Syllabus

Course Description

EEOS601 is designed to help students understand the basis in probability for statistical inference. This course will prepare students to understand the foundations for the complex regression and ANOVA models in advanced applied statistics courses, like EEOS611 offered in the spring. Through analysis of probability problems, I hope to convey the probabilistic foundation of all statistical inference. I’ll also introduce students to MATLAB®, a software package that can be used for a diverse array of research problems.

Course Objectives

The course is designed to:

• Introduce the probability theory which is the foundation of statistical inference and apply these probability distributions and statistical inferences to problems in the environmental sciences
• Cover basic inferential statistics including
       *Goodness of fit tests and tests of r x c contingency tables
       *Least-squares regression
       *One sample and two-sample tests & their nonparametric analogues
            -Student’s t tests
            -Wilcoxon tests
            -Sign test
       *One-way parametric ANOVA and nonparametric analogues !
• Teach basic data analysis with Matlab. " Introduce Matlab-based Monte Carlo simulation methods for analyzing problems in probability and statistics " These tools will allow students to write their own programs to analyze linear models and multivariate statistics.

Prerequisites

Permission of instructor.

Required Textbook

Other Reading

• Nahin, P. J. 2002. Duelling idiots and other probability puzzlers. Princeton University Press, Princeton N.J. {ISBN: 0-691-10286-4}
• Larsen, R. J. and M. L. Marx. 2006b. Student Solutions manual to an introduction to mathematical statistics and its applications. Prentice Hall, Upper Saddle River, NJ. {ISBN-13: 9780131867963}

Grading

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