課程名稱 |
生態研究法 Research Methods in Ecology |
開課學期 |
108-2 |
授課對象 |
生物資源暨農學院 昆蟲學系 |
授課教師 |
奧山利規 |
課號 |
ENT5053 |
課程識別碼 |
632EU1150 |
班次 |
|
學分 |
3.0 |
全/半年 |
半年 |
必/選修 |
選修 |
上課時間 |
星期四6,7,8(13:20~16:20) |
上課地點 |
|
備註 |
本課程以英語授課。上課教室:鄭江樓 505室。建議先修習基礎統計學。 限學士班三年級以上 總人數上限:16人 |
Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1082ENT5053 |
課程簡介影片 |
|
核心能力關聯 |
本課程尚未建立核心能力關連 |
課程大綱
|
為確保您我的權利,請尊重智慧財產權及不得非法影印
|
課程概述 |
This course is not restricted to students of entomology. Students from any departments can take this course. If unable to register online, please contact the instructor or simply show up to the first class.
This course has two parts: 1) experimental design and 2) data analysis. The experimental design part of the course will use a textbook (Ruxton and Colegrave 2016; see below). The main theme of the data analysis part is the method of maximum likelihood although other approaches are also discussed. Computer simulations will be used to understand the concepts of various statistical tests, but no prior experience in programming is required.
Although the course title contains the word 'ecology', this is a general course on experimental design and data analysis. Students of any fields (social science, political science, physical science, biological science, business, engineering, etc.) can take the course. No knowledge of ecology is required.
The computer language R (http://www.r-project.org/) is used.
Expectations
Ask questions, in or out of class, when you don’t understand something. If you are confused, you are probably not the only one.
Assignments will be given nearly every week. Students must work on assignments on their own.
Some information regarding the course (R scripts, optional readings, etc.) will be sent by email. Students are responsible for checking their NTU email accounts regularly.
Bonus points
TBA
Schedule
The schedule (shown in the content section of this website) is subject to change. |
課程目標 |
Students completing this course will:
understand how typical statistical tests work.
be able to program in R.
be familiar with common statistical models (e.g., generalized linear [mixed] models) and methods.
be able to build own statistical models when common models are not appropriate. |
課程要求 |
|
預期每週課後學習時數 |
|
Office Hours |
|
指定閱讀 |
|
參考書目 |
Selected chapters from...
Ruxton DG and N Colegrave. (2016) Experimental Design for the Life Sciences.
Fourth edition. Oxford University Press, Oxford, UK.
Hilborn R and M Mangel. (1997) The Ecological Detective: Confronting Models with
Data. Princeton University Press, Princeton, NJ.
Dalgaard P (2008) Introductory Statistics with R. Second edition. Springer, New
York, NY.
http://dx.doi.org/10.1007/978-0-387-79054-1
Zuur AF, EN Ieno, N Walker, AA Saveliev, GM Smith. (2009) Mixed Effects Models
and Extensions in Ecology with R. Springer, New York, NY.
http://dx.doi.org/10.1007/978-0-387-87458-6
Crawley MJ. (2012) The R Book. John Wiley & Sons, Chichester, UK.
https://onlinelibrary.wiley.com/doi/book/10.1002/9781118448908
|
評量方式 (僅供參考) |
|
週次 |
日期 |
單元主題 |
Week 1 |
3/05 |
Course overview<br/>
Statistics review |
Week 2 |
3/12 |
Starting with a well-defined hypothesis </br>
Selecting the broad design of your study<br/></br>
The sections on "Indirect measures" and "Controls" will be discussed in a different week. |
Week 3 |
3/19 |
Between-individual variation, replication and sampling<br/>
Pseudoreplication<br/>
Power analysis |
Week 4 |
3/26 |
Different experimental designs |
Week 5 |
4/02 |
no class (spring break) |
Week 6 |
4/09 |
Taking measurements |
Week 7 |
4/16 |
Exam 1 |
Week 8 |
4/23 |
Sum of squares<br/>
Numerical optimization<br/>
Bootstrap |
Week 9 |
4/30 |
Maximum likelihood<br/>
Likelihood ratio tests |
Week 10 |
5/07 |
Maximum likelihood review |
Week 11 |
5/14 |
Generalized Linear Models (GLMs)<br/>
Poisson GLM<br/>
Dummy variables |
Week 12 |
5/21 |
Binomial GLM<br/>
Offset<br/>
Gamma GLM |
Week 13 |
5/28 |
Overdispersion<br/>
Quasilikelihood<br/>
Negative binomial GLM |
Week 14 |
6/04 |
Customizing models |
Week 15 |
6/11 |
Generalized Linear Mixed Models (GLMMs) |
Week 16 |
6/18 |
Exam 2 |
|