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CEPHEI: MEF-502
Statistics for Engineering

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Statistics for Engineering
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This course aims to provide information that is useful for conducting analysis for students and graduates. This course is especially suitable for learners of engineering branches, but also for learners who want to acquire knowledge and skills on statistics used in engineering. With this course, you will draw attention with the keystone analysis information used in engineering. Below, you will find some basic information about the course.
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About This Course

Dear Students!

All of you welcome to the "Statistics for Engineering" course! This course aims to provide information that is useful for conducting analysis for students and graduates. This course is especially suitable for learners of engineering branches, but also for learners who want to acquire knowledge and skills on statistics used in engineering. With this course, you will draw attention with the keystone analysis information used in engineering. Below, you will find some basic information about the course.

Regards,

Ahmet Serdar Tan and Şuayb Ş.

Learning Outcomes

Students who successfully complete the course are expected to be able to:

LO1. Understand and implement basic concepts such as population and parameter as well as various techniques for illustration such as statistics table and box-plots.

LO2. Forecasting and estimating an unknown parameter of the population or a product. Assess different types of estimators, biasedness, mean square error metric for determining how good an estimator is.

LO3. Formulation of a hypothesis and measuring its correctness through specific testing procedures and detect hyothesis errors.

LO4. Apply p-value approach to testing, design and outline a procedure for the right hypothesis testing based on sample size, population distributions and its known or unknown parameters. Analyze two-tail left or right-tailed tests on a given sample case.

LO5. Identify basic linear regression model and then the estimation of parameters of a model in the sense of mean square error. Understrand and execute concepts of correlation and coefficients of variation and determination.

Requirements

BSc degree is required in the field of engineering, but there is no definite prerequisite for the course.

Course Staff

Course Staff Image #1

Ahmet Serdar Tan

Ahmet Serdar Tan received his bachelor's degree from Middle East Technical University Electrical and Electronics Engineering in 2003 and his Ph.D. degree in telecommunication from Bilkent University Electrical and Electronics Engineering in 2009. After receiving his PhD degree, he worked as a post-doctoral researcher on fiber optic communication in Chalmers University, Sweden between 2009 and 2010. In 2011, Tan started working as a team leader in Türk Telekom R/D department and worked as a researcher and executive in EU / TUBITAK research projects. Dr. Tan is the founder of the R/D company Gradus Technologies working on wireless communication technologies and holds the Patent Attorney degree of the Turkish Patent Institute. He worked as an Assistant Professor in Electrical and Electronics Engineering at MEF University between 2014-2020.

Course Staff Image #2

Şuayb Ş. Arslan

Şuayb Ş. Arslan received his bachelor's degree from Istanbul Bogaziçi University in 2006 with high honor. He then completed his master's degrees in the United States (USA) University of California, San Diego (UCSD) in 2009 and 2012. He also worked on "data segmentation and tracking" at the MERL research institute in Boston in the summer of 2009. Between 2011 and 2015, he conducted studies on signal processing for tape and disk systems in the R/D department of Quantum Corporation-USA. Meanwhile, he had the opportunity to work with Bristol teams on the next generation of high capacity tape formats and systems. He had the opportunity to work at the Polytech IRCCyN Research Institute at the University of Nantes in France, as a short-term invited academic, at different times of the academic year 2015-2016. His research interests are signal processing and coding techniques for data storage and communication, software based cloud storage system designs and distributed parallel architectures. Graduate research topics include coding, wireless communication techniques, and efficient image and video source coding and transmission for data storage and communication. Şuayb Ş. Arslan is a member of international research organizations IEEE and Sigma Xi.

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Content and Structure of the Course

The course includes the following modules:

Module 1. Introduction to Statistics and Sampling Distributions

Module 2. Estimation

Module 3. Hypothesis Testing I

Module 3. Hypothesis Testing II

Module 5. Linear Regression

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