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Information
| Code | TS0026 |
| Name | Statistical Methods in Water Resources Engineering |
| Term | 2023-2024 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 | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
The primary objectives of this course are: a) to teach the statistical methods commonly used in the analysis of data collected in the field of water resources engineering, b) to apply them to real observation data and, c) to contribute to the development of practical water resources projects by interpreting the results.
Course Content
Summarizing hydrologic and hydrometeorologic univariate data sets, graphical data analysis, describing uncertainty, hypothesis tests, testing differences between two independent groups, multiple comparisons of catchment data, correlation and simple regression analysis for completing missing data, multiple regression for hydrological modelling, trend and stationarity analysis, interpreting efficiency of observation numbers in estimation (power calculations for tests))
Course Precondition
In order to enroll this course, it is sufficient to be a graduate or doctoral student. There are no other prerequisites.
Resources
Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3, 458 p., https://doi.org/10.3133/tm4a3.
Notes
a) Selected articles from national and international journals. User manuals of softwares such as SPSS and MINITAB. b) https://www.sciencebase.gov/catalog/item/5bf30260e4b045bfcae0c205 c) Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical Methods in Water Resources - Supporting Materials: U.S. Geological Survey data release, https://doi.org/10.5066/P9JWL6XR.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Interprets descriptive statistics of hydrological and hydro-meteorological data for project purposes. |
| LO02 | Identifies possible uncertainties by analyzing hydrological data graphically. |
| LO03 | Determines the difference between two independent groups by performing hypothesis tests. |
| LO04 | Decides the homogeneity of the basins by making multiple comparisons on a catchment basis. |
| LO05 | Gains the ability to make hydrological predictions by using multiple regression models as well as correlation and regression analyzes. |
| LO06 | Determines quantitatively the effects of climate change on the trends of hydrological data and applies them to water resources projects. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Has the ability to develop and deepen the level of expertise degree qualifications based on the knowledge acquired in the field of agriculture and irrigation structures | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Has the ability to understand the interaction between irrigation and agricultural structures and related disciplines | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Qualified in devising projects in agricultural structures and irrigation systems. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Conducts land applications,supervises them and assures of development | |
| PLO05 | Bilgi - Kuramsal, Olgusal | Has the ability to support his specilist knowledge with qualitative and quantitative data. Can work in different disciplines. | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Solves problems by establishing cause and effect relationship | 5 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Has the ability to apply theoretical and practical knowledge in the field of agricultural structures and irrigation department | 3 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Able to carry out a study independently on a subject. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Has the ability to design and apply analytical, modelling and experimental researches, to analyze and interpret complex issues occuring in these processes. | 5 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Can access resources on his speciality, makes good use of them and updates his knowledge constantly. | 3 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Has the ability to use computer software in agricultural structures and irrigation; can use informatics and communications technology at an advanced level. | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Summarizing Univariate Data | Textbooks, articles and Internet resources | Öğretim Yöntemleri: Anlatım, Tartışma |
| 2 | Graphical Data Analysis | Textbooks, articles and Internet resources | Öğretim Yöntemleri: Beyin Fırtınası, Tartışma, Alıştırma ve Uygulama |
| 3 | Describing Uncertainty | Textbooks, articles and Internet resources | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | Hypothesis Tests | Textbooks, articles and Internet resources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Beyin Fırtınası |
| 5 | Testing Differences Between Two Independent Groups | Textbooks, articles and Internet resources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Paired Difference Tests of the Center | Textbooks, articles and Internet resources | Öğretim Yöntemleri: Anlatım, Benzetim |
| 7 | Comparing Centers of Several Independent Groups | Topics covered in the course, textbooks, articles, internet resources | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Örnek Olay |
| 8 | Mid-Term Exam | Topics covered in the course, textbooks, articles, internet resources | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Simple Linear Regression | Topics covered in the course, textbooks, articles, internet resources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme |
| 10 | Alternative Methods for Regression | Textbooks, articles and Internet resources | Öğretim Yöntemleri: Anlatım, Tartışma, Beyin Fırtınası |
| 11 | Multiple Linear Regression | Textbooks, articles and Internet resources | Öğretim Yöntemleri: Anlatım, Tartışma |
| 12 | Trend Analysis | Textbooks, articles and Internet resources | Öğretim Yöntemleri: Anlatım, Tartışma, Alıştırma ve Uygulama, Beyin Fırtınası |
| 13 | How Many Observations Do I Need? | Textbooks, articles and Internet resources | Öğretim Yöntemleri: Anlatım, Beyin Fırtınası, Örnek Olay |
| 14 | Discrete Relations | Textbooks, articles and Internet resources | Öğretim Yöntemleri: Anlatım, Beyin Fırtınası, Örnek Olay |
| 15 | Regression for Discrete Responses and Presentation Graphics | Textbooks, articles and Internet resources, supplemental resources | Öğretim Yöntemleri: Anlatım, Tartışma, Beyin Fırtınası |
| 16 | Term Exams | Topics covered in the course, textbooks, articles, internet resources | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Topics covered in the course, textbooks, articles, internet resources | 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 | 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 | ||