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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| ZOOTECHNICS (MASTER) (WITH THESIS) | |
| Code | ZO0028 |
| Name | Non -parametric Statistical Analysis with R |
| Term | 2024-2025 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
Prof. Dr. ZEYNEL CEBECİ
(Bahar)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
This course aims to teach the Non-parametric statistical methods using with R
Course Content
Assumptions for parametrical tests, rank calculation methods, non-parametric tests for one and two samples, non-parametric tests for one-way and two-way data tables, factorial designs, non-parametric correlation and regression methods, randomization analysis,categorical data analysis
Course Precondition
At least, completing a bachelor level courses such as Introduction to Statistics or parametric statistical methods is required.
Resources
Cebeci, Z. (2019). Non-parametric Statistical Analysis with R. Abaküs Kitap, Istanbul. ISBN :9786052263600
Notes
Non-parametric Methods. http://www.r-tutor.com/elementary-statistics/non-parametric-methods
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Learns the differences between parametric and non-parametric statistics |
| LO02 | Learns the non-parametric statistical methods. |
| LO03 | Learns how to analyse the data with non-parametric statistical analysis using R |
| LO04 | Analyzes the assumptions for the non-parametric methods. |
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 | 1 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Gains the ability to develop strategic approaches and produce regional, national or international solutions for the field of animal science | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Zootekni bilimindeki verileri kullanarak bilimsel yöntemlerle bilgiyi geliştirebilme, bilimsel, toplumsal ve etik sorumluluk bilinci ile bu bilgileri kullanabilme becerisini kazanır | |
| 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 | Assumption for the parametric statistics | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 2 | Non-parametric methods for one sample data | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Non-parametric methods for two samples data | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Non-parametric methods for one-way data | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Non-parametric methods for two-way data | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | Post-hoc tests (1) | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | Post-hoc tests (2) | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 8 | Mid-Term Exam | Preparation for the exam | Ölçme Yöntemleri: Sözlü Sınav, Ödev |
| 9 | Non-parametric methods for analysing factorial design data | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Non-parametric methods for correlation analysis | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Non-parametric regression analysis | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 12 | Logistic regression and classification | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 13 | Testing randomization (1) | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 14 | Testing randomization (2) | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 15 | Analysis of categorical data | Searching for the learning resources on the Internet, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 16 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Sözlü Sınav, Ödev |
| 17 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Ödev, Sözlü Sınav |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
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 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||