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Linear Regression Analysis: Theory and Computing
Open accessAbstract
This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using the Statistical Application System (SAS) are also included. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject fields.
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Citations
Cited by
Pathak, J., Hunt, B., Girvan, M., Lu, Z., & Ott, E. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.024102 · Cited by 1,303
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Pontes, H. M., Taylor, M., & Stavropoulos, V. (2018) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2017.0609 · Cited by 163
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Metadata
- Authors
- Yan, X., & Su, X. G.
- Venue
- WORLD SCIENTIFIC eBooks
- Publisher
- WORLD SCIENTIFIC
- Published
- 2009-06
- DOI
- 10.1142/6986
- OpenAlex
- W4214625488
- Cited by
- 564
- References
- 0
- FWCI
- 0.51
- Type
- book
- Language
- en
- Open access
- green
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
