Linear Models with R

ISBN-10: 1584884258

ISBN-13: 9781584884255

Edition: 2004

List price: $96.95
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Description:

In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and more importantly, in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion on topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results. All of the data sets used in the book are available for download from the Internet.
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Book details

List price: $96.95
Copyright year: 2004
Publisher: CRC Press LLC
Publication date: 8/12/2004
Binding: Hardcover
Pages: 240
Size: 6.25" wide x 9.25" long x 0.75" tall
Weight: 1.276
Language: English

Preface
Introduction
Before You Start
Initial Data Analysis
When to Use Regression Analysis
History
Estimation
Linear Model
Matrix Representation
Estimating [beta]
Least Squares Estimation
Examples of Calculating [beta]
Gauss-Markov Theorem
Goodness of Fit
Example
Identifiability
Inference
Hypothesis Tests to Compare Models
Testing Examples
Permutation Tests
Confidence Intervals for [beta]
Confidence Intervals for Predictions
Designed Experiments
Observational Data
Practical Difficulties
Diagnostics
Checking Error Assumptions
Finding Unusual Observations
Checking the Structure of the Model
Problems with the Predictors
Errors in the Predictors
Changes of Scale
Collinearity
Problems with the Error
Generalized Least Squares
Weighted Least Squares
Testing for Lack of Fit
Robust Regression
Transformation
Transforming the Response
Transforming the Predictors
Variable Selection
Hierarchical Models
Testing-Based Procedures
Criterion-Based Procedures
Summary
Shrinkage Methods
Principal Components
Partial Least Squares
Ridge Regression
Statistical Strategy and Model Uncertainty
Strategy
An Experiment in Model Building
Discussion
Insurance Redlining--A Complete Example
Ecological Correlation
Initial Data Analysis
Initial Model and Diagnostics
Transformation and Variable Selection
Discussion
Missing Data
Analysis of Covariance
A Two-Level Example
Coding Qualitative Predictors
A Multilevel Factor Example
One-Way Analysis of Variance
The Model
An Example
Diagnostics
Pairwise Comparisons
Factorial Designs
Two-Way ANOVA
Two-Way ANOVA with One Observation per Cell
Two-Way ANOVA with More than One Observation per Cell
Larger Factorial Experiments
Block Designs
Randomized Block Design
Latin Squares
Balanced Incomplete Block Design
R Installation, Functions and Data
Quick Introduction to R
Reading the Data In
Numerical Summaries
Graphical Summaries
Selecting Subsets of the Data
Learning More about R
Bibliography
Index
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