Modern Applied Biostatistical Methods: Using S-Plus: 28 (Monographs in Epidemiology and Biostatistics)
Language: English
Published by OUP USA, 1998
- Hardcover
- New
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This textbook is an integrated account of the application of statistics to biomedical data. Using the S-Plus computer language, it combines theory, applications and interpretation to present the entire process of biostatistical reasoning for a series of elementary and intermediate methods. Series: Monographs in Epidemiology & Biostatistics. Num Pages: 480 pages, numerous figures. BIC Classification: MB; PBT; PDE; PS. Category: (P) Professional & Vocational. Dimension: 242 x 165 x 38. Weight in Grams: 818. . 1998. First Edition. hardcover. . . . . Books ship from the US and Ireland.
Seller Inventory # V9780195120257
- Title
- Modern Applied Biostatistical Methods: Using S-Plus: 28 (Monographs in Epidemiology and Biostatistics)
- Author
- Selvin, Steve
- Publisher
- OUP USA
- Publication year
- 1998
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0195120256
- ISBN 13
- 9780195120257
This text is not a computer manual, even though it makes extensive use of computer language to describe and illustrate applied statistical techniques. This makes the details of the statistical process readily accessible, providing insight into how and why a statistical method identifies the properties of sampled data. The first chapter gives a simple overview of the S-PLUS language. The subsequent chapters use this valuable statistical tool to present a variety of analytic approaches. Combining statistical logic, data and computer tools, the author explores such topics as random number generation, general linear models, estimation, analysis of tabular data, analysis of variance and survival analysis. The end result is a clear and complete explanation of the way statistical methods can help one gain an understanding of collected data.
Modern Applied Biostatistical Methods is unlike other statistical texts, which usually deal either with theory or with applications. It integrates the two elements into a single presentation of theoretical background, data, interpretation, graphics, and implementation. This all-around approach will be particularly helpful to students in various biostatistics and advanced epidemiology courses, and will interest all researchers involved in biomedical data analysis.
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