課程資訊
課程名稱
 生態研究法
Research Methods in Ecology 
開課學期
 102-2 
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 
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
 上課教室：自動化中心。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/1022ecol_data 
課程簡介影片
 
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課程概述

This course is about conducting experiments and analyzing data in a broad sense. The emphasis may be on ecology, but the materials covered in the course are general and are useful for any disciplines. The course consists of two parts: (1) experimental design and (2) data analysis. In the experimental design part, we will discuss some of the common pitfalls in experimental designs. In the data analysis part of the course, we will cover several statistical methods which allows students to develop their own models without being restricted by the assumptions of commonly used statistical models. 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 will make the class participation portion of your 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’re confused you’re probably not the only one. Policies
  • Submit your assignments on time. You will be asked to submit most of the assignments in class via CEIBA (this web site). Late assignments will be penalized: one day (20% penalization), two days (30% penalization), and three days or more (100% penalization).
  • In this course, you are allowed to discuss the assigned problems with other students in your class. However, you must write the solutions in your own words without referring to any other students' work. The copying or even paraphrasing of anyone's solutions will be considered academic dishonesty. Assessment Class participation (including [pop] quizzes) (20%) Exams (40%) Assignments (40%) 

    • 課程目標
    		 Students will develop
  • common sense in experimental design
  • programming skills
  • data analysis skills 
    • 課程要求
    		  
    預期每週課後學習時數
    		  
    Office Hours
    		  
    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
    		 2/20  Course overview<br/>
    Statistics review

    <br/><br/>
    <b>Assignments</b>
    <li> Read Johnson (1999)
    <li> Complete the R tutorial 
    Week 2
    		 2/27  Starting with a well-defined hypothesis

    <br/><br/>
    <b>Assignments</b>
    <li> Read Chapter 2 (Ruxton and Colegrave)
    <li> Assignment 1 
    Week 3
    		 3/06  Between-individual variation, replication and sampling

    <br/><br/>
    <b>Assignments</b>
    <li> Read Chapter 3 (Ruxton and Colegrave)
    <li> Assignment 2 
    Week 4
    		 3/13  Different experimental designs

    <br/><br/>
    <b>Assignments</b>
    <li> Read Chapter 4 (Ruxton and Colegrave)
    <li> Assignment 3 
    Week 5
    		 3/20  Taking measurements


    <br/><br/>
    <b>Assignments</b>
    <li> Read Chapter 5 (Ruxton and Colegrave)
    <li> Assignment 4 
    Week 6
    		 3/27  Review 
    Week 7
    		 4/03  Spring break (no class) 
    Week 8
    		 4/10  Exam 1 
    Week 9
    		 4/17  Sum of squares<br/>
    Optimization<br/>
    Bootstrap

    <br/><br/>
    <b>Assignments</b>
    <li>Read Chapter 5 (Hilborn and Mangel)
    <li>Assignment 5  
    Week 10
    		 4/24  Maximum likelihood<br/>
    Likelihood Ratio Test

    <br/><br/>
    <b>Assignments</b>
    <li> Read Chapter 7 (Hilborn and Mangel)
    <li> Assignment 6 
    Week 11
    		 5/01  Generalized Linear Models (GLIMs)<br/>
    Poisson GLM<br/>
    <br/>

    Assignment 7 
    Week 12
    		 5/08  Binomial GLM<br/>
    Offset<br/>
    Gamma GLM<br/>
    <br/>
    Assignment 8 
    Week 13
    		 5/15  Overdispersion<br/>
    Quasilikelihood<br/>
    Negative Binomial GLM<br/>
    <br/>
    <b>Assignments</b>
    <li>Assignment 9
    <li>Read O'Hara and Kotze (2010) 
    Week 14
    		 5/22  Customizing models
    <br/>
    Assignment 10 
    Week 15
    		 5/29  Generalized Linear Mixed Models (GLMMs) 
    Week 16
    		 6/05  Review 
    Week 17
    		 6/12  No class (self study) 
    Week 18
    		 6/19  Exam 2