ENVS 550/550L -
CSU Monterey Bay.
Copyright (C) Fred Watson & past co-instructors.
Course Description from Catalog
Covers the concepts of scientific method (theories, propositions, hypotheses, etc.); how to design research questions; practical techniques of experimental design, statistical inference, and information theoretic methods; successful proposal; and communication of scientific results. Emphasizes effective linkage between science policy with respect to achieving levels of scientific quality, clarity, and relevance appropriate to real-world situations.
Instructors, time, location
Dr Fred Watson
Chapman Science Academic Center, Room E112, (831) 582-4452, fwatson@csumb.edu
Office hours: By appointment.
Second instructor TBD.
Tuesdays & Thursdays 9:00 – 11:50. Chapman Science Academic Center S119
Prerequisites

Instructor consent; Postgraduate course. Undergraduates may apply.
Required text

Gotelli, N.J. and Ellison, A.M.. 2004. A Primer in Ecological Statistics. 1 st edition. Sinauer Associates. ISBN 978-
0878932696.
Useful texts

Burham, K.P. & Anderson, D. (2003). Model Selection and Multi-Model Inference. 2 nd edition. Springer. ISBN 978-
0387953649

Hilborn, R. and M. Mangel. 1997. The Ecological Detective. Confronting Models with Data, Princeton University Press,
ISBN 0-691-03497-4

Valiela, Ivan. 2001. Doing Science. Design, Analysis, and Communications of Scientific Research. Oxford University Press.
ISBN 0-19-513413-3

Ford, E. David. Scientific Methods for Ecological Research. Cambridge University Press. p. 564. ISBN 0-521-66973-1.

Dalgaard, Peter. 2002. Introductory Statistics with R. Springer Press. ISBN 0-387-95475-9

Venables W.N. and B.D. Ripley. 2002. Modern Applied Statistics with S. Fourth Edition. Springer Press. ISBN 0-387-
95457-0.
Relation of Course to AMWS Major Learning Outcomes:
This course satisfies MLO 2 within the Applied Marine and Watershed Science (AWMS) Program at CSUMB: “Research Methods -
Design, conduct, analyze, and communicate the findings of a science or social science investigation based upon a scholarly review of the relevant literature ” .
Course Outcomes

Asterisk (*) denotes areas that may receive less emphasis, depending on instructor
1.
Learning Outcome 1 (Theory of knowledge) a.
Define scientific method in terms of postulates, hypotheses, evidence, and inference ( sensu Ford). b.
* Explain inductive reasoning.
2.
Learning Outcome 2 (Scientific Method) a.
Write clear, testable null and alternative hypotheses – under a null-hypothesis-testing framework b.
Write clear, comparable competing hypotheses – under a model-comparison framework c.
Critically review scientific method in published literature
3.
Learning Outcome 3 (Experimental and Sampling Design and Data Management) a.
* Design experimental and monitoring sampling schemes appropriate to given hypotheses. b.
Use uncertainty in the design and analysis of field studies. c.
* Critically review experimental design in published literature
4.
Learning Outcome 4 (Statistics)

