Statistics 310: Introduction to Mathematical Statistics II, Spring 2005.

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


• 4 credits
• Classes Tuesdays, Thursdays 9:30 - 10:45, rm 114 Ingraham
• Discussions Tuesday/Wednesday by teaching assistant Heejung Shim
• Weekly homeworks
• Course description: Following statistics 309, this course will introduce the student to the main techniques and findings of statistical inference. Main topics include estimation, hypothesis testing, and confidence set construction. Theoretical developments will focus on parametric models and will center around likelihood theory. Key results include the Cramer-Rao bound and the Neyman Pearson lemma. Methods for assessing sampling distributions, including the bootstrap, will be introduced. Topics will be developed in models such as regression, analysis of variance. Topics from data analysis will also be developed.
• We aim to cover the last chapters (8-15) 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 March 1, in class
• Midterm 2: Tuesday April 12, in class

Class material

Homework assignments
Some R code and data from class