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Biostatistics: A Computing Approach Chapman & Hall/CRC Biostatistics Series
Auteur : Anderson Stewart
The emergence of high-speed computing has facilitated the development of many exciting statistical and mathematical methods in the last 25 years, broadening the landscape of available tools in statistical investigations of complex data. Biostatistics: A Computing Approach focuses on visualization and computational approaches associated with both modern and classical techniques. Furthermore, it promotes computing as a tool for performing both analyses and simulations that can facilitate such understanding.
As a practical matter, programs in R and SAS are presented throughout the text. In addition to these programs, appendices describing the basic use of SAS and R are provided. Teaching by example, this book emphasizes the importance of simulation and numerical exploration in a modern-day statistical investigation. A few statistical methods that can be implemented with simple calculations are also worked into the text to build insight about how the methods really work.
Suitable for students who have an interest in the application of statistical methods but do not necessarily intend to become statisticians, this book has been developed from Introduction to Biostatistics II, which the author taught for more than a decade at the University of Pittsburgh.
Review of Topics in Probability and Statistics. Use of Simulation Techniques. The Central Limit Theorem. Correlation and Regression. Analysis of Variance. Discrete Measures of Risk. Multivariate Analysis. Analysis of Repeated Measures Data. Nonparametric Methods. Analysis of Time to Event Data. Sample Size and Power Calculations. Appendices. References. Index.
Date de parution : 01-2012
Ouvrage de 416 p.
15.6x23.4 cm
Thèmes de Biostatistics: A Computing Approach :
Mots-clés :
Multiple Linear Regression; Col1 Col2 Col3 Col4 Col5; Review of Topics in Probability and Statistics; Row Col1 Col2 Col3 Col4; Use of Simulation Techniques; Covariance Pattern Models; Correlation and Regression; Cumulative Distribution Function; Analysis of Variance; Cumulative Survival Function; Analysis of Time to Event Data; Proc IML; Input Sample Size; ANOVA Table; Col1 Col2 Col3 Col4; Uniform Random; SAS IML; Cumulative Density Function; Standard Uniform Distribution; Rat Gp; Experimentwise Error Rate; Total Person Time; Multiple Comparisons Procedures; Multinormal Distribution; Sig Sig; Empirical Cumulative Density Function; Central Limit Theorem; Mantel Haenszel Procedure; QOL Score; Error SSCP Matrix