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Instructor Class Description

Time Schedule:

John R. Skalski
Q SCI 483
Seattle Campus

Statistical Inference in Applied Research II: Regression Analysis for Ecologists and Resource Managers

Analysis of linear regression models and introduction to nonlinear models. Model selection using generalized F-tests; residual analysis. Application to categorical, count, binomial, transformed variables. Introduction to matrix formation of regression models and applications. Prerequisite: Q SCI 381; Q SCI 482. Offered: Sp.

Class description

Basic topics of ordinary least-squares including model fitting, parameter estimation, confidence interval estimates, residual analyses, hypothesis testing, sample size calculations, and power analyses. Generalized linear models will be presented for non-normally distributed data. Weighted regression, nonlinear regression, and loess smoothing will also be covered using the free statistical software R.

Student learning goals

Perform single and multiple variable regressions

Analyze continuous, count, categorical, and binomial data

Construct and evaluate model fit

Graphically and tabular summarize data

Use free statistical software R

General method of instruction

Two 2-hour lectures per week, along with a 2-hour computer lab using R.

Recommended preparation

Working knowledge of basic statistical inference, as taught in QSCI 482, essential.

Class assignments and grading

Nine weekly homework assignments will analyze a variety of ecological data sets using the methods introduced in the lectures and presented in the computer labs. One midterm and one in-lab final examination.

Grade based on total numerical score from nine weekly homework assignments (450 points), midterm exam (100 points), and final exam (150 points).


The information above is intended to be helpful in choosing courses. Because the instructor may further develop his/her plans for this course, its characteristics are subject to change without notice. In most cases, the official course syllabus will be distributed on the first day of class.
Last Update by John R. Skalski
Date: 11/03/2011