ECTS - Probability and Statistics II
Probability and Statistics II (IE202) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Probability and Statistics II | IE202 | 4. Semester | 3 | 1 | 0 | 3 | 6.5 |
| Pre-requisite Course(s) |
|---|
| IE201 |
| 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, Discussion, Question and Answer, Problem Solving, Team/Group, Project Design/Management. |
| Course Lecturer(s) |
|
| Course Objectives | The course aims to expose students to basic concepts of statistical inference, linear regression and correlation, forecasting and experimental design. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Hypothesis testing and statistical comparison of engineering systems/processes, simple and multiple regression and correlation, basic concepts of stochastic processes and time series, statistical estimation and confidence intervals, statistical quality control and process capability, analysis of variance, design and analysis of engineering experiments, basicinteractions between statistics, AI, and data-driven engineering, and integrated engineering case studies. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Hypothesis Testing | Montgomery, Runger & Hubele, Ch. 9 – Tests of Hypotheses for a Single Sample |
| 2 | Comparing Engineering Systems/Processes | Montgomery, Runger & Hubele, Ch. 10 – Statistical Inference for Two Samples |
| 3 | Regression Models | Montgomery, Runger & Hubele, Ch. 11 – Simple Linear Regression and Correlation; Ch. 12 – Multiple Linear Regression; James et al., Ch. 3 – Linear Regression |
| 4 | Time-based stochastic models in engineering | Russell & Norvig, Ch. 14 – Probabilistic Reasoning over Time; Montgomery, Runger & Hubele, Ch. 2–4 – Probability and Probability Distributions |
| 5 | From Statistics to AI and Data-Driven Engineering: Some basic interactions | James et al., Ch. 2 – Statistical Learning; Ch. 3 – Linear Regression; Russell & Norvig, Ch. 1 – Introduction |
| 6 | Statistical Estimation for Engineering Decision Making | Montgomery, Runger & Hubele, Ch. 4 – Decision Making for a Single Sample; Secs. 4-1–4-2, 4-4–4-8 |
| 7 | The role statistical estimation in quality control | Montgomery, Runger & Hubele, Ch. 8 – Statistical Process Control; Secs. 8-1–8-5 |
| 8 | Midterm Exam | |
| 9 | Comparison of multiple engineering systems/processes | Montgomery, Runger & Hubele, Ch. 5 – Decision Making for Two Samples; Sec. 5-8 – What if We Have More than Two Samples? |
| 10 | Design of engineering experiments for process improvement | Montgomery, Runger & Hubele, Ch. 7 – Design of Engineering Experiments; Secs. 7-1–7-3 |
| 11 | A Case example from engineering data and statistical estimation | Montgomery, Runger & Hubele, Ch. 4 – Decision Making for a Single Sample; Ex. 4-5 – Rocket Propellant Burning Rate |
| 12 | A case example from statistical quality improvement A case example from engineering experimentation | Montgomery, Runger & Hubele, Ch. 8 – Statistical Process Control; Ex. 8-1 – Jet Aircraft Engine Vane Opening; Ex. 8-3 – Electrical Current Process Capability James et al., Ch. 2 – Statistical Learning; Montgomery, Runger & Hubele, Ch. 6 – Building Empirical Models; Ex. 6-1 – Salt Concentration and Roadway Area |
| 13 | A case example from reliability engineering | Montgomery, Runger & Hubele, Ch. 3 – Random Variables and Probability Distributions; Ex. 3-13 -3-16 |
| 14 | An integrated case example from data-centric engineering | James et al., Ch. 2 – Statistical Learning; Montgomery, Runger & Hubele, Ch. 6 – Building Empirical Models; Ex. 6-1 – Salt Concentration and Roadway Area |
| 15 | Review | |
| 16 | Final Exam |
Sources
| Course Book | 1. Montgomery, D.C., and Runger, G.C., Applied Statistics and Probability for Engineers, 5th Edition, John Wiley and Sons, 2011. |
|---|---|
| Other Sources | 2. James, Witten, Hastie & Tibshirani - An Introduction to Statistical Learning, 2nd Edition, 2023. |
| 3. Russell & Norvig, Artificial Intelligence: A Modern Approach, 4th Edition, 2022. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 3 | 10 |
| Presentation | - | - |
| Project | 1 | 20 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 30 |
| Final Exam/Final Jury | 1 | 40 |
| Toplam | 6 | 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 | Gains adequate knowledge in mathematics, science, and relevant engineering disciplines and acquires the ability to use theoretical and applied knowledge in these fields to solve complex engineering problems. | X | ||||
| 2 | Gains the ability to identify, formulate, and solve complex engineering problems and the ability to select and apply appropriate analysis and modeling methods for this purpose. | X | ||||
| 3 | Gains the ability to design a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements and to apply modern design methods for this purpose. | |||||
| 4 | Gains the ability to select and use modern techniques and tools necessary for the analysis and solution of complex engineering problems encountered in industrial engineering applications and the ability to use information technologies effectively. | X | ||||
| 5 | Gains the ability to design experiments, conduct experiments, collect data, analyze results, and interpret findings for investigating complex engineering problems or discipline specific research questions. | X | ||||
| 6 | Gains the ability to work effectively in intra-disciplinary and multi-disciplinary teams and the ability to work individually. | |||||
| 7 | Gains the ability to communicate effectively in written and oral form, acquires proficiency in at least one foreign language, the ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | |||||
| 8 | Gains awareness of the need for lifelong learning and the ability to access information, follow developments in science and technology, and to continue to educate him/herself. | |||||
| 9 | Gains knowledge about behaviour in accordance with ethical principles, professional and ethical responsibility and standards used in industrial engineering applications | |||||
| 10 | Gains knowledge about business practices such as project management, risk management, and change management and develops awareness of entrepreneurship, innovation, and sustainable development. | |||||
| 11 | Gains knowledge about the global and social effects of industrial engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | |||||
| 12 | Gains skills in the design, development, implementation, and improvement of integrated systems involving human, material, information, equipment, and energy. | |||||
| 13 | Gains knowledge about appropriate analytical and experimental methods, as well as computational methods, for ensuring system integration. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | 14 | 2 | 28 |
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 2 | 32 |
| Presentation/Seminar Prepration | |||
| Project | 1 | 15 | 15 |
| Report | |||
| Homework Assignments | 3 | 6 | 18 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 10 | 10 |
| Prepration of Final Exams/Final Jury | 1 | 12 | 12 |
| Total Workload | 163 | ||