ECTS - Probability and Statistics

Probability and Statistics (IE220) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Probability and Statistics IE220 3. Semester 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Compulsory Departmental Courses
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)
  • Asst. Prof. Dr. Aida SALIMNEZHADGHAREHZIAEDDINI
Course Assistants
Course Objectives In this course, the students will be learning fundamental concepts of the probability and statistics so that they can solve practical problems of engineering which requires statistical techniques.
Course Learning Outcomes The students who succeeded in this course;
  • Students will acquire and apply fundamental concepts of probability theory to engineering problems.
  • Students will develop an insight about the role of statistics for different engineering disciplines.
  • Students will be able to evaluate and solve real life processes and problems using statistical applications.
  • Students will be able to use a suitable computer-based statistical package for statistical analysis.
Course Content Introduction to probability and statistics; random variables and probability distributions; expected value; sampling distributions; one and two sample estimation problems; test of hypotheses; simple linear regression.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 The role of probability and statistics in engineering [1] pages 1-15
2 Descriptive Statistics-Numerical Summary [1] pages 191-214
3 Descriptive Statistics-Graphical Summary [1] pages 191-214
4 Probability [1] pages 17-57
5 Probability [1] pages 17-57
6 Random Variables [1] pages 67-74 [1] pages 108-114
7 Midterm 1
8 Discrete Probability Distributions [1] pages 79-97
9 Continuous Probability Distributions [1] pages 116-127
10 Sampling Distributions [1] pages 223-231
11 Point and Interval Estimation [1] pages 253-263
12 Point and Interval Estimation [1] pages 253-263
13 Hypothesis Testing [1] pages 283-314
14 Midterm 2
15 Inference for two samples [1] pages 351-368
16 Simple Linear Regression [1] pages 401-440

Sources

Course Book 1. Montgomery, D.C., and Runger, G.C., Applied Statistics and Probability for Engineers, John Wiley and Sons, Inc., 4th Edition, June 2006.
Other Sources 2. Walpole, R.E. , Myers, R.H., Myers, S.L. an Ye, K., Probability and Statistics for Engineers and Scientists, Prentice Hall, 8th edition, 2007.
3. Milton, J.S. and Arnold, J.C., Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences, McGraw-Hill, 4th edition, 2002.
4. Ross, S., Introduction to Probability and Statistics for Engineers and Scientists, Academic Press, 3rd edition, 2004.
5. Triola, M.F., Essentials of Statistics, Addison Wesley,2nd edition, 2004.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 4 20
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 40
Final Exam/Final Jury 1 40
Toplam 7 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Engineering Knowledge: Knowledge of mathematics, science, fundamental engineering, computational sciences, and related engineering disciplines; the ability to apply this knowledge to solve complex engineering problems. X
2 Problem Analysis: The ability to identify, formulate, and analyze complex engineering problems using fundamental scientific, mathematical, and engineering knowledge, considering the relevant UN Sustainable Development Goals.
3 Engineering Design: The ability to design creative solutions to complex engineering problems; the ability to design complex systems, processes, devices, or products to meet current and future requirements, considering realistic constraints and conditions.
4 Techniques and Tool Usage: The ability to select and use appropriate techniques, resources, and modern engineering and computing tools, including estimation and modeling, for the analysis and solution of complex engineering problems, while being aware of their limitations.
5 Research and Investigation: The ability to use research methods, including literature review, designing experiments, conducting experiments, collecting data, analyzing and interpreting results, to investigate complex engineering problems.
6 Global Impact of Engineering Applications: Information about the impacts of engineering applications on society, health and safety, the economy, sustainability and the environment within the framework of the UN Sustainable Development Goals; awareness of the legal consequences of engineering solutions.
7 Engineering Ethics: Knowledge of ethical responsibility and adherence to engineering professional principles; awareness of impartiality, lack of discrimination, and inclusivity.
8 Individual and Teamwork: The ability to work effectively individually and as a team member or leader in interdisciplinary and multidisciplinary teams (face-to-face, on-line, or hybrid).
9 Oral and Written Communication: The ability to communicate effectively orally and in writing on technical topics, considering the diverse differences of the target audience (education, language, profession, etc.).
10 Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
11 Lifelong Learning: The ability to learn independently and continuously, adapt to new and emerging technologies, and think critically about technological change.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 3 48
Presentation/Seminar Prepration
Project
Report
Homework Assignments 4 5 20
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 2 3 6
Prepration of Final Exams/Final Jury 1 3 3
Total Workload 125