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borderland which separates the known from the unknown”

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Linear models with R / Julian J. Faraway.

By: Material type: TextTextSeries: Chapman & Hall/CRC texts in statistical science seriesPublication details: Boca Raton : CRC Press, 2015.Edition: 2nd edDescription: xii, 274 p. : illustrations ; 25 cmISBN:
  • 9781439887332 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 000SA.062 23 F219
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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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.062 F219 (Browse shelf(Opens below)) 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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