This course will provide students with an overview of statistical methods used in agriculture, animal sciences, plant sciences, and natural resource management and research. Students will familiarize themselves with data collection techniques and experimental design, probability distribution, statistical tests, measures of central tendency and dispersion. Simple linear regression, correlation and linear models. (Same as SNR 220) Satisfies Volunteer Core Requirement: (QR)

Credit for formalized international experiences related to agricultural sciences and natural resources. Determination of credit based on nature of the proposed experience. Students should discuss the opportunity with their faculty advisors prior to the trip to determine if it is appropriate for credit. Credit hours will be determined by the department and college depending on the extent of activity and types of projects and/or presentations to be completed by the student upon return.

Repeatability: May be repeated. Maximum 12 hours.
Registration Permission: Consent of instructor.

Other Instructors: Willcox, Adam Sage | Pulte, Andy

Experimental design and procedures; selection of experimental units; analysis and interpretation of data; statistical models and contrasts, analyses of variance: covariates, treatment arrangements, mean separation and regression. Cross-listed: (Same as Plant Sciences 571.)
Recommended Background: 3 hours of statistics.
Registration Restriction(s): Minimum student level – graduate or permission of instructor.

Other Instructors: Mueller, Thomas C

Relations between organisms and their environment, including human environmental problems. Topics include populations, communities, and ecosystems.

(RE) Prerequisite(s) : BIOL 150-BIOL 160-BIOL 159 or equivalent; or BIOL 113-BIOL 114-BIOL 115.

Experimental design and hypothesis testing for ecology and evolutionary biology research. Parameter estimation, general linear models, generalized linear models, maximum likelihood, and permutation approaches, and their application to problems in ecology and evolutionary biology. Satisfies Volunteer Core Requirement: (QR) Satisfies General Education Requirement through the 2021-2022 academic catalog: (QR)
(RE) Prerequisite(s) : MATH 141 or MATH 151.

Statistical applications in biological research.

Recommended Background: Statistics course or consent of instructor.

Exposure and in-depth training in contemporary topics and approaches important to advanced research in natural resources.

Repeatability: May be repeated with consent of department. Maximum 9 hours.
Registration Restriction(s): Minimum student level – graduate.

Other Instructors: Hodges, Donald G | Wilber, Mark Quentin

Experimental design and procedures; selection of experimental units; analysis and interpretation of data; statistical models and contrasts, analyses of variance: covariates, treatment arrangements, mean separation and regression. Cross-listed: (See Animal Science 571.)
Recommended Background: 3 hours of statistics.
Registration Restriction(s): Minimum student level – graduate or permission of instructor.

This course will provide students with an overview of statistical methods used in agriculture, animal sciences, plant sciences, and natural resource management and research. Students will familiarize themselves with data collection techniques and experimental design, probability distribution, statistical tests, measures of central tendency and dispersion. Simple linear regression, correlation and linear models. (See AGNR 220) Satisfies Volunteer Core Requirement: (QR)

Provides graduate students with a theoretical framework for data modeling (linear models, additive models and multivariate models), data visualization, data management, and data interpretation. Will also aim to teach a practical use of program R for data management, analysis and visualization. Topics include an overview of data management principles, followed by methods in R for data wrangling, linear models, polynomial regression, generalized linear models, generalized additive models, and multivariate models, particularly ordination methods. Credit Restriction: Students may not receive credit for both SNR 610 and FWF 690 taken during Fall 2024.
Recommended Background: A 500-level or higher statistics course is recommended prior to taking this course.
Registration Restriction(s): Minimum student level - graduate.
Registration Permission: Instructor permission.

Exposure and in-depth training in contemporary topics and approaches important to advanced research in natural resources.

Repeatability: May be repeated. Maximum 9 hours.
Registration Restriction(s): Minimum student level - graduate.

Use of R, which is an open-source programming language useful for all aspects of data analysis, statistical analysis, and visualization.  This course will cover the basics of the programming language, functions, and packages used in natural resource research and management.  The course will use examples of data collected on wildlife studies and animal movements.

(RE) Prerequisite(s) : MATH 125, MATH 115 or STAT 201; WFS 100, FWF 250, and FWF 315

This course will cover the theory and application of sample design, analysis, and interpretation of freshwater fisheries data. The course will provide students with the skills and knowledge to understand, evaluate, analyze, and interpret contemporary fisheries data. Topics will include fisheries management study design, recruitment, mortality, age and growth, abundance, biomass and production, size structure, bioenergetics, and development of management plans.

(RE) Prerequisite(s) : WFS 100, FWF 315, WFS 442