TÜRKÇE
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Information
| Unit | FACULTY OF ENGINEERING |
| INDUSTRIAL ENGINEERING PR. | |
| Code | ENM243 |
| Name | Probability And Statistics I |
| Term | 2018-2019 Academic Year |
| Semester | 3. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 4 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Label | C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. RIZVAN EROL |
| Course Instructor |
Prof. Dr. RIZVAN EROL
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
Learn the basic concepts of permutation, combination, probability and random variables and their properties. Learn to design and analyze the data
Course Content
Permutation, combination, probability, sampling, measures of central tendency ,random variables
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Solve the problems of permutation and combination |
| LO02 | Use the probability of an event, probability axioms, and some of the rules of probability |
| LO03 | Apply conditional probability, independent events, Bayes theorem |
| LO04 | Organize and analyze data |
| LO05 | Understand measures of central tendency and dispersion measures |
| LO06 | Know the concept of a random variable, the distribution of a random variable |
| LO07 | Describe the expected value of a random variable, the variance and their properties |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | - | Has sufficient background on topics related to mathematics, physical sciences and industrial engineering. | 0 |
| PLO02 | - | Gains ability to use the acquired theoretical knowledge on basic sciences and industrial engineering for describing, formulating and solving an industrial engineering problem, and to choose appropriate analytical and modeling methods. | 0 |
| PLO03 | - | Gains ability to analyze a service and/or manufacturing system or a process and describes, formulates and solves its problems . | 0 |
| PLO04 | - | Gains ability to choose and apply methods and tools for industrial engineering applications. | 0 |
| PLO05 | - | Can collect and analyze data required for industrial engineering problems ,develops and evaluates alternative solutions. | 0 |
| PLO06 | - | Works efficiently and takes responsibility both individually and as a member of a multi-disciplinary team. | 0 |
| PLO07 | - | Can access information and to search/use databases and other sources for information gathering. | 0 |
| PLO08 | - | Appreciates life time learning; follows scientific and technological developments and renews himself/herself continuously. | 0 |
| PLO09 | - | Can use computer software in industrial engineering along with information and communication technologies. | 0 |
| PLO10 | - | Can use oral and written communication efficiently. | 0 |
| PLO11 | - | Uses English skills to follow developments in industrial engineering and to communicate with people in his/her profession. | 0 |
| PLO12 | - | Has a conscious understanding of professional and ethical responsibilities. | 0 |
| PLO13 | - | Has a necessary consciousness on issues related to job safety and health, legal aspects of environment and engineering practice. | 0 |
| PLO14 | - | Becomes competent on matters related to project management, entrepreneurship, innovation and has knowledge about current matters in industrial engineering. | 0 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Sample spaces and events | Source reading | |
| 2 | Permutation | Source reading | |
| 3 | Combination | Source reading | |
| 4 | Ordered and unordered disruptions, Binomial expansion | Source reading | |
| 5 | Probability of an event, Probability axioms, | Source reading | |
| 6 | Conditional Probability | Source reading | |
| 7 | Independent events, Bayes theorem | Source reading | |
| 8 | Midterm exam | Source reading | |
| 9 | Methods of sample selection | Source reading | |
| 10 | Data Organization, Frequency Distribution, Graphical Representations | Source reading | |
| 11 | Measures of Central Tendency, Measures of Dispersion | Source reading | |
| 12 | Measure of skewness and kurtosis, Coefficient of variation | Source reading | |
| 13 | Distribution of Discrete Random Variable | Source reading | |
| 14 | Distribution of Continous Random Variable | Source reading | |
| 15 | Expected Value, Variance and their properites | Source reading | |
| 16 | Final exam | Source reading | |
| 17 | Final exam | Source reading |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 7 | 7 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 109 | ||
| Total Workload / 25 (h) | 4,36 | ||
| ECTS | 4 ECTS | ||