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 |
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 |
- Introduction Set Theory
- Applying Set Theory to Probability
- Axioms
- Tree Diagrams
- Discrete Random Variables
- Probability Mass Function, Families of DRV
- Cumulative Distribution Function Averages,
- Mid-term Examination
- Variance and Standard Deviation
- Continuous Random Variables
- Probability Density Function
- Families of CRV
- Gaussian RV
- Delta Functions, Mixed RV
- Prob. Models of Derived RV
|
LABORATORY/PRACTICE PLAN |
- Introduction, Set Theory
- Applying Set Theory to Probability
- Axioms
- Tree Diagrams
- Discrete Random Variables
- Probability Mass Function, Families of DRV
- Cumulative Distribution Function Averages,
- Mid-term Examination
|
- Variance and Standard Deviation
- Continuous Random Variables
- Probability Density Function
- Families of CRV
- Gaussian RV
- Delta Functions, Mixed RV
- Prob. Models of Derived RV
|
Description |
- Interactive Lectures
- Practical Sessions
|
Description (%) |
Quiz | 1 | 15 | Midterm Exam(s) | 1 | 30 | Lab/Practical Exam(s) | 1 | 25 |
|
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 |
Lecture (14 weeks x Lecture hours per week) | 14 | 2 | 28 | Laboratory / Practice (14 weeks x Laboratory/Practice hours per week) | 14 | 2 | 28 | Midterm Examination (1 week) | 1 | 2 | 2 | Final Examination(1 week) | 1 | 2 | 2 | Preparation for Midterm Examination | 1 | 20 | 20 | Preparation for Final Examination | 1 | 20 | 20 | Assignment / Homework/ Project | 2 | 20 | 40 | Seminar / Presentation | | | 0 |
|