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Information
| Code | BBZ206 |
| Name | Statistics II |
| Term | 2024-2025 Academic Year |
| Semester | 4. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. GÜZİN YÜKSEL |
| Course Instructor |
1 |
Course Goal / Objective
The aim of this course is to provide the basic concepts of probability theory, random variables and their distributions, and to lay the foundation for an introduction to statistics.
Course Content
In this course, random experiments, sample space events, probability functions, probability calculations, conditional probability, random variables, functions of random variables, discrete random variables and their distributions are covered.
Course Precondition
None
Resources
Olasılık ve İstatistik, Fikri Akdeniz, Nobel Yayınevi, Adana
Notes
Ders Notları
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Understands the rules of sample spaces, sample points, and counting sample points. |
| LO02 | Solves permutation and combination problems. |
| LO03 | Uses probability of an event, the rules of probability and probability axioms |
| LO04 | Applies conditional probability, independent events and Bayes theorem. |
| LO05 | Understands the concept of a random variable and distribution of a random variable. |
| LO06 | Understands the expected value, the variance and the properties of a random variable. |
| LO07 | Uses concepts of moments, skewness and kurtosi, and the Chebyshew inequality. |
| LO08 | Recognize some discrete distributions such as Bernoulli, Binomial, Multinomial, Geometric, Negative Binomial. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Gain comprehensive knowledge of fundamental concepts, algorithms, and data structures in Computer Science. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learn essential computer topics such as software development, programming languages, and database management | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understand advanced computer fields like data science, artificial intelligence, and machine learning. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Acquire knowledge of topics like computer networks, cybersecurity, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develop skills in designing, implementing, and analyzing algorithms | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gain proficiency in using various programming languages effectively | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learn skills in data analysis, database management, and processing large datasets. | 4 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Acquire practical experience through working on software development projects. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthen teamwork and communication skills. | 3 |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | Foster a mindset open to technological innovations. | 3 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourage the capacity for continuous learning and self-improvement. | 3 |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Enhance the ability to solve complex problems | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | The concept of sample space, sample point, event, counting rules for sample points | Required reading | Öğretim Yöntemleri: Tartışma, Beyin Fırtınası |
| 2 | Permutations, combinations. | Reading sources | Öğretim Yöntemleri: Soru-Cevap, Tartışma |
| 3 | Ordered and unordered partitions, Binomial Theorem | Reading sources | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | The probability of an event, the probability axioms, some of the probability rules | Reading sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Geometric probablity, Conditonal probability | Reading sources | Öğretim Yöntemleri: Anlatım, Tartışma |
| 6 | Independent events, Bayes theorem | Reading sources | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 7 | Random variables, probabilty distribution of discrete random variables | Reading sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 8 | Mid-Term Exam | Written Exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Probabilty distribution of continuous random variables | Reading sources | Öğretim Yöntemleri: Anlatım, Örnek Olay |
| 10 | The expected value of a random variable, the variance and their properties, | Reading sources | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 11 | Moments, skewness and kurtosis, | Reading sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 12 | Chebyshew inequality, Problem solving | Reading sources | Öğretim Yöntemleri: Anlatım, Tartışma |
| 13 | Bernoulli distribution, binomial distribution, a multinomial distribution, Geometric distribution | Reading sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 14 | Negative binomial distribution, Hypergeometric distribution, Uniform distribution, | Reading sources | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 15 | Solving Problem | Review of topics discussed in the lecture notes and sources | Öğretim Yöntemleri: Soru-Cevap, Problem Çözme |
| 16 | Term Exams | Written exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Written exam | Ölçme Yöntemleri: Yazılı Sınav |
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 | 6 | 84 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 8 | 8 |
| Final Exam | 1 | 16 | 16 |
| Total Workload (Hour) | 150 | ||
| Total Workload / 25 (h) | 6,00 | ||
| ECTS | 6 ECTS | ||