Course Catalog

GRAD > EEOS > 601

Introduction to Probability and Applied Statistics

The course will analyze basic probability theory, probability distributions useful for modeling environmental processes - including binomial, Poisson, exponential, normal, geometric, hypergeometric, Chi-square, F, and Student's t - conditional probabilities & Bayes' theorem, random variables, & expected values, the central limit theorem, and parameter estimation. The course focuses on software-based hypothesis testing including p-values & confidence limits, Monte Carlo simulations, Type I and II error & power, Student's t tests and non-parametric alternatives, contigency tables & goodness-of-fit tests, regression, correlation, and one-way randomized block ANOVA. The course will make extensive use of programming software (e.g., Matlab or R). Calculus is a prerequisite.

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