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R Companion to Linear Statistical Models

Christopher Hay-Jahans
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A01=Chris Hay-Jahans
A01=Christopher Hay-Jahans
Ab Ilit
analysis
ANOVA Table
applied regression for scientists
assumption
Author_Chris Hay-Jahans
Author_Christopher Hay-Jahans
Basic Plotting Functions
Box Cox Procedure
Category=PBT
column
constant
Constant Variance Assumption
Continuous Explanatory Variables
Continuous Response Variable
data
Data Frame
Density Histogram
Diagnostic Plots
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
experimental design
exploratory
factor analysis
FALSE FALSE
frame
full
Full Column Rank
Function Boxcox
Interaction Plots
Linear Models with Fixed-Effects Factors
Linear Regression Models
model diagnostics
Multiple Linear Regression
Nonadditive Model
Package Mass
Pairwise Comparisons
Pf
Qq Plot
quantitative analysis
R programming techniques
rank
Regression Models
Residual Standard Error
Script Editor
Shapiro Wilk Normality Test
Simple Remedies for Multiple Regression
statistical computing
Tukey Kramer Procedure
Untransformed Model
variance
Working with Data Structures

Product details

  • ISBN 9781138116030
  • Weight: 690g
  • Dimensions: 156 x 234mm
  • Publication Date: 18 Oct 2017
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters.

This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures.

About Chris Hay-Jahans | Christopher Hay-Jahans

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.

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