This course consists of two parts: 1) experimental design and 2) data analysis. The first part of the course on experimental design largely follows a textbook (Ruxton and Colegrave, 2016; see reference information below). The main focus of the data analysis part is the method of maximum likelihood, although other approaches will also be discussed. Computer simulations will be utilized to aid in understanding the concepts of various statistical methods, but prior experience in programming is not required.
Although the first part of the course on experimental design largely follows the textbook, not all topics in the book will be covered in lectures. However, it is expected (required) that students read the entire book. Some topics discussed in lectures may not be covered in the book. Simply studying the book will not be sufficient to do well on exams
Although the course title contains the word 'ecology', this is a general course on experimental design and data analysis. Students from any field (including social science, political science, physical science, biological science, business, engineering, etc.) can take the course. No prior knowledge of ecology is required.
The computer language R (http://www.r-project.org/) is used.
Ask questions, in or out of class, if you do not understand something. If you are confused, you are probably not the only one who is.
Assignments will be given almost every week, and students must complete the assignments independently.
Some course materials, such as R scripts and optional readings, will be sent by email, and students are responsible for regularly checking their NTU email accounts.
Bonus points may be awarded primarily based on attendance and participation. Two tardies will count as one absence, and two absences will result in zero bonus points. Poor class participation, such as playing with a cell phone/computer, sleeping, etc., will be considered as an absence for the meeting. Even if a student has a valid reason for an absence, the absence is not excused since attendance only affects bonus points. The maximum possible bonus points are 10% of the final percentage grade.
The detail about how bonus points affect the final grade may change. If it changes, the change will not negatively influence grades (e.g., each student would receive 0 or positive increase). But students should not expect a change to take place.
Assignments only influence bonus points. The emphasis will not be on the correctness of the R scripts. Students should not attempt to get solutions from classmates or use AI. Cheating will result in 0 bonus points. Late assignments will not be accepted.
The schedule (shown in the Modules section of this website) is subject to change. For example, if all students in class struggle with some topics, we may repeat those topics until they are understood. Consequently, we may cover fewer topics than initially planned.
|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.
Zuur AF, EN Ieno, N Walker, AA Saveliev, GM Smith. (2009) Mixed Effects Models and Extensions in Ecology with R. Springer, New York, NY.
Crawley MJ. (2012) The R Book. John Wiley & Sons, Chichester, UK.