Objective

The objective of the course is to extend your repertoire of approaches towards modeling beyond the standard linear model based approaches. Towards this objective we will examine the various assumptions made in a standard analysis, examine our options when our assumptions are different, and examine our options when these assumptions are relaxed. Along the way we will not only look at the application of different analytical methods but, we will also look at the theory behind these methods.

Course Info

Instructor:	Steve Kachman
E-mail:	skachman@unl.edu
Office:	355B Hardin Center
Phone:	472-7302
Text Book:	Generalized, Linear, and Mixed Models, McCulloch and Searle
	Generalized Linear Models, McCullagh and Nelder Is a classic book which provides some additional depth.
Syllabus:	Course Syllabus
	To view the Course Syllabus click on Course Syllabus . If Acrobat Reader is not installed on your computer click on .
Prerequisites:	Statistics 970 Matrix Algebra Linear Models Distributions Experimental Design Basics of Theory and Inference SAS
Homework:	Approximately every other week 20% will be deducted for each day late.
Exams:	Two exams, will be announced at least one week in advance
Final Exam:	Tuesday, Dec 13 10:00-Noon Revise travel plans accordingly.
Check Grades:	Blackboard Course Info

Support Material

• Statistics 970: Linear Models
• An Introduction to Linear Algebra