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Information
| Code | BBZ205 |
| Name | Statistics I |
| Term | 2024-2025 Academic Year |
| Semester | 3. 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
This course aims to lay the foundation for an introduction to statistics by teaching information about probability theory, random variables and the distributions of these variables.
Course Content
Random experiment, sample space, event, probability function, probability calculations, conditional probability, random variables, functions of random variables, discrete random variables and their distributions.
Course Precondition
There are no prerequisites.
Resources
Olasılık ve İstatistik, Fikri Akdeniz, Nobel Yayınevi, Adana
Notes
Olasılığa Giriş, George Roussas, Çeviri Editörü: Prof. Dr. Şanslı Şenol, Doç. Dr. Güzin Yüksel.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Understands sample spaces, sample points and counting sample points rules. |
| LO02 | Solves permutation, combination problems |
| LO03 | Uses probability of an event, probability axioms and some of the rules of probability |
| 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 the concepts of moments, skewness and kurtosis of the distribution and Chebyshev's 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. | 4 |
| 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. | 5 |
| 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. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourage the capacity for continuous learning and self-improvement. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Enhance the ability to solve complex problems | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Sample spaces, sample points, counting rules for sample points | Reading source | Öğretim Yöntemleri: Beyin Fırtınası, Anlatım |
| 2 | Permutations and combinations | Reading source | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Ordered and unordered partitions, Binomial Theorem | Reading source | Öğretim Yöntemleri: Anlatım, Tartışma, Soru-Cevap |
| 4 | The probability of an event, the probability axioms and some of the probability rules | Reading source | Öğretim Yöntemleri: Anlatım, Tartışma |
| 5 | Geometric probablity, conditonal probability | Reading source | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | Independent events and Bayes theorem | Reading source | Öğretim Yöntemleri: Anlatım, Tartışma, Soru-Cevap |
| 7 | Concept of random variable, distribution of discrete random variable | Reading source | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Tartışma |
| 8 | Mid-Term Exam | Review of topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Distribution of continuous random variable | Reading source | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 10 | The expected value of a random variable, the variance and their properties | Reading source | Öğretim Yöntemleri: Anlatım, Tartışma |
| 11 | Moments, skewness and kurtosis in a distribution | Reading source | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 12 | Chebyshew inequality, problem solving | Reading source | Öğretim Yöntemleri: Anlatım, Tartışma |
| 13 | Bernoulli distribution, binomial distribution, a multinomial distribution, geometric distribution | Reading source | Öğretim Yöntemleri: Anlatım, Tartışma |
| 14 | Negative binomial, Hypergeometric, Poisson and Uniform Distribution | Reading source | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 15 | Solving Problem | Review of topics discussed in the lecture notes and sources | Öğretim Yöntemleri: Alıştırma ve Uygulama, Soru-Cevap |
| 16 | Term Exams | Review of topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Review of topics discussed in the lecture notes and sources | 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 | ||