Course Description
Introduction to equipment used in the field to navigate across the landscape and to measure a variety of natural resource attributes. Basic statistical sampling techniques will be reviewed and applied in the field to obtain information at a desired level of precision and statistical confidence level. Concepts will be presented in lecture and apply during field labs. Field data will be summarized and evaluated to create reports of field findings.
Athena Title
Field Orientation Measurements
Equivalent Courses
Not open to students with credit in FORS 4050, FORS 6050
Prerequisite
Enrollment in Professional Program of Warnell
Corequisite
FANR 3000L
Semester Course Offered
Offered fall and spring
Grading System
A - F (Traditional)
Course Objectives
By the end of the semester, students will have the following skills. A. Identify and use appropriate techniques for collecting, analyzing, testing, and interpreting natural resource data. 1. Understand appropriate methods for sampling terrestrial and aquatic habitats. a. Be aware of potential sources of measurement error and bias and know proper measurement techniques to minimize error. b. Understand the proper methods for summarizing field data from these measurement techniques, expanding estimates to a per-acre or per-hectare basis, and reporting it appropriately to users. c. Be able to design a simple multi-resource inventory for a forested property for a target level of estimation for a given stand measure. 2. Be able to analyze natural resource data and apply appropriate statistical techniques. [Faculty from all majors are expected to provide examples.] a. Sampling of populations (nesting, stratification, randomization). b. Development of descriptive statistics (mean, sdev, cv, stderror, bounds, etc.). c. Estimation of population parameters and error bounds. d. Hypothesis testing -- testing for differences and change. e. Understanding variability, reliability – confidence in the numbers, confidence limits, measurement error, sample size requirements. f. Using simple linear regression to fit a line to paired observations.
Topical Outline
Statistical Concepts [illustrated with examples from across the disciplines in the WSFR.] Populations, samples, inference, sample units, sampling frame. Qualitative and quantitative data summarization / analysis. Types of data and their descriptions. Point estimators and inference. Measures of relative standing, shape, variation, and dispersion of data. Interval estimators and inference. Simple random sampling and Systematic sampling. Sample size requirements. Expansion of estimates. Estimation of population proportions. Linear regression. Tests of significance - differences among means, regression parameters.
Syllabus