An R Companion to Linear Statistical Models

Christopher Hay-Jahans received his Doctor of Arts in mathematics from Idaho State University in 1999. After spending three years at University of South Dakota, he moved to Juneau, Alaska, in 2002 where he has taught a wide range of undergraduate courses at University of Alaska Southeast. Each year, since 2004, he has also been teaching a course on regression and analysis of variance. Students enrolling in this course have included UAS undergraduates, masters and doctoral students from the Juneau Campus of the University of Alaska Fairbanks School of Fisheries and Ocean Sciences, as well as area professionals in the applied sciences. This work was developed as a supplement for his regression and analysis of variance course and is geared to cover topics from a wide range of textbooks, as well as address the interests, needs, and abilities of a fairly diverse group of students.

Background: Getting Started. Working with Numbers. Working with Data Structures. Basic Plotting Functions. Automating Flow in Programs. Linear Regression Models: Simple Linear Regression. Simple Remedies for Simple Regression. Multiple Linear Regression. Additional Diagnostics for Multiple Regression. Simple Remedies for Multiple Regression. Linear Models with Fixed-Effects Factors: One-Factor Models. One-Factor Models with Covariates. One-Factor Models with a Blocking Variable. Two-Factor Models. Simple Remedies for Fixed-Effects Models. Bibliography. Index.