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This course explores statistical problems beyond the classical linear models including mixed effects, non-normal error distributions, autocorrelations, etc. Hierarchical models, including explicit observer effects, will also be considered.

This course explores statistical problems beyond the classical linear models including mixed effects, non-normal error distributions, autocorrelations, etc. Hierarchical models, including explicit observer effects, will also be considered.

This laboratory course introduces the statistical computing language R and provides hands-on experience using R to screen and adjust data, examine deterministic functions and probability distributions, conduct classic one- and two-sample tests, utilize bootstrapping and Monte Carlo randomization procedures, and conduct stochastic simulations for ecological modeling.

Description not available at this time

Description not available at this time

Description not available at this time

Contact department for description.

Provides graduate students with a broad sampling of new and cutting-edge research related to environmental conservation to help foster critical thinking and provide a more expansive view of natural resources research. Seminars will be given by departmental faculty and faculty from other departments, both on campus and from other institutions. The seminars will be designed for both students who plan a research career and those who plan a more applied path.

This course provides students with an understanding of basic statistical concepts critical to the proper use and understanding of statistics in ecology and conservation science and prepares students for subsequent ECO courses in ecological modeling.