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Information
| Code | ST0055 |
| Name | Statistical analysis for fisheries sciences-I |
| Term | 2024-2025 Academic Year |
| Term | Fall |
| Duration (T+A) | 4-2 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 5 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
Prof. Dr. SEDAT GÜNDOGDU
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The aim of this course is to teach students how to do statistical analysis of data by using computer programs (SPSS/R/Tableau/EXCEL) and how to interpret and visualize the results.
Course Content
Basic terms in statistics. Data preperation and visualisation. Descriptive Statistics. Parametrical hypothesis testing (t-test, z-test, etc.). Non-Parametric Hypothesis testing (Chi-square, Mann-Whitney-U, etc.). Correlation and Regression Analysis. Some growth models. ANOVA and MANOVA
Course Precondition
Yok
Resources
Gündoğdu S. (2014). The usage of common multiple comparison tests (post-hoc) in fisheries sciences. Journal of FisheriesSciences. com 8 (4), 310-316 Gündoğdu, S., & Baylan, M. (2016). Farklı parametrizasyon tekniklerinin Saurida lessepsianus (Russell, Golani & Tikochinski, 2015)’un von Bertalanffy büyüme parametrelerinin tahminine etkisi. Su Ürünleri Dergisi, 32(4), 205-208.
Notes
Kalaycı, Ş. (2010). SPSS uygulamalı çok değişkenli istatistik teknikleri (Vol. 5). Ankara, Turkey: Asil Yayın Dağıtım. Crawley, M. J. (2012). The R book. John Wiley & Sons
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | With this course, students will have the ability to analyze the data obtained from both the real environment and the experimental studies. |
| LO02 | Learns how to prepare the data at hand for analysis before analysis |
| LO03 | Learns how to visualize analysis results. |
| LO04 | Gains general knowledge of some of the commonly used statistical package programs. |
| LO05 | Gains the ability to interpret the outputs obtained from statistical package programs. |
| LO06 | Gains the ability to construct the relationship between different disciplines statistically. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Develops theoretical and practical knowledge in the field of Marine Biology, Inland Water Biology or Basic Sciences in Fisheries at the level of expertise. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Comprehends interactions between Fisheries Basic Sciences and other disciplines. | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Determines the strategies related to the field of specialization in Basic Sciences of Aquaculture; explains the methods and techniques, measurement and concepts. | 5 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Produces new information and theories by interpreting and synthesising the information from other disciplines and uses the theoretical and practical information from their field of study in Fisheries Basic Science. | 4 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Collects data, interprets results and suggests solutions by using dialectic research methodology in the certain field of Marine and Inland Water Biology and Fisheries Basic Sciences. | 5 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Independently plans, designs and performs a certain project in the field of Fisheries Basic Sciences. | 3 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Produces solutions by improving new strategic approaches and taking responsibilities for the potential problems in the field of study as an individual or team member. | 3 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Determines the requirements for Fishery Basic Science education, reaches the resources, critically interpretes knowledge and skills and gains experience to direct the education. | 3 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Has positive stance on the lifelong education and uses it for the public benefit by using the gained theoretical and practical knowledge in the field of Marine and Inland Water Biology and Fisheries Basic Sciences. | |
| PLO10 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Follows the current topics and improvements in the field of Fisheries Basic Sciences, publishes and presents the research results, contributes to constitution of a public conscience in the field of interest. | |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Effectively communicates about the field of Marine and Inland Water Biology and Fisheries Basic Sciences by using written and oral presentation tools, follows up and criticizes the meetings and seminars. | |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Follows up international publications and communicates with international collaborators by using language skills. | |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Uses the communication and information technologies about the field of interest in an advanced level. | |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | Conforms, controls and teaches social, cultural and scientific ethics in the investigation and publication process of the data related with the field of interest. | 5 |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | Improves strategies, politics and application codes by following scientific and technological developments on the certain field of Marine and Inland Water Biology and Fisheries Basic Sciences. Investigates and extends the results on behalf of public in frame of total quality management process. | 5 |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | Uses the abilities and experiences on applications and solving problems that gained during the MSc education for the interdisciplinary studies. | 5 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Basic terms in statistics | The lecture notes should be reading before course | Öğretim Yöntemleri: Alıştırma ve Uygulama, Gösterip Yaptırma |
| 2 | Data preperation and visualisation | The lecture notes should be reading before course | Öğretim Yöntemleri: Alıştırma ve Uygulama, Gösterip Yaptırma |
| 3 | Data preperation and visualisation in Excel/Tableau/Spss | The lecture notes should be reading before course | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 4 | Descriptive Statistics | The lecture notes should be reading before course | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 5 | Descriptive Statistics in Excel/R/Tableau | The lecture notes should be reading before course | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Parametrical hypothesis testing (t-test, z-test, etc.) | The lecture notes should be reading before course | Öğretim Yöntemleri: Alıştırma ve Uygulama, Gösterip Yaptırma |
| 7 | Parametrical hypothesis testing in R/SPSS | The lecture notes should be reading before course | Öğretim Yöntemleri: Alıştırma ve Uygulama, Gösterip Yaptırma |
| 8 | Mid-Term Exam | The notes of the courses shown in the previous weeks should be read. | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Non-Parametric Hypothesis testing (Chi-square, Mann-Whitney-U, etc.) | The lecture notes should be reading before course | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 10 | Non-Parametric Hypothesis testing in SPSS | The lecture notes should be reading before course | Öğretim Yöntemleri: Alıştırma ve Uygulama, Gösterip Yaptırma |
| 11 | Non-Parametric Hypothesis testing (Chi-square, Mann-Whitney-U, etc.) in SPSS | The lecture notes should be reading before course | Öğretim Yöntemleri: Alıştırma ve Uygulama, Gösterip Yaptırma |
| 12 | Correlation and Regression Analysis in R/SPSS | The lecture notes should be reading | Öğretim Yöntemleri: Alıştırma ve Uygulama, Gösterip Yaptırma |
| 13 | Some growth models in R | The lecture notes should be reading | Öğretim Yöntemleri: Alıştırma ve Uygulama, Gösterip Yaptırma |
| 14 | ANOVA and MANOVA | The lecture notes should be reading | Öğretim Yöntemleri: Alıştırma ve Uygulama, Gösterip Yaptırma |
| 15 | ANOVA and MANOVA in SPSS | The lecture notes should be reading | Öğretim Yöntemleri: Alıştırma ve Uygulama, Gösterip Yaptırma |
| 16 | Term Exams | The lecture notes should be reading before course | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Exams | The lecture notes should be reading before course | Ölçme Yöntemleri: Yazılı 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 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 150 | ||
| Total Workload / 25 (h) | 6,00 | ||
| ECTS | 6 ECTS | ||