Courses
Following
is a list of courses Professor Gregoire teaches at the Yale School of
Forestry & Environmental Studies. Click on a course to see
a description:
- FES 622a: Seminar
in Forest Inventory
- FES 711a: Sampling Methodology
and Practice
- FES 713b:
Statistics for Environmental Sciences
- FES 719b: Statistical Design of Experiments
F&ES 622a, Seminar in Forest Inventory. 2 credits. An advanced seminar that explores the design and implementation of forest inventory. Topics are varied to meet the interest of the class, but generally include the evolution and current status of broad regional and national inventories in the United States and abroad; the use of remote sensing data and GIS in forest inventory planning; forest inventory and consulting; the generation of forest inventory estimates at various scales of concern; acquisition of forest inventory data from Internet databases. Readings are assigned on a weekly basis and discussed during the seminar. A familiarity with the precepts and vernacular of probability sampling or statistics is presumed. Prerequisite: F&ES 711a. Limited enrollment. Timothy G. Gregoire.
F&ES 711a, Sampling Methodology and Practice. 3 credits. This course is intended to provide a fundamental understanding of the principles of statistical sampling, alternative estimators of population parameters, and the basis for inference in survey sampling. Natural, ecological, and environmental resource applications of sampling are emphasized, with particular focus upon the sampling of forest-related resources. Sample designs to be studied include simple random; systematic; unequal probability; fixed- and variable-radius plot; and 3P/Poisson. Line-intersect and importance-sampling variants of probability proportional to size designs are also covered. Weekly and biweekly problem sets requiring the use of a computer spreadsheet. Timothy G. Gregoire.
F&ES 713b, Statistics for Environmental Sciences. 3 credits. This course in applied statistics assists scientific researchers in the analysis and interpretation of both experimental and observational data. After considering statistical and graphical summaries of data, the notion of a random variable, distributional properties, parameter estimation, and testing are reviewed. Frequently encountered discrete and continuous distributions are examined in greater detail, with specific emphasis on the Gaussian distribution and the role of the central limit theorem. The major topics of the course are estimation and inference with linear and nonlinear regression models. Three hours lecture. Statistical computing, weekly problem exercises. Prerequisite: introductory statistics. Timothy G. Gregoire.
F&ES 719b, Statistical Design of Experiments. 3 credits. Principles of design for planned experiments, coupled with method of analysis of experimental data. The course is applications oriented using the results of established theory. The nuances, strengths, and weaknesses of a number of classical designs are discussed. These include completely randomized design, block designs, and split plot designs. The analysis of data from these designs is treated at length. Prerequisite: a prior course in introductory statistics. Timothy G. Gregoire.