the theory in the development of statistical procedures. Some examples of applying statistics to engineering problems are also given. Theory- and computation-based assignments help students digest the concepts and apply them in practice. Covered topics: Fundamental concepts of statistics and related distributions; design of experiments and analysis of variance; multiple hypotheses testing; regression and correlation analysis; Bayesian statistics; computer-aided analysis of data.
Course Learning Outcomes:
| 1. | Describe the types of statistics: descriptive statistics, parametric inferential statistics and non-parametric statistics |
|---|---|
| 2. | Obtain descriptive statistics and employ the basic graphical visualization techniques to summarize and analyze the data |
| 3. | Describe the general properties of estimators: biasedness, mean square error, consistency and efficiency |
| 4. | Determine the point estimators of unknown parameters of interest based on three widely-applied methods: maximum likelihood estimation, the method of moments and Bayes estimation |
| 5. | Derive the confidence interval estimators of unknown parameters of interest based on three approaches: exact methods, approximations based on the large sample properties and approximations using bootstrapping |
| 6. | Discuss the basic principals of the methods of hypothesis testing |
| 7. | Identify key points in Diagnostics and Remedial Measures for the regression analysis |
| 8. | Perform simple and multiple regression analyses by the help of a software such as SPSS and MATLAB. |
Course Objective
The main objective of this course is to review the basic concepts of the theory of statistics
and further develop an understanding of some fundamental applied statistical methods. Our
emphasis will be on applications of the theory in the development of statistical procedures.
Practical applications of statistics to some problems in engineering and management will
be given. Computational assignments will be given to help the students to understand the
concepts and to have an opportunity to practice applying them.
and further develop an understanding of some fundamental applied statistical methods. Our
emphasis will be on applications of the theory in the development of statistical procedures.
Practical applications of statistics to some problems in engineering and management will
be given. Computational assignments will be given to help the students to understand the
concepts and to have an opportunity to practice applying them.
Sustainable Development Goals (SDGs) Related to This Course:
| Decent Work and Economic Growth |
Course Materials
Resources:
There is no specific reference text for the course. The material will develop with lecture
notes and assignments. However, you are encouraged to read textbooks as well as useful
tutorials available on the internet. Below are some of them.
1. Mathematical Statistics and Data Analysis (with CD Data Sets) (3rd Edition), John A. Rice.
2. Probability and statistics in engineering and management science, William W. Hines and Douglas C. Montgomery.
3. Principles of Statistical Inference (1st Edition), David R. Cox.
4. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science, Efron, B., & Hastie, T. (2016).
5. Applied Linear Statistical Models (4th Edition), John Neter, Michael H. Kutner, Christopher J. Nachtsheim and W. Wasserman.
6. Applied Multivariate Statistical Analysis, R.A. Johnson, D.W. Wichern.
7. A Second Course in Statistics: Regression Analysis (6th Edition), W. Mendenhall, T. Sincich.
8. The Elements of Statistical Learning: data mining, inference and prediction, T. Hastie, R. Tibshirani, and J. Friedman,
9. Bayesian Data Analysis, (3rd Edition), Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin
10. Monte Carlo Statistical Methods (2nd Edition), Robert, C. P. and Casella, G. (2004),
11. Monte Carlo: Simulation Methods for Statistical Inference, Sinan Yıldırım, lecture notes, 2017
notes and assignments. However, you are encouraged to read textbooks as well as useful
tutorials available on the internet. Below are some of them.
1. Mathematical Statistics and Data Analysis (with CD Data Sets) (3rd Edition), John A. Rice.
2. Probability and statistics in engineering and management science, William W. Hines and Douglas C. Montgomery.
3. Principles of Statistical Inference (1st Edition), David R. Cox.
4. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science, Efron, B., & Hastie, T. (2016).
5. Applied Linear Statistical Models (4th Edition), John Neter, Michael H. Kutner, Christopher J. Nachtsheim and W. Wasserman.
6. Applied Multivariate Statistical Analysis, R.A. Johnson, D.W. Wichern.
7. A Second Course in Statistics: Regression Analysis (6th Edition), W. Mendenhall, T. Sincich.
8. The Elements of Statistical Learning: data mining, inference and prediction, T. Hastie, R. Tibshirani, and J. Friedman,
9. Bayesian Data Analysis, (3rd Edition), Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin
10. Monte Carlo Statistical Methods (2nd Edition), Robert, C. P. and Casella, G. (2004),
11. Monte Carlo: Simulation Methods for Statistical Inference, Sinan Yıldırım, lecture notes, 2017
Technology Requirements:
You are encouraged to learn and/or use a programming language to apply the methods taught in the course.