a.
Define probability & use probabilities in algebra b.
Determine the probability of events given discrete data sets. c.
Explain what a probability density function (PDF) is d.
Explain characteristics and use of different probability distributions, e.g. normal, lognormal, Poisson, binomial e.
Compute measures of central tendency and spread and interpret their meaning in the context of normally and nonnormally distributed data sets. f.
Determine confidence intervals from environmental data sets. g.
Describe conceptually and mathematically Type I and Type II errors h.
Write equations for generalized linear models. i.
Describe, perform, and interpret a regression analysis using simple linear models and generalized linear models (e.g. j.
k.
logistic regression)
*
*
Describe and calculate goodness of fit measures such as the sum-of-squared-errors
Describe and calculate likelihood l.
Use AIC tables to compare models and draw appropriate inference m.
* Describe, perform, and interpret an ANOVA analysis n.
* Perform and interpret one-way, two-way, and mixed effects ANOVA. o.
p.
* Use post-hoc tests to distinguish between treatments
* Use uncertainty to calculate the power of an analysis
5.
Learning Outcome 5 (Programming with R) a.
Describe the basics of the R programming environment b.
Start R and follow an introductory session of R c.
Import data from text, flat files, and excel files as vectors and data frames in R. d.
* Manipulate data in order to be analyzed (create factors, indexing, sub-setting, sorting, splitting, and merging) e.
* Us R to coerce objects, use functions, create formulae, maintain program control and use loops. f.
Generate histograms of data g.
Generate density functions, overlying with probability distributions. h.
* Generate box plots and interpret and adjust whiskers. i.
Generate scatter plots using plot() and xyplot() j.
* Create an ANOVA table. k.
Develop appropriate statistical modeling code to analyze data using R. l.
Create publication-quality graphics using R.
6.
Learning Outcome 6 (Scientific Styles of Communication) a.
Students will be able to write scientific papers and using scientific writing style. b.
Students will be able to produce graphical representations of figures that conform to scientific and journal article conventions.
7.
Learning Outcome 7 (Following instructions) a.
Be able to follow instructions (e.g. assignment instructions; analogous in the professional world to project briefs and scopes of work)
Assessment
This course must be taken for a letter grade. Student learning will be assessed through a combination of assignments, quizzes, and participation. Assignments must be submitted electronically through Blackboard. The assessment breakdown is as follows:
Item
Assignments
(Will most likely be about 7 or 8 assignments lasting 1 to 2 weeks, and then
N
~9
1 or 2 longer ones lasting several weeks)
Attendance
Quizzes (There may be one or two quizzes) 1
Total
Percentages of total assessment
Between 5% and 20% each, for 75% to 95% total
5%
0% or 20%
100%
You are expected to have a good understanding of Excel 2010, Word 2010, PowerPoint 2010, and Acrobat Standard and you should be able to utilize and combine all of these programs to create a single PDF document for the completion of your course assignments.
Most of the assessment will be based around assignments . Many of these will last about one to two weeks, and will be designed to help you develop skills in scientific method, data manipulation, statistics, R, problem solving, and scientific writing. One or two of the assignments will be longer projects involving reviews of methods in the literature and/or application of advanced methods or basic methods at a more advanced level.
There may be one or two quizzes that will primarily test your working knowledge of statistics using R independently of help from others.

C+
C
D+
D
F
Penalties:

Late assignments: At least 1% of total course grade will be deducted for each 24 hours late, starting immediately after the deadline (e.g. 6hrs late = 0.25% total course penalty), unless prior arrangements have been made with the relevant instructor.

Quizzes: If a quiz date is missed and must be re-scheduled, the subsequent score will be reduced by 20%. It is the student’s responsibility to make any re-scheduling arrangements before the originally scheduled date.

Attendance: Attendance is expected, and considered essential to success in the course. Missing a day or two for various reasons is understandable, but on the third day, 1% of total course score may be deducted per day missed. Being consistently late to class could also attract a penalty. Approximately 0.5% of the score may also be associated with completing a student evaluation of the class and the instructor at the end of semester.

Exceptions: Exceptions require a doctor’s letter or equivalent evidence pertinent to the circumstance.
Grading Philosophy : Grades are linked to course learning outcomes.
Grading basis will be approximately as follows. The scores listed are thresholds that need to be reached; scores will not be rounded up.
Grade
A+
A
A-
B+
B
B-
Score
98%
92%
90%
88%
82%
80%
78%
70%
68%
60%
<60%
Writing
Good writing skills are extremely important. Several assignments require professional-quality writing and report preparation and will be assessed as such.
On R
R is a free, open source and powerful statistics program! But like many computer programs, it is a new language.
So…we’ll learn it. For more information on R, see http://www.r-project.org/
If you have access to a computer then download and install R or use the computer rooms S117 and S119 when it is available.
Accommodation of Students with Disabilities
CSUMB welcomes and accommodates students with disabilities as part of campus diversity and for legal compliance. Students with disabilities who may need accommodations please contact an instructor by the
Add/Drop deadline; usually this the 2 nd Friday of semester). Also, contact:
Student_Disability_Resources@csumb.edu, Building 47, Student Services, First Floor, Phone: 831/582-3672 voice, or 582-4024 fax/TTY http://sdr.csumb.edu/.
Class Policy on Academic Honesty
1.
It is the responsibility of the students in this class to familiarize themselves with the University Policy on
Academic Integrity: a.
https://csumb.edu/policy/academic-integrity-policy
2.
The plagiarism detection software Turnitin.com may be used in this class.
Academic Integrity is extremely important. Don’t cheat. Don’t copy someone else’s text or data without attribution. Accidental plagiarism is still plagiarism, and it leads to heavy penalties with lasting impacts.
Schedule
The schedule of class session topics, assignments, quizzes etc is posted on the internal class web site, and is subject to change.