| Information Systems Engineering | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
| Course Code: | FEC201 | ||||||||
| Ders İsmi: | Probability and Statistics | ||||||||
| Ders Yarıyılı: | Fall | ||||||||
| Ders Kredileri: |
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| Language of instruction: | Turkish | ||||||||
| Ders Koşulu: | |||||||||
| Ders İş Deneyimini Gerektiriyor mu?: | No | ||||||||
| Type of course: | Required | ||||||||
| Course Level: |
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| Mode of Delivery: | E-Learning | ||||||||
| Course Coordinator : | Asst. Prof. Dr. BAHAR YALÇIN KAVUŞ | ||||||||
| Course Lecturer(s): |
Asst. Prof. Dr. BAHAR YALÇIN KAVUŞ |
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| Course Assistants: |
| Course Objectives: | To learn the basics of probability and statistics and their applications in engineering problems. |
| Course Content: | This course provides a comprehensive introduction to probability theory and engineering applications. Among the topics covered in the course; probability definition and rules; random variables and uncertainty, expected value, variance and standard deviation; discrete probability distributions: Bernoulli, Binomial, geometric and Poisson distributions; continuous probability distributions: smooth, exponential and normal distributions; multivariate probability distributions, covariance and correlation; descriptive statistics; sampling and sampling distributions; estimation and confidence interval; Hypothesis tests are simple correlation. |
The students who have succeeded in this course;
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| Week | Subject | Related Preparation |
| 1) | Introduction to Probability. Example Space, simple events and event definitions. Probability axioms | |
| 2) | Conditional Probability, independent, incompatibilism. Counting techniques, Permutation Combination. Tree Diagram. Binomial and Multiple Binomial Theorems | Lecture Notes |
| 3) | Random Variable, Expectation (Mean) value, variance and standard deviation | Lecture Notes |
| 4) | Discrete random variables ( Bernoulli , Binomial, Poisson ) | Lecture Notes |
| 5) | Continuous random variables (Uniform, Exponential, Gamma, Gaussian) | Lecture Notes |
| 6) | Distribution functions of Discrete and Continuous random variables | |
| 7) | Markov and Chebychev inequality. Moment generating functions and their characteristics | Lecture Notes |
| 8) | Mid term | Lecture Notes |
| 9) | Marginal and conditional probabilities of two-dimensional probability distribution functions | Lecture Notes |
| 10) | Independence, covariance, correlation and correlation coefficient of two-dimensional random variables | Lecture Notes |
| 11) | Law of large numbers, Central limit theorem, hypothesis testing | Lecture Notes |
| 12) | Normality and single sample tests | Lecture Notes |
| 13) | Two Sample Test (Dependent and Independent two sample t test) and non -parametric equivalents) | Lecture Notes |
| 14) | N sample hypothesis tests and non -parametric equivalents | Lecture Notes |
| 15) | Final | Lecture Notes |
| Course Notes / Textbooks: | Leon-Garcia, Alberto (3rd ed.) Probability, statistics, and random processes for electrical engineering |
| References: | 1. Leon-Garcia, Alberto (3rd ed.) Probability, statistics, and random processes for electrical engineering 2. Sheldon M. Ross. (Sixth Edition) Introduction to Probability and Statistics for Engineers and Scientists |
| Ders Öğrenme Kazanımları | 1 |
4 |
2 |
3 |
|---|---|---|---|---|
| Program Outcomes | ||||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 1 | % 50 |
| Final | 1 | % 50 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| total | % 100 | |