Lectures: MWF 10:30-11:20am, room FSH 136

Labs: Tue 1:00-2:50pm, Room FSH 136

Instructor: Trevor A. Branch, tbranch@uw.edu, room FSH 322

Office hours: As needed, after lectures and labs

TA: Merrill Rudd, mbrudd@uw.edu, room FSH 214

**Prerequisites**

Introduction to Ecological Modeling FISH 454 or the equivalent is recommended. Although participants are taught how to program in R, it is highly advantageous to have familiarity with the statistical programming language R, or to have taken Introduction to R FISH 552 and Advanced R Programming FISH 553. For those unfamiliar with programming in general, or R specifically, you are strongly advised to go through the course notes of those courses and the recommended R textbook below. Participants will be taught, and need to proficient at the following programming constructs in R: for-loops, writing functions, using vectors and matrices, if-then-else statements, reading in .csv files and writing files, producing basic line plots and histograms. Lectures and labs will be run in Excel and in R using Rstudio.

*Textbook*

There are no required textbooks. However, participants would benefit greatly from purchasing these two books:

"The Ecological Detective" by Hilborn and Mangel (about $60) which is an easy-reading and useful general reference written for ecologists about how to fit maximum likelihood and Bayesian models to data (the core part of the course).

"The Art of R Programming" by Norman Matloff (about $25) is an R programming textbook that will serve you in this class and well beyond. A draft version can be found here for free, but the book itself is more comprehensive and better written. The key chapters needed for the class are chapters 1-4, 8-9 and 11. Participants are advised to read through these chapters before and during the course, especially if they are not familiar with R.

**Time commitment**

Attendance in lectures and laboratories (5 hr per week). There will be three lectures and one 2-hr lab each week.

Two mid-term examinations (10 hr preparation time each). There will be 50-minute closed-book mid-terms on 9 May and 6 June, testing knowledge of materials from lectures, readings and labs.

Final examination: there is no final examination.

Lab exam: The final lab on 3 June will be an in-class open book 2-hr lab exam. The exam will be in R and will test your knowledge of practical abilities learned during the course.

Readings (1 hr per week): Occasional scientific papers will be assigned for reading.

Homework (4-8 hr per week): There will be 6 project-style homework problems assigned, every one to two weeks. Initial assignments will be in Excel; later assignments in R.

**Grading**

A percentage grade will be assigned for the following components of the course, with highest weight given to the homework and lab exam:

15% Mid-term I

15% Mid-term II

20% Lab exam

50% Homework

Grades are not converted using a curve, thus everyone can do well in the class. Instead, percentages are converted to a grade on the point scale (0.7-4.0) as follows: I pick a lower bound for a 0.7 score, usually 30-50%, and an upper bound for a 4.0 score (usually 90-95%), then linearly interpolate between these points. For example, if the lower bound is 40% and the upper bound is 95%, then the percentages are converted to grades as follows:

<40% 0.0

40% 0.7

50% 1.3

60% 1.9

70% 2.5

80% 3.1

90% 3.7

95% 4.0

>95% 4.0

**University policy on plagiarism and misconduct**

Plagiarism, cheating, and other misconduct are serious violations of the student conduct code. We expect that you will know and follow the UW's policies on cheating and plagiarism. Any suspected cases of academic misconduct will be handled according to UW regulations. More information, including definitions and examples, can be found in the Faculty Resource for Grading and the Student Conduct Code (WAC 478-120).