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Linear models with R / Julian J. Faraway.

By: Faraway, Julian J.
Material type: TextTextSeries: Chapman & Hall/CRC texts in statistical science series.Publisher: Boca Raton : CRC Press, 2015Edition: 2nd ed.Description: xii, 274 p. : illustrations ; 25 cm.ISBN: 9781439887332 (hardcover : alk. paper).Subject(s): Analysis of variance | Linear models (Statistics) | Regression analysisDDC classification: 000SA.062
Contents:
1. Introduction-- 2. Estimation-- 3. Inference-- 4. Prediction-- 5. Explanation-- 6. Diagnostics-- 7. Problems with the predictors-- 8. Problems with the error-- 9. Transformation-- 10. Model selection-- 11. Shrinkage methods-- 12. Insurance redlining- A complet example-- 13. Missing data-- 14. Categorical predictors-- 15. One factor models-- 16. Models with several factors-- 17. Experiments with blocks-- A about R Bibliography-- Index.
Summary: The second edition reorganizes the material on interpreting linear models to distinguish the main applications of prediction and explanation. Elementary notions of causality are introduced and new topics include the QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates. Statistical strategy is covered and the author uses the ggplot2 graphics package in addition to base graphics.
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Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
000SA.062 F219 (Browse shelf) Available 135676
Total holds: 0

"A CRC title."

Includes bibliographical references and index.

1. Introduction--
2. Estimation--
3. Inference--
4. Prediction--
5. Explanation--
6. Diagnostics--
7. Problems with the predictors--
8. Problems with the error--
9. Transformation--
10. Model selection--
11. Shrinkage methods--
12. Insurance redlining- A complet example--
13. Missing data--
14. Categorical predictors--
15. One factor models--
16. Models with several factors--
17. Experiments with blocks--

A about R
Bibliography--
Index.

The second edition reorganizes the material on interpreting linear models to distinguish the main applications of prediction and explanation. Elementary notions of causality are introduced and new topics include the QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates. Statistical strategy is covered and the author uses the ggplot2 graphics package in addition to base graphics.

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Linear models with R by Faraway Julian J
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