Biostatistics: A Computing Approach

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A01=Stewart Anderson
Analysis of Time to Event Data
Analysis of Variance
ANOVA Table
Author_Stewart Anderson
Category=PS
Central Limit Theorem
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computational statistics
Correlation and Regression
Covariance Pattern Models
Cumulative Density Function
Cumulative Distribution Function
Cumulative Survival Function
data visualisation techniques
Empirical Cumulative Density Function
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Error SSCP Matrix
Experimentwise Error Rate
Input Sample Size
Mantel Haenszel Procedure
Multinormal Distribution
Multiple Comparisons Procedures
Multiple Linear Regression
power calculation methods
Proc IML
QOL Score
Rat Gp
repeated measures analysis
Review of Topics in Probability and Statistics
risk assessment modelling
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SAS IML
Sig Sig
simulation techniques for biomedical research
Standard Uniform Distribution
time to event modelling
Total Person Time
Uniform Random
Use of Simulation Techniques

Product details

  • ISBN 9781584888345
  • Weight: 770g
  • Dimensions: 156 x 234mm
  • Publication Date: 20 Dec 2011
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.

University of Pittsburgh, Pittsburgh, PA University of Bath, UK University of Minnesota, Minneapolis, USA Northwestern University, Evanston, Illinois, USA University of British Columbia, Vancouver, Canada

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