STATS 600 - Linear Models
Section: 001
Term: FA 2017
Subject: Statistics (STATS)
Department: LSA Statistics
Waitlist Capacity:
Advisory Prerequisites:
Knowledge of linear algebra; STATS 425 and STATS 426 or equivalent courses in probability and statistics.
May be repeated for a maximum of 8 credit(s).
Primary Instructor:

This is an advanced introduction to regression modeling and prediction, including traditional and modern computationally-intensive methods.  The following topics will be covered:  1) Theory and practice of linear models, including the relevant distribution theory, estimation, confidence and prediction intervals, testing, models and variable selection generalized least squares, robust fitting, and diagnostics; 2) Generalized linear models, including likelihood formulation, estimation and inference, diagnostics, and analysis of deviance; and 3) Large and small-sample inference as well as inference via the bootstrap, cross-validation, and permutation tests.

STATS 600 - Linear Models
Schedule Listing
001 (LEC)
25STATS PhD only
MW 11:30AM - 1:00PM
002 (LAB)
Tu 5:30PM - 7:00PM
NOTE: Data maintained by department in Wolverine Access. If no textbooks are listed below, check with the department.

ISBN: 9780471415404
Linear regression analysis, Author: Seber, G. A. F. (George Arthur Frederick), 1938-, Publisher: Wiley-Interscience 2003
ISBN: 9812834117
Linear regression analysis theory and computing, Author: Yan, Xin., Publisher: World Scientific 2009
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