Faculty Of Engıneerıng
Industrıal Engıneerıng (Englısh)
Course Information
| ENGINEERING STATISTICS | |||||
|---|---|---|---|---|---|
| Code | Semester | Theoretical | Practice | National Credit | ECTS Credit |
| Hour / Week | |||||
| MAT311 | Fall | 3 | 0 | 3 | 3 |
| Prerequisites and co-requisites | NONE |
|---|---|
| Language of instruction | English |
| Type | Required |
| Level of Course | Bachelor's |
| Lecturer | Asst. Prof. Dr. Türker ERTEM |
| Mode of Delivery | Face to Face |
| Suggested Subject | NONE |
| Professional practise ( internship ) | None |
| Objectives of the Course | This course aims to give the fundamental concepts in statistics and discuss their philosophy. The students learn the brief history of statistics and fundamental definitions in this science. The main goal is to familiarize students with analytical and numerical tools in the areas of statistics that can be used to solve real-world engineering problems. |
| Contents of the Course | Descriptive statistics. Elementary probability. Propagation of error. Probability distributions. The central limit theorem. Point and interval estimations. Confidence intervals. Hypothesis testings. Selected examples of engineering applications. |
Learning Outcomes of Course
| # | Learning Outcomes |
|---|---|
| 1 | To be able to compute and interpret descriptive statistics using numerical and graphical techniques. |
| 2 | To be able to compute point estimation of parameters, explain sampling distributions, and understand the central limit theorem. |
| 3 | To be able to construct confidence intervals and hypothesis testings on parameters for a single sample. |
| 4 | To be able to use statistical methodology and tools in the engineering problem-solving process. |
Course Syllabus
| # | Subjects | Teaching Methods and Technics |
|---|---|---|
| 1 | I. Sampling and Descriptive Statistics 1.1 Sampling 1.2 Summary Statistics | lecturing, problem solving, discussing |
| 2 | 1.2 Summary Statistics 1.3 Graphical Summaries | lecturing, problem solving, discussing |
| 3 | II. Probability 2.1 Basic Ideas 2.2 Counting Methods 2.3 Conditional Probability and Independence 2.4 Random Variables | lecturing, problem solving, discussing |
| 4 | 2.5 Linear Functions of Random Variables 2.6 Jointly Distributed Random Variables | lecturing, problem solving, discussing |
| 5 | III. Propagation of Error 3.1 Measurement Error 3.2 Linear Combinations of Measurements | lecturing, problem solving, discussing |
| 6 | 3.3 Uncertainties for Functions of One Measurement 3.4 Uncertainties for Functions of Several Measurements | lecturing, problem solving, discussing |
| 7 | IV. Commonly Used Distributions 4.1 The Bernoulli Distribution 4.2 The Binomial Distribution 4.3 The Poisson Distribution 4.4 Some Other Discrete Distributions | lecturing, problem solving, discussing |
| 8 | Mid-Term Exam | |
| 9 | 4.5 The Normal Distribution 4.6 The Lognormal Distribution 4.7 The Exponential Distribution 4.8 Some Other Continuous Distributions | lecturing, problem solving, discussing |
| 10 | 4.9 Some Principles of Point Estimation 4.10 Probability Plots 4.11 The Central Limit Theorem 4.12 Simulation | lecturing, problem solving, discussing |
| 11 | V. Confidence Intervals 5.1 Large-Sample Confidence Intervals for a Population Mean 5.2 Confidence Intervals for Proportions | lecturing, problem solving, discussing |
| 12 | 5.2 Confidence Intervals for Proportions 5.3 Small-Sample Confidence Intervals for a Population Mean | lecturing, problem solving, discussing |
| 13 | 5.9 Prediction Intervals and Tolerance Intervals | lecturing, problem solving, discussing |
| 14 | VI. Hypothesis Testing 6.1 Large-Sample Tests for a Population Mean 6.2 Drawing Conclusions from the Results of Hypothesis Tests | lecturing, problem solving, discussing |
| 15 | 6.3 Tests for a Population Proportion 6.4 Small-Sample Tests for a Population Mean | lecturing, problem solving, discussing |
| 16 | Final Exam |
Course Syllabus
| # | Material / Resources | Information About Resources | Reference / Recommended Resources |
|---|---|---|---|
| 1 | William Navidi Statistics for Engineers and Scientists | McGraw-Hill | |
| 2 | Douglas C. Montgomery, George C. Runger Applied Statistics and Probability for Engineers | John Wiley & Sons, Inc. | |
| 3 | John A. Rice Mathematical Statistics and Data Analysis | Thomson Brooks/Cole |
Method of Assessment
| # | Weight | Work Type | Work Title |
|---|---|---|---|
| 1 | 20% | Mid-Term Exam | Mid-Term Exam |
| 2 | 80% | Final Exam | Final Exam |
Relationship between Learning Outcomes of Course and Program Outcomes
| # | Learning Outcomes | Program Outcomes | Method of Assessment |
|---|---|---|---|
| 1 | To be able to compute and interpret descriptive statistics using numerical and graphical techniques. | 1͵11 | 1͵2 |
| 2 | To be able to compute point estimation of parameters, explain sampling distributions, and understand the central limit theorem. | 1͵11 | 1͵2 |
| 3 | To be able to construct confidence intervals and hypothesis testings on parameters for a single sample. | 1͵11 | 1͵2 |
| 4 | To be able to use statistical methodology and tools in the engineering problem-solving process. | 1͵11 | 1͵2 |
Work Load Details
| # | Type of Work | Quantity | Time (Hour) | Work Load |
|---|---|---|---|---|
| 1 | Course Duration | 14 | 3 | 42 |
| 2 | Course Duration Except Class (Preliminary Study, Enhancement) | 14 | 2 | 28 |
| 3 | Presentation and Seminar Preparation | 0 | 0 | 0 |
| 4 | Web Research, Library and Archival Work | 0 | 0 | 0 |
| 5 | Document/Information Listing | 0 | 0 | 0 |
| 6 | Workshop | 0 | 0 | 0 |
| 7 | Preparation for Midterm Exam | 1 | 6 | 6 |
| 8 | Midterm Exam | 1 | 2 | 2 |
| 9 | Quiz | 0 | 0 | 0 |
| 10 | Homework | 0 | 0 | 0 |
| 11 | Midterm Project | 0 | 0 | 0 |
| 12 | Midterm Exercise | 0 | 0 | 0 |
| 13 | Final Project | 0 | 0 | 0 |
| 14 | Final Exercise | 0 | 0 | 0 |
| 15 | Preparation for Final Exam | 1 | 10 | 10 |
| 16 | Final Exam | 1 | 2 | 2 |
| 90 | ||||