課程資訊
課程名稱
 生態研究法
Research Methods in Ecology 
開課學期
 105-2 
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
 星期四2,3,4(9:10~12:10) 
上課地點
  
備註
 本課程以英語授課。上課教室：永齡生醫工程館421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/1052ENT5053 
課程簡介影片
 
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課程概述

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

  • 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 (see below).
  • Assignments will be given nearly every week. Students must work on assignments on their own. Understanding a solution and deriving it by yourself are not the same (especially for programming). To discourage copying assignments (that has been very common in past years), assignments are not graded by the instructor. That is, students are not asked to submit their assignments. Therefore, even if students copy and have the perfect assignments, the assignments have no influence on their grades. Nonetheless, successful completions of the assignments are essential for the successful completion of the course. Students are encouraged to seek out the instructor for help when they have troubles completing assignments. Grading Exam 1 40% Exam 2 60% (cumulative) Bonus points Bonus points may be added to the final grades at the end of the semester. Bonus points will be calculated based on attendance and participation (e.g., asking questions). Two absences or four tardinesses will make 0 bonus points (a tardiness over 20 min is considered as an absence). Poor class participation (e.g., playing with a cell phone/computer, sleeping, etc.) is considered as an absence. Because these are bonus points (e.g., without them, it is still possible to get 100% in the course), even when students have a valid reason for, e.g. an absence, it is still considered an absence. The maximum possible bonus points is 10%, but students whose grade is less than 60% without a bonus point can get at most 60% in their final grades no matter how good attendance and participation are. In addition, graduate students whose grade is less than 70% without a bonus points can have at most 70% no matter how many bonus points they may have. Schedule The schedule (shown in the content section of this website) is subject to change. 

    • 課程目標
    		 . 
    課程要求
    		  
    預期每週課後學習時數
    		  
    Office Hours
    		 另約時間 
    指定閱讀
    		  
    參考書目
    		 待補 
    評量方式
    (僅供參考)
    		    
    課程進度
    週次
    日期
    單元主題
    Week 1
    		 2/23  Course overview<br/>
    Statistics review 
    Week 2
    		 3/02  Starting with a well-defined hypothesis  
    Week 3
    		 3/09  Between-individual variation, replication and sampling  
    Week 4
    		 3/16  Different experimental designs 
    Week 5
    		 3/23  Taking measurements  
    Week 6
    		 3/30  Review 
    Week 7
    		 4/06  Exam 1 
    Week 8
    		 4/13  Sum of squares<br/>
    Numerical optimization<br/>
    Bootstrap  
    Week 9
    		 4/20  Maximum likelihood<br/>
    Likelihood ratio tests 
    Week 10
    		 4/27  Maximum likelihood review
     
    Week 11
    		 5/04  Generalized Linear Models (GLMs)<br/>
    Poisson GLM<br/>
    Dummy variables 
    Week 12
    		 5/11  Binomial GLM<br/>
    Offset<br/>
    Gamma GLM 
    Week 13
    		 5/18  Overdispersion<br/>
    Quasilikelihood<br/>
    Negative binomial GLM 
    Week 14
    		 5/25  Customizing models 
    Week 15
    		 6/01  Generalized Linear Mixed Models (GLMMs) 
    Week 16
    		 6/08  Review 
    Week 17
    		 6/15  Exam 2 
    Week 18
    		 6/22  TBA