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 |
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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 |
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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 |
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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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為確保您我的權利,請尊重智慧財產權及不得非法影印
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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 |
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Office Hours |
另約時間 |
指定閱讀 |
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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) |
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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)
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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 |
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