Textbook

Casella, G., Berger, R. L., "Statistical Inference," 2nd edition.  

References                                                                                                                                                                                                  

Class lecture notes will soon be available (will include a complete bibliography).

Baclawaski, K.,  "Introduction to Probability with R.''

(*) Breiman, Leo, "Probability and Stochastic Processes: With a View Toward Applications"

(*) Dalgaard, Peter, "Introductory Statistics with R."

(*) Feller, W.,  "An introduction to Probability Theory and its applications: Volume I."

(*) Hogg, Robert V., Craig, Allen, McKean, Joseph W.,  "Introduction to Mathematical Statistics."

(*) Minh, D.L. (Paul),  "Applied probability models."

(*) Ross, Sheldon,  "Introduction to Probability Models."

The books marked with (*) are reserved in the Math Library in Padelford

Prerequisites       

Multivariable calculus (limits, infinite series, partial derivatives, and multiple integrals).  Linear algebra (vectors, matrices, determinants, inverses, Cauchy-Schwartz inequality, orthogonal and positive definite matrices). Some familiarity with elementary probability theory, e.g., probability distributions, expected values, random variables, conditional probability.

Readings and Homework

Read the paper by Joseph Keller (see link on the left).  Read Chapter 1 and start Chapter 2 in the textbook and/or class notes.

Weekly HW assignments (see links on left) are due each Monday unless otherwise stated.  You are urged to work individually.  If you run into difficulties with a HW problem, before discussing it with a fellow student, find a similar one, and discuss that one.  Please write neatly and legibly, and include your name on your paper.

Grade

HW 30%, Midterm 20%, Final Exam 50%.  These percentages may change if we hold the workshop I mention in the overview below.

Overview

In AMATH/STAT 506 we will review probability theory and statitical inference.  Topics will include: probability spaces, random variables, expectations, random vectors, order statistics, moment generating functions, limit laws, exploratory data analysis, estimation, regression, the Poisson process, and reliability.  I intend to finish the course with a workshop where the class will work as a research team solving a real problem.