Statistics 309: Introduction to Mathematical Statistics I, Fall 2004.

Instructor: Michael Newton, office hours Wednesdays 1:00 - 3:00 rm 1245A MSC


• 4 credits
• Classes Tuesdays, Thursdays 9:30 - 10:45, rm 1131 Humanities
• Discussions Tuesday/Wednesday by teaching assistant Hui Wang
• Weekly homeworks
• Course description: To learn the main findings in the theory of mathematical statistics, the student must become well versed in the language of probability theory. This semester covers that theory at an introductory level, assuming the basic techniques of calculus. We cover elements of set theory, the axioms of probability, random variables, many aspects of distribution theory of single and multiple random variables, limit theorems including the weak law of large numbers and the central limit theorem. The discussion is oriented towards mathematical statistics; methods of statistics and their application are used to provide context for the mathematical results.
• We aim to cover the first 6-7 chapters of John Rice's, "Mathematical Statistics and Data Analysis", 2nd edition, Duxbury Press.
• Computing examples will be based on the R language for programming with data.
• Grading: Homework 25%, two midterms 20% each, final 35%
• Midterm 1: Tuesday October 12, in class: Test ; Raw score distribution
• Midterm 2: Tuesday November 23, in class

Class material
• Class 1: R code , Athens data, Sydney data, and plot from Olympics example, 9/2/04.
• Some R code to learn basics of data manipulation

Homework assignments