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 | Service Courses Taken From Other Departments |
| 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 | |
| Supportive Courses | X |
| 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 | An ability to apply knowledge of mathematics, science, and engineering. | |||||
| 2 | An ability to design and conduct experiments, as well as to analyze and interpret data. | |||||
| 3 | An ability to design a system, component, or process to meet desired needs. | |||||
| 4 | An ability to function on multi-disciplinary teams. | |||||
| 5 | An ability to identify, formulate, and solve engineering problems. | |||||
| 6 | An understanding of professional and ethical responsibility. | |||||
| 7 | An ability to communicate effectively. | |||||
| 8 | The broad education necessary to understand the impact of engineering solutions in a global and societal context. | |||||
| 9 | Recognition of the need for, and an ability to engage in life-long learning. | |||||
| 10 | Knowledge of contemporary issues. | |||||
| 11 | An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice. | |||||
| 12 | Skills in project management and recognition of international standards and methodologies | |||||
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 | ||