INTERNATIONAL BURCH UNIVERSITY
Faculty of Engineering and Natural Sciences
Department of Information Technologies
20152016
SYLLABUS 
Code 
Name 
Level 
Year 
Semester 
MTH 104 
Probability and Statistics for Engineers 
Undergraduate 
1 
Spring 
Status 
Number of ECTS Credits 
Class Hours Per Week 
Total Hours Per Semester 
Language 
Nonarea Elective 
5 

120 
English 
Probabilty and Statistics for Engineers 
COURSE OBJECTIVE 
The course is intendent to achieve following goals:
•Present role of statistics in engineering.
•Introduce basic topics and solution techniques of statistics and probability.
•Develop an appreciation for the development of mathematical thought using learned techniques.
•Expand understanding of introduced topics and research principles for applications in real life problems and analyzing the results. 
COURSE CONTENT 
 Introduction, Set Theory, Applying Set Theory to Probability
 Axioms, Conditional Probability, Independence
 Tree Diagrams, Counting Methods
 Independent Trials, Reliability Problems
 Discrete Random Variables, Probability Mass Function
 Families of DRV, Cumulative Distribution Function, Averages
 Expected Value, Variance and Standard Deviation, Conditional Probability Mass Function
 Midterm
 Continuous Random Variables
 Families of CRV
 Probability Models
 Conditioning a CRV
 Pairs of RV
 Joint PDF
 Independent RV

LABORATORY/PRACTICE PLAN 
 Introduction, Set Theory, Applying Set Theory to Probability
 Axioms, Conditional Probability, Independence
 Tree Diagrams, Counting Methods
 Independent Trials, Reliability Problems
 Discrete Random Variables, Probability Mass Function
 Families of DRV, Cumulative Distribution Function, Averages

 Expected Value, Variance and Standard Deviation, Conditional Probability Mass Function
 Midterm
 Continuous Random Variables
 Families of CRV
 Probability Models
 Conditioning a CRV
 Pairs of RV
 Joint PDF
 Independent RV

Description 
 Interactive Lectures
 Practical Sessions
 Excersises
 Problem solving
 Assignments

Description (%) 
Quiz  2  15  Midterm Exam(s)  1  30  Final Exam  1  40 

Learning outcomes 
 Evaluate basic theories, processes and outcomes of computing
 Apply theory, techniques and relevant tools to the specification, analysis, design, implementation and testing of a simple computing product
 Knowledge and critical understanding of the wellestablished principles of computing, and of the way in which those principles have developed as technology has progressed
 Knowledge of all of the main development methods relevant to the field of computing, and ability to evaluate critically the appropriateness of different approaches to solving problems in the field of study

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  10  20  Seminar / Presentation    0 

