Faculty of Engineering and Natural Sciences
Department of Information Technologies

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
Non-area Elective 5 110 English

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

Probabilty and Statistics

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.

  1. Course orientation and overview
  2. Set theory, events, sample space, definition and axioms of probability
  3. Counting techniques & Conditional probability
  4. Independence & Random variables
  5. Discrete random variables & Expected value
  6. Binomial distribution & Hypergeometric and negative binomial distributions
  7. Poisson distribution & Continuous probability distributions
  8. Midterm
  9. Exponential & Normal distribution
  10. Distribution of the mean and linear combinations & Basic properties of confidence intervals
  11. Confidence intervals on the mean & variance
  12. Introduction to hypothesis testing on the mean
  13. P values
  14. Inferences Based on Two Samples and The Two Sample t-test and conf. Interval
  15. Single Factor ANOVA


    • Lectures
    • Excersises
    • Presentation
    • Assignments
    • Recitation
    Description (%)
    Method Quantity Percentage (%)
    Midterm Exam(s)130
    Final Exam145
    +Attance 1 5
    Total: 100
    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
    • Probability and Statistics for Engineering and the Sciences by JAY DEVORE 8th Edition
    • Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Probability and Statistics for Engineers and Scientists”, Pearson International Edition, Eighth Edition

    ECTS (Allocated based on student) WORKLOAD
    Activities Quantity Duration (Hour) Total Work Load
    Lecture (14 weeks x Lecture hours per week)14456
    Laboratory / Practice (14 weeks x Laboratory/Practice hours per week) 0
    Midterm Examination (1 week)122
    Final Examination(1 week)122
    Preparation for Midterm Examination11515
    Preparation for Final Examination11515
    Assignment / Homework/ Project10220
    Seminar / Presentation 0
    Total Workload: 110
    ECTS Credit (Total workload/25): 4