Course Description
Methods for sampling the environment and analysis of environmental data are considered. Techniques are presented for estimation, hypothesis testing, and regression when data are non-normal and/or dependent. Statistical methods based on generalized linear models, linear mixed models, time series analysis, and spatial data analysis are surveyed from an applied perspective.
Athena Title
ENVIRON STATIST
Prerequisite
STAT 6220 or STAT 4230/6230 or STAT 6315 or STAT 6320 or STAT 6420 or permission of department
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Course Objectives
Students will learn how to analyze environmental data that are not normally distributed and/or dependent. Techniques are presented in a very applied context suitable for quantitatively oriented students from ecology, environmental health science, forestry and other disciplines in the environmental sciences.
Topical Outline
Sampling the environment: stratified sampling, sampling temporal and spatial data, composite sampling, ranked-set sampling. Discrete and continuous distributions. Estimation: method of moments, least squares, maximum likelihood. Hypothesis testing via likelihood ratio and Wald tests. Regression via generalized linear models and linear mixed models. Time Series: autocorrelation function, time series models, regression with time series errors. Spatial Statistics: variograms, Kriging.
Syllabus