INTERNATIONAL BURCH UNIVERSITY
Faculty of Engineering and Natural Sciences
Department of Information Technologies
2015-2016

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
Compulsory 5 2 + 2 120 English

Instructor Assistant Coordinator
Nejdet Dogru, Assist. Prof. Dr. Nejdet Dogru Jasmin Kevrić, Assist. Prof. Dr.
[email protected] [email protected] no email

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
Week
Topic
  1. Introduction, Set Theory, Applying Set Theory to Probability
  2. Axioms, Conditional Probability, Independence
  3. Tree Diagrams, Counting Methods
  4. Independent Trials, Reliability Problems
  5. Discrete Random Variables, Probability Mass Function
  6. Families of DRV, Cumulative Distribution Function, Averages
  7. Expected Value, Variance and Standard Deviation, Conditional Probability Mass Function
  8. Midterm
  9. Continuous Random Variables
  10. Families of CRV
  11. Probability Models
  12. Conditioning a CRV
  13. Pairs of RV
  14. Joint PDF
  15. Independent RV

LABORATORY/PRACTICE PLAN
Week
Topic
  1. Introduction, Set Theory, Applying Set Theory to Probability
  2. Axioms, Conditional Probability, Independence
  3. Tree Diagrams, Counting Methods
  4. Independent Trials, Reliability Problems
  5. Discrete Random Variables, Probability Mass Function
  6. Families of DRV, Cumulative Distribution Function, Averages

  1. Expected Value, Variance and Standard Deviation, Conditional Probability Mass Function
  2. Midterm
  3. Continuous Random Variables
  4. Families of CRV
  5. Probability Models
  6. Conditioning a CRV
  7. Pairs of RV
  8. Joint PDF
  9. Independent RV

TEACHING/ASSESSMENT
Description
  • Interactive Lectures
  • Practical Sessions
  • Excersises
  • Problem solving
  • Assignments
Description (%)
Method Quantity Percentage (%)
Quiz215
Midterm Exam(s)130
Final Exam140
Total: 85
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 well-established 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
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/ Project21020
Seminar / Presentation 0
Total Workload: 120
ECTS Credit (Total workload/25): 5