Mechanical Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Mechanical Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Mechanical Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 99600010 | Probability and Statistics | 1 | Autumn | 1 | 3+0+0 | 3 | 5 |
| Course Type | Compulsory |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | English |
| Methods and Techniques | Anlatım |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Asst. Prof. Remzi ŞAHİN |
| Instructor(s) | Asst. Prof. Sümeyye BAKIM |
| Instructor Assistant(s) | - |
Course Content
Introduction, Data Collection, Data Processing, Series, Graphs, measures of central tendency and dispersion, analysis of variance, curve fitting, correlation and regression analysis
Objectives of the Course
Under the main heading of teaching basic statistical concepts; Collecting, compiling, summarizing, presenting and analyzing data, as well as drawing valid conclusions from the data, curve fitting, correlation and regression analysis are the main objectives of the probability and statistics course.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | X |
| Specialization / Field Courses | |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | Adequate knowledge of mathematics, science and mechanical engineering disciplines; Ability to use theoretical and applied knowledge in these fields in solving complex engineering problems. | 5 |
| P5 | An ability to design and conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or research topics specific to mechanical engineering. | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Gains the ability to apply mathematics, science and engineering knowledge | P.1.29 | 1 |
| O2 | Analyzes engineering data. | P.1.30 | 1 |
| O3 | Gains the ability to analyse, interpret, infer and predict statistically. | P.5.1 | 1 |
| O4 | Gains the ability to design a system. | P.5.2 | 1 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | The importance of statistics in mechanical engineering |
| 2 | Cluster Theory, Counting techniques: multiplication rule, permutation, Repeated permutation, combination |
| 3 | Frequency analysis and estimation of parameters |
| 4 | Randomness, sample space, set algebra of events, probability space, probability axiom |
| 5 | Conditional Probability, Bayes theorem |
| 6 | Probability distributions |
| 6 | Determination of Continuous Probability Distribution Models |
| 8 | Determination of Probability Distribution Models |
| 9 | Multivariate distribution, joint probability distributions, extreme distributions |
| 10 | Hypothesis tests |
| 11 | Hypothesis tests |
| 12 | Regression and correlation analysis |
| 13 | Regression and correlation analysis |
| 14 | General applications |
Textbook or Material
| Resources | Douglas C. Montgomery and George C. Runger, "Applied Statistics and Probability for Engineers |
| Ross S. , 2010, First Course in Probability, 8th edition, Prentice Hall |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 4 | 56 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 11 | 4 | 44 |
| Midterms | 1 | 10 | 10 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 10 | 10 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 120 | ||
| Total Work Load / 30 | 4 | ||
| Course ECTS Credits: | 4 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P5 |
|---|---|---|---|
| O1 | Gains the ability to apply mathematics, science and engineering knowledge | 5 | - |
| O2 | Analyzes engineering data. | 5 | - |
| O3 | Gains the ability to analyse, interpret, infer and predict statistically. | - | 5 |
| O4 | Gains the ability to design a system. | - | 5 |