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Applied Regression Analysis (2nd ed).
Abstract
This book brings together a number of procedures developed for regression problems in current use. Since the emphasis is on practical application theoretical results are stated without proofs in many cases. This book provides a standard basic course in multiple linear regression but it also includes material that either has not previously appeared in a textbook or if it has appeared is not generally available. Chapters 1 and 3 together provide a course in fitting a straight line without using matrix algebra at all. If chapter 2 is added the idea of matrix representation of regression problems can be introduced as well. Chapter 4 covers 2 predictor variables and chapter 5 deals with more complicated models. Selecting the best regression equation is discussed in chapter 6. Chapter 7 covers specific problems. Chapters 8 and 9 discuss 1) multiple regression and mathematical model building and 2) multiple regression applied to analysis of variance problems. Chapter 10 contains an introduction to nonlinear estimation. The 2nd edition contains many new regression ideas and techniques. In particular new computational algorithms and new software regression packages have made it very easy to investigate the allequacy of conjectured models with many different techniques.
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Citations
Cited by
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George, E. I., & McCulloch, R. E. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476353 · Cited by 2,828
Wold, S., Ruhe, A., Wold, H., & Dunn, III (1984) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0905052 · Cited by 2,529
Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey
Shadlen, M. N., & Newsome, W. T. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.4.1916 · Cited by 1,755
Clinical versus mechanical prediction: a meta-analysis
Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000) · Psychological Assessment · doi:10.1037/1040-3590.12.1.19 · Cited by 1,640
Metadata
- Authors
- de Carvalho, J. F., Draper, N. R., & Smith, H.
- Venue
- Journal of the American Statistical Association
- Publisher
- JSTOR
- Published
- 1981-12
- Volume
- 76
- Issue
- 376
- Pages
- 1012
- ISSN
- 0162-1459 (ISSN-L)
- OpenAlex
- W2022480405
- Cited by
- 3,044
- References
- 0
- FWCI
- 42.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- de Carvalho, J. F., Draper, N. R., & Smith, H. (1981). Applied Regression Analysis (2nd ed). Journal of the American Statistical Association. https://doi.org/10.2307/2287608
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