課程名稱 
生態研究法 Research Methods in Ecology 
開課學期 
1092 
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。 總人數上限：16人 
Ceiba 課程網頁 
http://ceiba.ntu.edu.tw/1092ENT5053 
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 consists of two parts: 1) experimental design and 2) data analysis. The experimental design part of the course largely (but not completely) follows a textbook (Ruxton and Colegrave 2016; see below for the reference infomation). 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 methods, 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.rproject.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 independently.
Some course materials (R scripts, optional readings, etc.) will be sent by email. Students are responsible for checking their NTU email accounts regularly.
Bonus points
Bonus points may be awarded based mainly on attendance and participation. Two tardies are equivalent to one absence, and two absences will result in 0 bonus points. Poor class participation (e.g., playing with a cell phone/computer, sleeping, etc.) is considered as an absence. Even when a student has a valid reason for an absence, the absence is not excused because attendance only affects bonus points. The maximum possible bonus points are 10% (in the final % grade), but undergraduate students whose grades are less than 60% (final grade without bonus points) can get at most 60% (final grade with bonus points). Graduate student whose grades are less than 70% (final grade without bonus points) can get at most 70% (final grade with bonus points).
The detail about how bonus points affect the final grade may change. If it changes, the change will not negatively influence grades (e.g., each student would receive 0 or positive increase). But students should not expect a change to take place.
Assignments
Assignments only influence bonus points. The emphasis will not be on the correctness of the R scripts. Students should not attempt to get solutions from classmates. Assignments that are obviously copied (including the original) may reduce bonus points. Late assignments will not be accepted.
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 (e.g., null hypothesis significance testing) 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 
另約時間 備註： (by appointment) 
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/10.1007/9780387790541
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/10.1007/9780387874586
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 
2/25 
Course overview<br/>
Statistics review 
Week 2 
3/04 
Starting with a welldefined 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 
3/11 
Betweenindividual variation, replication and sampling<br/>
Pseudoreplication<br/>
Power analysis 
Week 4 
3/18 
Different experimental designs 
Week 5 
3/25 
Taking measurements 
Week 6 
4/01 
no class (溫書假) 
Week 7 
4/08 
Exam 1 
Week 8 
4/15 
Sum of squares<br/>
Numerical optimization<br/>
Bootstrap 
Week 9 
4/22 
Maximum likelihood<br/>
Likelihood ratio tests 
Week 10 
4/29 
Maximum likelihood review 
Week 11 
5/06 
Generalized Linear Models (GLMs)<br/>
Poisson GLM<br/>
Dummy variables 
Week 12 
5/13 
Binomial GLM<br/>
Offset<br/>
Gamma GLM 
Week 13 
5/20 
Overdispersion<br/>
Quasilikelihood<br/>
Negative binomial GLM 
Week 14 
5/27 
Customizing models 
Week 15 
6/03 
No class 
Week 16 
6/10 
Generalized Linear Mixed Models (GLMMs) 
Week 17 
6/17 
Exam 2 (tentative) 
