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 | 5. 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 Lecturer(s) |
|
| Course Objectives | In this course, students will learn the basic concepts of probability and statistics and their applications to engineering data and problems. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Introduction to probability and statistics, conditional probability, random variables and probability distributions, expected value, sampling distributions, one- and two-sample estimation problems, hypothesis testing, comparison of engineering systems/processes, simple and multiple linear regression, time-based stochastic models, and basic interactions between statistics, data science, machine learning, and AI in engineering |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Probabilistic modeling and the role of Statistics in engineering in the age of AI | Montgomery & Runger, Ch. 1 – The Role of Statistics in Engineering; James et al., Ch. 1 – Introduction; Russell & Norvig, Ch. 1 – Introduction |
| 2 | Probability Fundamentals & Conditional Probability | Montgomery & Runger, Ch. 2 – Probability Russell & Norvig, Ch. 12 – Quantifying Uncertainty; Ch. 13 – Probabilistic Reasoning |
| 3 | Modeling engineering systems/processes via random variables | Montgomery & Runger, Ch. 3 – Discrete Random Variables and Probability Distributions; Ch. 4 – Continuous Random Variables and Probability Distributions |
| 4 | Modeling discrete-valued engineering systems/processes | Montgomery & Runger, Ch. 3 – Discrete Random Variables and Probability Distributions |
| 5 | Modeling continuous-valued engineering systems/processes | Montgomery & Runger, Ch. 4 – Continuous Random Variables and Probability Distributions |
| 6 | A case example from renewable energy | Montgomery & Runger, Ch. 2–4 – Probability, Random Variables and Probability Distributions; Instructor-provided renewable energy case/data |
| 7 | Midterm Exam I (Weeks 1-5) Collection of data from an engineering system/process and estimation | Montgomery & Runger, Ch. 1 – The Role of Statistics in Engineering; Ch. 6 – Descriptive Statistics; Ch. 7 – Sampling Distributions and Point Estimation of Parameters |
| 8 | Estimation in engineering systems/processes with case examples | Montgomery & Runger, Ch. 7 – Sampling Distributions and Point Estimation of Parameters; Ch. 8 – Statistical Intervals for a Single Sample; Instructor-provided engineering cases |
| 9 | A case example from renewable energy (Continued) | Montgomery & Runger, Ch. 3–4 – Probability Distributions; Ch. 7 – Point Estimation of Parameters; Instructor-provided renewable energy case/data |
| 10 | Midterm Exam II (Weeks 6-9) Hypothesis Testing | Montgomery & Runger, Ch. 9 – Tests of Hypotheses for a Single Sample |
| 11 | Comparing Engineering Systems/Processes | Montgomery & Runger, Ch. 10 – Statistical Inference for Two Samples |
| 12 | Regression Models | Montgomery & Runger, Ch. 11 – Simple Linear Regression and Correlation; Ch. 12 – Multiple Linear Regression; James et al., Ch. 3 – Linear Regression |
| 13 | Time-based stochastic models in engineering | Russell & Norvig, Ch. 14 – Probabilistic Reasoning over Time; Montgomery & Runger, Ch. 2–4 – Probability and Probability Distributions |
| 14 | 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 |
| 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 | - | - |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 2 | 60 |
| Final Exam/Final Jury | 1 | 40 |
| Toplam | 3 | 100 |
| Percentage of Semester Work | 60 |
|---|---|
| Percentage of Final Work | 40 |
| Total | 100 |
Course Category
| Core Courses | |
|---|---|
| Major Area Courses | X |
| 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 | Knowledge of mathematics, natural sciences, engineering fundamentals, computing, and topics specific to the relevant engineering discipline; the ability to use this knowledge in the solution of complex engineering problems. | X | ||||
| 2 | The ability to identify, formulate, and analyze complex engineering problems using knowledge of basic sciences, mathematics, and engineering, and considering the UN Sustainable Development Goals relevant to the problem. | X | ||||
| 3 | The ability to design creative solutions for 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 | The ability to select and use appropriate techniques, resources, and modern engineering and IT tools, including prediction and modeling, for the analysis and solution of complex engineering problems, with an awareness of their limitations. | X | ||||
| 5 | The ability to use research methods for the investigation of complex engineering problems, including literature search, designing and conducting experiments, collecting data, and analyzing and interpreting results. | X | ||||
| 6 | Knowledge of the effects of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals; awareness of the legal consequences of engineering solutions. | |||||
| 7 | Acting in accordance with engineering professional principles, knowledge of ethical responsibility; awareness of acting impartially without discrimination on any grounds and being inclusive of diversity. | |||||
| 8 | The ability to work effectively individually and in intra-disciplinary and multi-disciplinary teams (face-to-face, remote, or hybrid) as a team member or leader. | |||||
| 9 | "The ability to communicate effectively orally and in writing on technical topics, considering the various differences of the target audience (such as education, language, profession). | |||||
| 10 | Knowledge of practices in business life such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation. | |||||
| 11 | The ability to engage in life-long learning, including independent and continuous learning, adapting to new and emerging technologies, and thinking inquisitively regarding technological changes. | |||||
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 | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 8 | 16 |
| Prepration of Final Exams/Final Jury | 1 | 13 | 13 |
| Total Workload | 125 | ||