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Special Topics in Environmental Sciences - Biostatistics

Course Number: ENS 798
Credits: 3
Professor: Dr. Matthew Aiello-Lammens
Meeting day and time: Mon 5:40 - 8:40 PM
Term: Spring 2016
Location: Environmental Center Classroom

Course description

This special topics course will focus on techniques required to properly analyze ecological data. Students will work with standard datasets, as well as their own data. The students will learn basic exploratory and descriptive statistical techniques, as well as peer-review each others work, providing feedback on analysis methods. Individually, each student will focus on learning advanced analysis techniques required to address the uniqueness of their research questions and datasets. All coursework will be carried out using the freely available R statistical programming language.


Scientists need to analyze data in order to evaluate hypotheses and extract further insights from their observations. Further, the ability to analyze data collected during fieldwork is a key component of effective science communication. This course will provide students with these techniques. Additionally, the methods of biostatistical analysis are applicable in all fields relating to science, whether they are applied directly when analyzing data or indirectly by providing a structure to interpret scientific findings.

Learning objectives

  • How to perform exploratory data analysis.
  • How to evaluate data quality.
  • Methods to visualize patterns in data.
  • How to perform standard data analysis techniques, including linear regression, ANOVA, and generalized linear modeling.
  • How to identify the most appropriate analysis techniques to use with a given dataset.
  • How to use knowledge of standard data analysis techniques to effectively choose and apply more advanced methods.

Required materials

  • Quinn and Keough. 2009. Experimental Design and Data Analysis for Biologists. Cambridge Press
  • Logan. 2010. Biostatistical Design and Analysis Using R: A Practical Guide. Wiley-Blackwell


Problem sets - 30%
Two exams - 40% (20% each)
Written analysis report - 30%

Written analysis report

The written analysis report will be a document that describes the methods used to analyze the students data, the analysis results, and all figures and tables associated with the analysis. This paper is functionally equivalent to the Methods and Results sections of a paper following standard scientific paper format.

Course schedule

This is a guide for the material we will cover during the course, however it may be amended during the semester as needed. Note that the Reading column indicates chapters in which material relavant to our topic can be found. I do not expect you to read all of what is listed here, but rather am pointing to the chapters most relevant for helping you solidify your knowledge of what we cover in class.

Date Topic Reading Lecture Notes Assignment
1/25 Introduction to Biostatistics and R Q&K Chap. 1; Logan Chap. 1 Lecture notes 1; In class R file Problem Set 1
2/1 Data Exploration and Probability Distributions Q&K Chap. 1 & 4; Logan Chap. 3 & 5 Lecture notes 2; In class R file  
2/8 Probability Distributions and Estimation Q&K Chap. 2 ; Logan Chap. 3 Lecture notes 3; In class R file Problem Set 2
2/15 NO CLASS      
2/22 Distributions and Hypothesis Testing Q&K Chap. 2; Logan Chap. 3 Lecture notes 4; In class R file Problem Set 3
2/29 Estimation, Hypothesis Testing Q&K Chap. 3; Logan Chap. 6 Lecture notes 5; In class R file  
3/7 Hypothesis Testing, Non-parametric Methods, Introduction to Linear Models Q&K Chap. 3; Logan Chap. 6 Lecture notes 6; In class R file  
3/14 Correlation and Regression   Lecture notes 7; Lecture notes 7 - addition; In class R file Take-home Midterm
3/21 NO CLASS      
3/28 Regression and Multiple Regression Q&K Chap. 5 & 6; Logan Chap. 8 & 9 Lecture notes 8; Lecture notes 8 - addition; In class R file Problem Set 4
4/4 Review of Midterm; Special Lecture      
4/11 Review of Midterm; Multiple and complex regression; ANOVA Q&K Chap. 6; Logan Chap. 9 Lecture notes 9  
4/18 Complex regression; ANOVA (cont’d) Q&K Chap. 6; Logan Chap. 9 Lecture notes 10; In class R file Problem Set 5
4/25 Nested and Factorial ANOVA Q&K 8 & 9; Logan 11 & 12 Lecture notes 11; In class R file Problem Set 6
5/2 ANCOVA; Analysis of Frequencies; GLM; wrap-up Logan Chap. 15-17; Q&K 12-14 Lecture notes 12; In class R file Take-home Final (Due May 11, 2016)
5/9 Project Presentations Correlation   PROJECT PAPERS DUE

Data sets

  • M&M color counts from our bags during class 1: class_mm_data.csv
  • Logan (2010) datasets can be found here.

Accommodations for Students with Disabilities

The University’s commitment to equal educational opportunities for students with disabilities includes providing reasonable accommodations for the needs of students with disabilities. To request an accommodation for a qualifying disability, a student must self-identify and register with the Coordinator of Disability Services for his or her campus.
No one, including faculty, is authorized to evaluate the need and arrange for an accommodation except the Coordinator of Disability Services. Moreover, no one, including faculty, is authorized to contact the Coordinator of Disability Services on behalf of a student.
For further information, please see Information for Students with Disabilities on the University’s web site.