NRES 730 - Autocorrelation in time and space: Applied analysis using R

(3 units)

Identification of temporal and spatial relationships in environmental data, and presentation of numerical methods to quantify autocorrelation as a tool for investigating natural phenomena. Practical applications will be examined using public-domain records, such as climate datasets at various spatio-temporal scales. All examples will employ the open-source R software.

Maximum units a student may earn: 3

Recommended Preparation: At least one course in introductory applied statistics or data analysis.

Grading Basis: Graded
Units of Lecture: 3
Offered: Every Spring

Student Learning Outcomes
Upon completion of this course, students will be able to:
1. utilize quantitative methods to analyze environmental data.
2. describe the principles of the public-domain R software environment, its capability, and applications.
3. analyze autocorrelated data and to extract information about statistical relationships in space and time.

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