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Information
| Code | ZO656 |
| Name | Statistial Programming and Analysis with R |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 4-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 4 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. ZEYNEL CEBECİ |
| Course Instructor |
1 |
Course Goal / Objective
This course aims to teach the methods for statistical analysis, generating simple and advanced graphics, and statistical programming and applications with R..
Course Content
This course covers the basic statistical analysis, simple and advanced graphic plotting, and statistical programming and applications with R..
Course Precondition
No prerequisites
Resources
Cebeci, Z. (2019). Non-parametric Statistical Data Analysis with R. Abaküs Kitap, İstanbul. ISBN 9786052263600 Cebeci, Z. (2020). Veri Biliminde R İle Veri Önişleme. Nobel Akademik Yayıncılık, Ankara. ISBN 9786254060755
Notes
R Tutorial. https://www.tutorialspoint.com/r/index.htm
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Works in R statistical computing environment |
| LO02 | Learns how to analyse data with R. |
| LO03 | Learns the basic descriptive and inferential statistical methods. |
| LO04 | Produces and interprets the graphical results. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | After undergraduate education, increases knowledge in one of the fields of animal breeding and breeding, feeds and animal nutrition, biometrics and genetics. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Understands the interaction between different disciplines | 2 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Gains the ability to develop strategic approaches and produce regional, national or international solutions for the field of animal science | 1 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Zootekni bilimindeki verileri kullanarak bilimsel yöntemlerle bilgiyi geliştirebilme, bilimsel, toplumsal ve etik sorumluluk bilinci ile bu bilgileri kullanabilme becerisini kazanır | 5 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Gains the ability to use and develop information technologies with computer software and hardware knowledge required by the field of animal science. | 5 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Gains the ability to convey their own studies or current developments in the field of animal science to groups in the field or other fields of science, verbally and visually. | |
| PLO07 | Bilgi - Kuramsal, Olgusal | Gains the ability to evaluate the quality processes of animal products | |
| PLO08 | Bilgi - Kuramsal, Olgusal | Gains the ability to keep animal production dynamic in accordance with changing economic and social conditions. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Gains the ability to follow national and international current issues, to follow developments in lifelong learning, science and technology, to constantly renew themselves and to transfer innovations to animal production. | |
| PLO10 | Bilgi - Kuramsal, Olgusal | Absorbs the relationship between animal products and human health and community welfare |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Installing and working with R | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 2 | Data types and data organization with R | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 3 | Introduction to statistical methods | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 4 | Descriptive statistics | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 5 | Probability and computation of probabilities | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 6 | Probability distributions | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 7 | Data visualization techniques | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Preparation for the exam | Ölçme Yöntemleri: Ödev, Sözlü Sınav |
| 9 | Comparsion of sample means | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 10 | Comparison of proportions | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 11 | Comparison of variances | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 12 | Correlations and simple linear regression | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 13 | Introduction to categorical data analysis | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 14 | One-way ANOVA | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 15 | Case study | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Gösterip Yaptırma |
| 16 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Ödev, Sözlü Sınav |
| 17 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Ödev, Sözlü Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 28 | 28 |
| Total Workload (Hour) | 152 | ||
| Total Workload / 25 (h) | 6,08 | ||
| ECTS | 6 ECTS | ||