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Publisher:

Chapman & Hall/CRC

Publication Date:

2014

Number of Pages:

274

Format:

Hardcover

Edition:

2

Series:

Texts in Statistical Science

Price:

89.95

ISBN:

9781439887332

Category:

Textbook

The Basic Library List Committee suggests that undergraduate mathematics libraries consider this book for acquisition.

[Reviewed by , on ]

Priya Kohli

01/6/2015

As the title implies, the book is a text on the core topic of linear models with a slant toward applications. The data examples cover a broad range of fields including physical sciences, engineering, social sciences, and economics. The book provides an excellent introduction of the various aspects of linear models with many interesting examples.

The explanations are clear enough for beginners with little statistical background and are accompanied by worked examples with associated R code. This is an important contribution, since it provides the readers/students an opportunity to replicate the analyses and results of an example. There are many books written on the topic of linear models, but this book takes an applied approach and explains the concepts intuitively, using graphical explanations and examples.

Overall this is a nicely written book, which can lay a strong foundation for senior undergraduate and beginning graduate students. This book can be recommended as a textbook for computational linear regression courses. It will also be useful for practitioners who want to get started on applying regression models for studying associations among different variables, estimation of regression coefficients, and prediction. It offers insightful interpretations and discussions with examples worked using the R software.

Priya Kohli is an Assistant Professor of Statistics at the Connecticut College. She holds a Master’s degree in Applied Probability and Statistics from Northern Illinois University and a PhD in Statistics from Texas A&M University. Her current research interests include covariance modeling, high-dimensional data modeling, time series analysis, spatial statistics, and multivariate data modeling.

**Introduction **

Before You Start

Initial Data Analysis

When to Use Linear Modeling

History

**Estimation **

Linear Model

Matrix Representation

Estimating b

Least Squares Estimation

Examples of Calculating ˆb

Example

QR Decomposition

Gauss–Markov Theorem

Goodness of Fit

Identifiability

Orthogonality

**Inference **

Hypothesis Tests to Compare Models

Testing Examples

Permutation Tests

Sampling

Confidence Intervals for b

Bootstrap Confidence Intervals

**Prediction **

Confidence Intervals for Predictions

Predicting Body Fat

Autoregression

What Can Go Wrong with Predictions?

**Explanation **

Simple Meaning

Causality

Designed Experiments

Observational Data

Matching

Covariate Adjustment

Qualitative Support for Causation

**Diagnostics **

Checking Error Assumptions

Finding Unusual Observations

Checking the Structure of the Model

Discussion

**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

Broken Stick Regression

Polynomials

Splines

Additive Models

More Complex Models

**Model Selection **

Hierarchical Models

Testing-Based Procedures

Criterion-Based Procedures

Summary

**Shrinkage Methods **

Principal Components

Partial Least Squares

Ridge Regression

Lasso

**Insurance Redlining—A Complete Example **

Ecological Correlation

Initial Data Analysis

Full Model and Diagnostics

Sensitivity Analysis

Discussion

**Missing Data **

Types of Missing Data

Deletion

Single Imputation

Multiple Imputation

**Categorical Predictors **

A Two-Level Factor

Factors and Quantitative Predictors

Interpretation with Interaction Terms

Factors with More than Two Levels

Alternative Codings of Qualitative Predictors

**One Factor Models **

The Model

An Example

Diagnostics

Pairwise Comparisons

False Discovery Rate

**Models with Several Factors **

Two Factors with No Replication

Two Factors with Replication

Two Factors with an Interaction

Larger Factorial Experiments

**Experiments with Blocks **

Randomized Block Design

Latin Squares

Balanced Incomplete Block Design

**Appendix: About R **

**Bibliography**

**Index**

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