課程資訊
課程名稱
 生態研究法
Research Methods in Ecology 
開課學期
 103-2 
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 
全/半年
 半年 
必/選修
 必修 
上課時間
 星期三2,3,4(9:10~12:10) 
上課地點
  
備註
 本課程以英語授課。B群組。上課教室：自動化中心生物學館。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/1032ecol_data 
課程簡介影片
 
核心能力關聯
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課程概述

The classroom is located in this building. http://map.ntu.edu.tw/ntu-eng.html?layer=build&uid=AT3008&scale=16 The instructor's office is located in this building. http://map.ntu.edu.tw/ntu-eng.html?layer=build&uid=AT6004&scale=16 This is a course in experimental design and data analysis. Computer simulations will be used to understand the concepts of various statistical tests, but no prior experience in programming is required. The experimental design part of the course will use a textbook (see below). The main theme of the data analysis part of the course is the maximum likelihood method although other approaches are also discussed. The computer language R (http://www.r-project.org/) will be used. Expectations

  • Attend and participate in class; do the reading and all the assignments. Two unexcused absences or four tardiness will make the class participation grade 0. Make your absence arrangement before the class if possible.
  • Ask questions, in or out of class, when you don’t understand something. There is no such thing as a stupid question, and if you are confused you are probably not the only one. Asking questions in class is probably the best way to increase your class participation points. Assessment Class participation (including [pop] quizzes) (10%) Exam 1 (30%) Exam 2 (40%) Assignments (20%) If we curve final grades (may or may not happen), attendance and participation will heavily weigh how they are curved. Policies
  • You will be asked to submit most of the assignments via CEIBA (this web site). Late assignments will not be accepted. 

    • 課程目標
    		 . 
    課程要求
    		 Prerequisite is a course in statistics (e.g., should know t-test, etc.). 
    預期每週課後學習時數
    		  
    Office Hours
    		 另約時間 
    指定閱讀
    		  
    參考書目
    		 Dalgaard, P (2008) Introductory Statistics with R. Second edition. Springer, New
    York, NY. 
    評量方式
    (僅供參考)
    		    
    課程進度
    週次
    日期
    單元主題
    Week 1
    		 2/25  Course overview<br/>
    Statistics review<br/>

    <br/>
    <b>Assignment</b>
    <li>R tutorial (rtutorial.pdf) 
    Week 2
    		 3/04  Starting with a well-defined hypothesis  
    Week 3
    		 3/11  Between-individual variation, replication and sampling  
    Week 4
    		 3/18  Different experimental designs 
    Week 5
    		 3/25  Taking measurements  
    Week 6
    		 4/01  No class  
    Week 7
    		 4/08  Review 
    Week 8
    		 4/15  Exam 1 
    Week 9
    		 4/22  Sum of squares<br/>
    Optimization<br/>
    Bootstrap  
    Week 10
    		 4/29  Maximum likelihood<br/>
    Likelihood Ratio Test  
    Week 11
    		 5/06  MLE review<br/>
    Generalized Linear Models (GLMs)
     
    Week 12
    		 5/13  Poisson GLM<br/>
    Dummy variables (Review) 
    Week 13
    		 5/20  Binomial GLM<br/>
    Offset<br/>
    Gamma GLM 
    Week 14
    		 5/27  Overdispersion<br/>
    Quasilikelihood 
    Week 15
    		 6/03  Distributions for overdispersed data<br/>
    Customizing models 
    Week 16
    		 6/10  Practice test 
    Week 17
    		 6/17  Review 
    Week 18
    		 6/24  Exam 2