Department of Environmental Sciences

Huxley College of the Environment

Western Washington University

**ESCI 340 Biostatistical Analysis **

Instructor: John McLaughlin |
Teaching Assistant: Matt Sturza |

Office: ES 434 | Office: ES 303 |

Phone: 650-7617 | Phone: 650-4416 |

E-mail: | E-mail: sturzam[at]students[dot]wwu[dot]edu |

( Please do not send attachments in proprietary formats.) | |

Office Hours: MWF 10 | Office Hours: MWF 11-12 |

Course Web Site: http://faculty.wwu.edu/jmcl/Biostat/syl2016w.htm

http://larch.huxley.wwu.edu/Biostat/syl2016w.htm

Please note: some hyperlinks may not be activated until class time.

Text: (Recommended) J. H. Zar. 2010. *Biostatistical Analysis,*
5th^{th} ed. Prentice Hall

Additional readings available via links below.

Prerequisite: one year of general biology.

*It is easy to lie with statistics.
It is hard to tell the truth without statistics.*

**Course Description:**

This course is an introduction to data analysis and statistical tests commonly
used in the biological and environmental sciences. Much of the material will
be developed with a series of projects during which you will collect data
to address research questions and analyze those data using appropriate methods.
In a broad sense, the main objective of the course is to help you understand
the principles, methods, and limitations of data analysis. After successfully
completing the course, you should be able to identify appropriate applications
of common statistical methods, to perform the methods competently, and to interpret
statistical results critically.

**Course Evaluation:**

Grades will be based on homework assignments and three cumulative
examinations.
Homework assignments will comprise 50% of the course grade. The first two
exams will contribute 15% each toward the course grade. The third exam will
contribute the remaining 20%.

**Homework: **
Homework will be posted online to the links below,
generally before class time on Friday.

Assignments are due at the start of class the following Friday. To earn credit, meet the deadline.

**Homework Guidelines:
**(1) Be clear, neat, complete, and concise. The teaching assistant
has many assignments to grade;

it will be to your advantage to organize your work to make her job easier.

(2) Staple your work, if you submit more than one page.

(3) Put your name, course name, assignment number, and date submitted somewhere at the top of the first page.

(4) Show your work. Correct methods will be worth more than correct answers. For full credit, show all formulas used. Numerical tools (calculators, spreadsheets, SPSS, R) often combine several steps their calculations; you must show formulas for each step. When you use computer programs, indicate commands or menu options that you used to obtain your results.

(5) For assignments based on data collected for class, state assumptions that you made in your analysis.

**Course Schedule:**

Week |
Topics |
Research Project |

Jan. 6 | Summary Statistics Displaying data Introduction to R Computer lab transcript, Jan. 8 |
Edge effects on tree growth Traffic loads on Bellingham/WWU streets (Snowy weather alternative) |

Jan. 11 | Distributions estimation with uncertainty Computer lab transcript, Jan. 15 |
Meet 8 am at Stair Sculpture, between AW, CF, ES Maple seed dispersal distances |

Jan. 18 | Martin Luther King, Jr. Day -- No class | |

Jan. 20 | Linear Models Hypothesis testing: Comparing means, variances Computer lab transcript, Jan. 22 |
Moss growth on maple trees Avian scavenger abundance |

Jan. 25 |
Hypothesis testing, continued: Comparing means, variances Exam 1: Wed. Jan. 27 (one page of notes permitted) Exam 1 extra credit study question answers Computer lab transcript, Jan. 29 | |

Feb. 1 | Hypothesis testing: Proportions and frequencies
Computer lab transcript, Feb. 5 | Maple seed dispersal distances |

Feb. 8 | Hypothesis testing: Regression and Correlation
Example: regression calculations w/ artificial "data" | Moss growth vs. tree size |

Feb. 15 | Presidents' Day -- No class | |

Feb. 17 | Logistic Regression Exam 2: Wed. Feb. 17 (two pages of notes permitted) study question answers More practice problems Computer lab transcript, Feb. 19 |
Travel mode vs. distance Stream channel stability vs. urban development |

Feb. 22 | Information Theoretic methods and Multimodel inference
Computer lab transcript, Feb. 26 | The Mixed Nut Problem |

Feb. 29 |
Course Review: Appropriate application of statistical methods;
Answers More review questions; Answers Extra review sessions (optional) Tue March 1, 9-10am, ES 410 Tue March 1, 5pm, ES 410 Thur March 3, 5pm, ES 410 Even more review questions Exam 3: Friday March 4 (four pages of notes permitted) |

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Homework Assignments

Assignment |
Due Date |

One | Jan. 15 |

Two | Jan. 22 |

Prepare for exam 1 | |

Three | Feb. 5 |

Four | Feb. 12 |

Prepare for exam 2 | |

Five | Feb. 26 |

Six | March 4 |

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