| Course Type | Elective Course |
|---|
| Course Level | Master's Degree Without Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- will have an ability on understanding the importance of probability in Industrial Engineering
- will have an ability on interpreting statistical data sets by using numerical and graphical techniques
- will have an ability on learning the probability definition and its rules to be able to solve problems
- will have an ability on learning the concept of random variables and their probability and distribution functions
- will have an ability on choosing suitable probability distributions to apply and interrelate with the real life problems.
- will have an ability on determination of the probability distributions for the functions of random variables and an ability to interrelate with sampling distributions
- will have an ability to describe the basic concepts of statistics (population, sample, random sample, sampling distribution etc.)
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Basic concepts and theorems of probability. Independence, Conditional probability and Baye's rule. Rando variables and realted functions. Basic probability and density functions.Fundamental discrete and contiuous distributions. Distributions of functions of random variables. Sampling distributions. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Probability as a scientific activity and extensions |
|
2nd Week |
Fundamentals of Probability |
|
3rd Week |
Definition of Probability, Conditional Probability, Bayes' Theorem and Independent Events |
|
4th Week |
Concept of Random Variable, Probability Distributions |
|
5th Week |
Discrete and Continuous Probability Distributions |
|
6th Week |
Joint Probability Distributions, Statistical Independence |
|
7th Week |
Mathematical Expectation, Variance and Covariance |
|
8th Week |
Some Discrete Probability Distributions |
|
9th Week |
Examination Week |
|
10th Week |
Some Continuous Probability Distributions |
|
11th Week |
Fuctions of Random Variables, Distribution Function Technique |
|
12th Week |
Change of Variables Techique (For one and two random variables) |
|
13th Week |
Moment Generating Function Technique, Definition of Random Sampling |
|
14th Week |
Concept of Random Sampling, Sampling Distributions for Some Statistics |
|
| Recommended or Required Reading | Walpole R.E., Myers R.H. Myers Sh. L. Ye K. Probability and Statistics or Engineers and Scientists Prentice Hall. 7th edition;
İ. Kara. Olasılık, Bilim Teknik, 2000
|
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Problem Solving
- Experiment
- Other
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
35 |
| Assignment |
3 |
15 |
| Quiz |
3 |
15 |
| Total(%) |
|
65 |
| Contribution of In-term Studies to Overall Grade(%) |
|
65 |
| Contribution of Final Examination to Overall Grade(%) |
|
35 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
3 |
4 |
12 |
| Individual or group work |
14 |
14 |
196 |
| Preparation for Final exam |
1 |
20 |
20 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
20 |
20 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
3 |
6 |
18 |
| Total Work Load | 312 |
| Total Work Load / 30 (h) | 10.4 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
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