Linear Models with R
Seiten
2014
|
2nd edition
Chapman & Hall/CRC (Verlag)
978-1-4398-8733-2 (ISBN)
Chapman & Hall/CRC (Verlag)
978-1-4398-8733-2 (ISBN)
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A Hands-On Way to Learning Data Analysis
Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.
New to the Second Edition
Reorganized material on interpreting linear models, which distinguishes the main applications of prediction and explanation and introduces elementary notions of causality
Additional topics, including QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates
Extensive use of the ggplot2 graphics package in addition to base graphics
Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.
Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.
New to the Second Edition
Reorganized material on interpreting linear models, which distinguishes the main applications of prediction and explanation and introduces elementary notions of causality
Additional topics, including QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates
Extensive use of the ggplot2 graphics package in addition to base graphics
Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.
Julian J. Faraway
Introduction. Estimation. Inference. Prediction. Explanation. Diagnostics. Problems with the Predictors. Problems with the Error. Transformation. Model Selection. Shrinkage Methods. Insurance Redlining—A Complete Example. Missing Data. Categorical Predictors. One Factor Models. Models with Several Factors. Experiments with Blocks. Appendix: About R. Bibliography. Index.
| Erscheint lt. Verlag | 14.8.2014 |
|---|---|
| Reihe/Serie | Chapman & Hall/CRC Texts in Statistical Science |
| Zusatzinfo | 94 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 700 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Naturwissenschaften ► Biologie | |
| ISBN-10 | 1-4398-8733-0 / 1439887330 |
| ISBN-13 | 978-1-4398-8733-2 / 9781439887332 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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