REN R 501 - Topics in Renewable Resources

★ 1 (fi VAR)(EITHER, VARIABLE)

Directed study in the multiple aspects of renewable resources. Open to fourth year or graduate students upon consent of instructor.

REN R 690 - Multivariate Statistics for Environmental Sciences

★ 3 (fi 6)(SECOND, 3-0-3)

Focuses on visualizing and analyzing complex biological or environmental data for the purpose of prediction and scientific hypothesis testing. Covers classical and modern approaches to ordination and classification, direct and indirect gradient analysis, and models of ecological and environmental interactions. Participants engage in problem-based learning by analyzing data from their thesis research project. Students without a suitable dataset should enroll in two or more *1 REN R 58X courses instead. Prerequisite: *3 introductory statistics recommended.

REN R 906 - Research Project

★ 6 (fi 12)(VAR, UNASSIGNED)

The final research project that comprises REN R 906 is a final capping exercise for the degrees of MAg and MF. Its practical and professional focus should integrate the core areas of study in the program. The successful completion of the project entails (1) a research topic approved by the supervisor; (2) the presentation of a draft research proposal; and (3) the presentation of the research as a written document to the supervisor. The project may take the form of any of the following: (1) a formal analysis of management practice, organizational processes or policy; (2) a formative or summative evaluation of a research project or program; (3) a case study, using secondary documents, survey data, or interviews; or (4) replication of a previous study, with either the introduction of a new variable or an analysis in a changed context.

REN R 906B - Research Project

★ 3 (fi 12)(VAR, UNASSIGNED)

The final research project that comprises REN R 906 is a final capping exercise for the degrees of MAg and MF. Its practical and professional focus should integrate the core areas of study in the program. The successful completion of the project entails (1) a research topic approved by the supervisor; (2) the presentation of a draft research proposal; and (3) the presentation of the research as a written document to the supervisor. The project may take the form of any of the following: (1) a formal analysis of management practice, organizational processes or policy; (2) a formative or summative evaluation of a research project or program; (3) a case study, using secondary documents, survey data, or interviews; or (4) replication of a previous study, with either the introduction of a new variable or an analysis in a changed context.

REN R 480 - Applied Statistics for Environmental Sciences

★ 3 (fi 6)(FIRST, 3-0-1.5)

Focuses on problem formulation, method selection, and interpretation of statistical analysis. Covers data management and data visualization, statistical tests for parametric, non-parametric and binomial data, linear and non-linear regression approaches. Participants will gain general statistical literacy and learn how to visualize and analyze data with open-source software packages. Prerequisite: 60 units. 3 units in introductory statistics recommended.

REN R 580 - Applied Statistics for Environmental Sciences

★ 3 (fi 6)(FIRST, 3-0-1.5)

Focuses on problem formulation, method selection, and interpretation of statistical analysis. Covers data management and data visualization, statistical tests for parametric, nonparametric and binomial data, linear and non-linear regression approaches. Participants engage in problem-based learning by analyzing data from their thesis research project. Students without a suitable dataset should enroll in two or more 1 unit REN R 58X courses instead. Prerequisite: 3 units in introductory statistics recommended.

REN R 690 - Multivariate Statistics and Machine Learning for the Environmental Sciences

★ 3 (fi 6)(SECOND, 3-0-3)

Covers methods for visualization, analysis and prediction for complex biological or environmental data. Includes classical and modern approaches to ordination and classification, analysis of multivariate relationships, and the application of deep neural networks and other machine learning tools for prediction. Participants engage in problem-based learning by analyzing data from their thesis research project. Students without a suitable dataset should enroll in two or more 1 unit REN R 58X courses instead. Prerequisite: 3 units introductory statistics recommended.