Course Syllabus

Environmental Statistics I (MEES 698B)

Fall 2015 (see schedule)


Dong Liang
Slava Lyubchich


Monday, Wednesday 10:00–11:30 a.m. IVN (?)

Course Objective:

This course will extend the quantitative training for students in the environmental sciences. It will explore the basic practices of statistics to inter-disciplinary environmental data. The goal is to train students with the statistical knowledge and tools needed to conduct statistical analysis in their own research. The statistical programming language R is used in class, to complete homework sets, and to analyze online data.

Course Contents:

  1. Analyzing data using R

  2. Producing data through sampling or experiments

  3. Making inference about variables, using t-tests

  4. Making inference about relationships, regression

  5. Introducing generalized linear and additive model

  6. Designing experiments and analyzing experimental data

  7. Introducing time series analyses

  8. Exploring spatial and Bayesian analyses

Required Textbook:

Given the diverse topics covered in this course, we recommend a general "how to" book in R. The statistical contents would be illustrated using real data examples, delivered through handouts, and supplemented by suggested readings from the following e books accessible from the libraries.

Crawley, M. J. 2007. The R Book. John Wiley and Sons. ISBN 9780470510247

Reference Textbooks:

Oehlert, G. W. 2000. First Course in Design and Analysis of Experiments

Chatterjee, S., Hadi, A. S. and Price, B. 2000. Regression Analysis by Example

Lane et al. 2013 Introduction to Statistics. WEB

Brockwell P. J. and Davis R. A. 2002. Introduction to time series and forecasting. 2nd ed. Springer: New York.

Shumway, R. H. and Stoffer D. S. 2014. Time series analysis and its applications. 3rd ed.

R Reference Websites:

Grading and Philosophy for the Class:

Grades will be based on performance of two take home exams, and an individual project and homework problem sets. The exams and individual project will represent 30% of the grade. The homework problem sets will make up the remaining 10%. In cases where students are borderline between lower and higher grades, a high level of participation in the class discussions and class in general will win the day for the higher grade.

Homework problems are essential to understanding of the materials. Although the homework comprises only 10% of the final grade, performance on the exams is usually correlated with effort on the homework problems.

Whereas plagiarism will not be tolerated, students are encouraged to work together to learn from one another and solve problems in a collaborative and collegial way (aside from the take home exam).

Distribution of Class Materials:

We will be using the distance learning tool, Moodle for storing and disseminating class information – class notes, R code and output, assigned readings, and even discussion threads if you wish. Each student will be given a personal login and password to access the site. Materials for the next class will be posted no later than 12 hours before the beginning of the class. You are strongly encouraged to download and bring the R code and output to each class as these are critical components of the lectures and may be hard to follow without having these in front of you.

For the first several class periods, we will email reminders to get the info for class and where the info will be located. Please bookmark the Moodle site ( in your web browser so that you can rapidly get there.

Fall Semester 2015 Academic Calendar

Start Date August 31 (Monday)

Labor Day September 7 (Monday)

Thanksgiving Recess November 26 - 29 (Thursday - Sunday)

Last Day of Classes December 11 (Friday)

Final Project December 14 - 18

Tentative Course Outline



Graphical exploration and tabular display of data

Lane Ch2

Estimation & R intro

Lane Ch3

Normal distribution

Lane Ch7

Correlation, partial correlation

Lane Ch4

Simple Linear Regression

Lane Ch14


Lane Ch6


Lane Ch6

Probability and Sampling Distribution

Lane Ch5,9

Confidence Interval

Lane Ch 10

Hypothesis Testing

Lane Ch11

Inference for Linear Regression

Chatterjee Ch 2

Multiple Linear Regression

Chatterjee Ch 3

Regression Diagnostics

Chatterjee Ch 4

Transformation of Variables

Chatterjee Ch 6

Experimental design, Power analysis

Oehlert Ch 2

1-way ANOVA

Oehlert Ch 3

ANOVA - Assumptions and heterogeneous variances

Oehlert Ch 6

ANOVA - Multiple comparisons tests

Oehlert Ch 5

ANOVA – blocking designs and random effects

Oehlert Ch 13

ANOVA – 2 way & factorial

Oehlert Ch 8

ANOVA – nested effects and subsamples

Oehlert Ch 12

ANOVA – split plots, etc.

Oehlert Ch 16

ANOVA – repeated measures

Oehlert Ch 16

Introduction to geostatistics

Introduction to time series, Smoothing

Brockwell & Davis Ch 1

Regression on time series

Shumway Ch 2

Introduction to Bayesian statistics

Introduction to Mixed effects models

Class presentations

Individual project*

  1. Decide on a series of questions of interest and the associated hypotheses and predictions that you will attempt to test and answer with inferential statistics covered in class.

  2. Design an experiment/study or analysis (if using an existing dataset) to answer these questions.

  3. Identify and obtain or generate a dataset to analyze.

  4. Analyze the data and prepare a report as you would for the scientific journal ‘Ecology’. Include in the Discussion a section on how you might better design the study/experiment if you had the opportunity to do things over again.

    Report limited to 10 double spaced pages of text (including literature cited) with 1” margins and 12 pt font. Title page, tables, and figures are in addition to 10 page limit. Be concise yet informative, organized, and well written.

    Everyone will have a chance to present their project findings in a standard 15 minute talk format (12 minute talk, 3 minutes for questions) the last day of class. This should be a good exposure to giving talks at scientific meetings, but you’ll be among friends in our case. The 15 minute limit will be rigidly enforced.

* Well done projects are sometimes good enough to publish or may become a chapter in your thesis, so keep this in mind during your project.