Industrial Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 15240407 | Numerical Analysis | 2025 | Spring | 4 | 3+1+0 | 3,5 | 5 |
| Course Type | Compulsory |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Prof. Murat DARÇIN |
| Instructor(s) | - |
| Instructor Assistant(s) | - |
Course Content
Sampling theory refers to sampling distributions, differences in sampling distributions, t-distribution, chi-square distribution, F-distribution, estimation theory, classical theory of estimation, hypothesis testing, goodness-of-fit test, test of independence, homogenization, linear regression and correlation, and tests for analysis of variance.
Objectives of the Course
To provide the ability of using probability and statistics knowledge which is required to apply modeling and decision making techniques in engineering.
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 | Knowledge of mathematics, natural sciences, fundamental engineering, computational sciences, and industrial engineering-specific subjects; the ability to apply this knowledge to solve complex industrial engineering problems. | 5 |
| P5 | The ability to use research methods, including literature review, experimental design, experiment execution, data collection, analysis, and interpretation of results, to investigate complex industrial engineering problems. | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Analyzes data using graphical and numerical methods. | P.1.144 | 1 |
| O2 | Develops the fundamentals of statistical decision-making. | P.1.145 | 1 |
| O3 | Uses basic tools to analyze and model experimental relationships between variables. | P.1.146 | 1 |
| O4 | Analyzes estimation problems for single and double populations. | P.1.147 | 1 |
| O5 | Applies hypothesis tests. | P.1.148 | 1 |
| O6 | Analyzes data using graphical and numerical methods. | P.5.32 | 1 |
| O7 | Applies the principles of statistical decision-making. | P.5.33 | 1 |
| O8 | Uses basic tools to analyze and model experimental relationships between variables. | P.5.34 | 1 |
| O9 | Analyzes estimation problems for single and two populations. | P.5.35 | 1 |
| O10 | Applies hypothesis tests. | P.5.36 | 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 | Introduction to Engineering Statistics (statistics, science and observation, data structures, research method and statistics, variables, measurement) |
| 2 | Frequency Distributions (Tables, Graphs, Percentages) |
| 3 | Measures of Central Tendency (Mean, median, peak) |
| 4 | Measures of change (Range, variance, standard deviation) |
| 5 | Measures of change (Range, variance, standard deviation) |
| 6 | SPSS Login |
| 7 | SPSS Application |
| 8 | Midterm |
| 9 | SPSS Application |
| 10 | Hypothesis Tests |
| 11 | Regression Analysis-SPSS Application |
| 12 | Linear Regression and Correlation, Simple Linear Regression |
| 13 | Parametric and Nonparametric Hypothesis Testing - SPSS Application |
| 14 | Correlation Analysis-SPSS Application |
| 15 | Final Exam |
Textbook or Material
| Resources | MONTGOMERY, D. C., RUNGER, G. C., 1994. Applied Statistics and Probability for Engineers. John Wiley&Sons, Inc., USA. |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | 1 | 30 (%) |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 30 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 4 | 56 |
| Midterms | 1 | 16 | 16 |
| Quiz | 0 | 0 | 0 |
| Homework | 1 | 20 | 20 |
| 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 | 16 | 16 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 150 | ||
| Total Work Load / 30 | 5 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P5 |
|---|---|---|---|
| O1 | Analyzes data using graphical and numerical methods. | 5 | - |
| O2 | Develops the fundamentals of statistical decision-making. | 5 | - |
| O3 | Uses basic tools to analyze and model experimental relationships between variables. | 5 | - |
| O4 | Analyzes estimation problems for single and double populations. | 5 | - |
| O5 | Applies hypothesis tests. | 5 | - |
| O6 | Analyzes data using graphical and numerical methods. | - | 5 |
| O7 | Applies the principles of statistical decision-making. | - | 5 |
| O8 | Uses basic tools to analyze and model experimental relationships between variables. | - | 5 |
| O9 | Analyzes estimation problems for single and two populations. | - | 5 |
| O10 | Applies hypothesis tests. | - | 5 |