課程資訊
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  昆蟲學研究所  
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
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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)
    		    
    課程進度
    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