INTERNATIONAL BURCH UNIVERSITY
Faculty of Engineering and Natural Sciences
Department of Information Technologies
20172018
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,
 Midterm 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,
 Midterm 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 problemsolving 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 
