Linear Models with R

Name: Linear Models with R
Price: 10.27 USD
Availability: InStock
ISBN: 9781584884255

ISBN-10: 1584884258

ISBN-13: 9781584884255

Edition: 2004

Authors: Laurie Kelly, Julian J. Faraway

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…

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: 1.034
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