INTERNATIONAL BURCH UNIVERSITY
Faculty of Engineering and Natural Sciences
Department of Information Technologies
2017-2018

SYLLABUS
Code Name Level Year Semester
MTH 205 Probability and Statistics for Engineers Undergraduate 3 Spring
Status Number of ECTS Credits Class Hours Per Week Total Hours Per Semester Language
5 140 English

Instructor Assistant Coordinator
Nejdet Dogru, Assoc. Prof. Dr. Nejdet Dogru Nejdet Dogru, Assoc. Prof. Dr.
[email protected] [email protected] no email

This course on uncertainty in engineering analysis can also be referred to as probability and statistics for engineers. In particular, we will deal with the applications of probability and statistics.

COURSE OBJECTIVE
The objective of this course is to give students understanding of following topics: the role of statistics in engineering, probability, discrete random variables and probability distributions, continuous random variables and probability distributions, joint probability distributions, random sampling and data description, point estimation of parameters, statistical intervals for a single sample, and tests of hypotheses for a single sample.

COURSE CONTENT
Week
Topic
  1. Introduction Set Theory
  2. Applying Set Theory to Probability
  3. Axioms
  4. Tree Diagrams
  5. Discrete Random Variables
  6. Probability Mass Function, Families of DRV
  7. Cumulative Distribution Function Averages,
  8. Mid-term Examination
  9. Variance and Standard Deviation
  10. Continuous Random Variables
  11. Probability Density Function
  12. Families of CRV
  13. Gaussian RV
  14. Delta Functions, Mixed RV
  15. Prob. Models of Derived RV

LABORATORY/PRACTICE PLAN
Week
Topic
  1. Introduction, Set Theory
  2. Applying Set Theory to Probability
  3. Axioms
  4. Tree Diagrams
  5. Discrete Random Variables
  6. Probability Mass Function, Families of DRV
  7. Cumulative Distribution Function Averages,
  8. Mid-term Examination

  1. Variance and Standard Deviation
  2. Continuous Random Variables
  3. Probability Density Function
  4. Families of CRV
  5. Gaussian RV
  6. Delta Functions, Mixed RV
  7. Prob. Models of Derived RV

TEACHING/ASSESSMENT
Description
  • Interactive Lectures
  • Practical Sessions
Description (%)
Method Quantity Percentage (%)
Quiz115
Midterm Exam(s)130
Lab/Practical Exam(s)125
Total: 70
Learning outcomes
  • Apply statistical methodology and tools in the engineering problem-solving process.
  • Compute and interpret descriptive statistics using numerical and graphical techniques.
  • Show a capacity for investigation and experimentation into physical (engineering) phenomena.
  • Compute point estimation of parameters, explain sampling distributions, and understand the central limit theorem.
  • Construct confidence intervals on parameters for a single sample.
TEXTBOOK(S)
  • R.D. Yates and D. J. Goodman, Probability and Stochastic Processes- A Friendly Introduction for Electrical and Computer Engineers, John Wiley & Sons, Inc., 2005 (2/e)

ECTS (Allocated based on student) WORKLOAD
Activities Quantity Duration (Hour) Total Work Load
Lecture (14 weeks x Lecture hours per week)14228
Laboratory / Practice (14 weeks x Laboratory/Practice hours per week)14228
Midterm Examination (1 week)122
Final Examination(1 week)122
Preparation for Midterm Examination12020
Preparation for Final Examination12020
Assignment / Homework/ Project22040
Seminar / Presentation 0
Total Workload: 140
ECTS Credit (Total workload/25): 6