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 | Prof. Dr. Adnan MAZMANOĞLU |
| Mode of Delivery | Face to Face |
| Suggested Subject | NONE |
| Professional practise ( internship ) | None |
| Objectives of the Course | The goal is to familiarize students with powerful analytical and numerical tools in the areas of probability and statistics that can be used to solve real world engineering problems. |
| Contents of the Course | Descriptive statistics. Elementary probability. Propagation of error. Probability distributions: binomial, Poisson, normal, exponential. The central limit theorem. Point and interval estimation. Selected examples of engineering applications. |
Learning Outcomes of Course
| # | Learning Outcomes |
|---|---|
| 1 | To Choose appropriate descriptive statistics and graphical displays to summarize a data set. |
| 2 | To compute the numerical values of the sample statistics and interpret them |
| 3 | To distinguish between commonly used random variables and sampling distributions in order to identify the appropriate statistical tools based on the context of a given problem. |
| 4 | To identify, formulate, and evaluate appropriate tools for statistical inference based on the context of a given problem. |
| 5 | To understand and to be able to apply the central limit theorem. |
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 | 1.3 Graphical Summaries II. Probability 2.1 Basic Ideas | lecturing, problem solving, discussing |
| 3 | 2.2 Counting Methods 2.3 Conditional Probability and Independence | lecturing, problem solving, discussing |
| 4 | 2.4 Random Variables 2.5 Linear Functions of 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 | lecturing, problem solving, discussing |
| 8 | Mid-Term Exam | |
| 9 | 4.3 The Poisson Distribution 4.4 Some Other Discrete Distributions | lecturing, problem solving, discussing |
| 10 | 4.5 The Normal Distribution 4.6 The Lognormal Distribution | lecturing, problem solving, discussing |
| 11 | 4.9 Some Principles of Point Estimation 4.10 Probability Plots | lecturing, problem solving, discussing |
| 12 | 4.11 The Central Limit Theorem V. Confidence Intervals 5.1 Large-Sample Confidence Intervals for a Population Mean | lecturing, problem solving, discussing |
| 13 | 5.2 Confidence Intervals for Proportions 5.3 Small-Sample Confidence Intervals for a Population Mean | lecturing, problem solving, discussing |
| 14 | 5.4 Confidence Intervals for the Difference Between Two Means 5.6 Small-Sample Confidence Intervals for the Difference Between Two Means | lecturing, problem solving, discussing |
| 15 | 5.7 Confidence Intervals with Paired Data 5.8 Prediction Intervals and Tolerance Intervals | 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 | George G. Roussas, A Course in Mathematical Statistics | Academic Press | |
| 3 | John A. Rice, Mathematical Statistics and Data Analysis | Thomson Brooks/Cole |
Method of Assessment
| # | Weight | Work Type | Work Title |
|---|---|---|---|
| 1 | 40% | Mid-Term Exam | Mid-Term Exam |
| 2 | 60% | Final Exam | Final Exam |
Relationship between Learning Outcomes of Course and Program Outcomes
| # | Learning Outcomes | Program Outcomes | Method of Assessment |
|---|---|---|---|
| 1 | To Choose appropriate descriptive statistics and graphical displays to summarize a data set. | 1͵11 | 1͵2 |
| 2 | To compute the numerical values of the sample statistics and interpret them | 1͵11 | 1͵2 |
| 3 | To distinguish between commonly used random variables and sampling distributions in order to identify the appropriate statistical tools based on the context of a given problem. | 1͵11 | 1͵2 |
| 4 | To identify, formulate, and evaluate appropriate tools for statistical inference based on the context of a given problem. | 1͵11 | 1͵2 |
| 5 | To understand and to be able to apply the central limit theorem. | 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 | ||||