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Applying contemporary statistical techniques /

Applying Contemporary Statistical Techniques explains why traditional statistical methods are often inadequate or outdated when applied to modern problems. Wilcox demonstrates how new and more powerful techniques address these problems far more effectively, making these modern robust methods underst...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Wilcox, Rand R.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; Boston : Academic Press, �2003.
Temas:
Acceso en línea:Texto completo

MARC

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245 1 0 |a Applying contemporary statistical techniques /  |c Rand R. Wilcox. 
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520 |a Applying Contemporary Statistical Techniques explains why traditional statistical methods are often inadequate or outdated when applied to modern problems. Wilcox demonstrates how new and more powerful techniques address these problems far more effectively, making these modern robust methods understandable, practical, and easily accessible. * Assumes no previous training in statistics * Explains how and why modern statistical methods provide more accurate results than conventional methods * Covers the latest developments on multiple comparisons * Includes recent advances in risk-based methods * Features many illustrations and examples using data from real studies * Describes and illustrates easy-to-use s-plus functions for applying cutting-edge techniques * Covers many contemporary ANOVA (analysis of variance) and regression methods not found in other books. 
505 0 |a Introduction; Probability and Related Concepts; Summarizing Data; Sampling Distributions and Confidence Intervals; Hypothesis Testing; Least Squares Regression and Pearson's Correlation; Basic Bootstrap Methods; Comparing Two Independent Groups; One-Way Anova; Two-Way Anova; Comparing Dependent Groups; Multiple Comparisons; Detecting Outliers in Multivariate Data; More Regression Methods; Rank-Based and Nonparametric Methods. 
504 |a Includes bibliographical references and index. 
505 0 |a 1. Introduction -- 2. Probability and related concepts -- 3. Summarizing data -- 4. Sampling distributions and confidence intervals -- 5. Hypothesis testing -- 6. Least squares regression and Pearson's correlation -- 7. Basic bootstrap methods -- 8. Comparing two independent groups -- 9. One-way ANOVA -- 10. Two-way ANOVA -- 11. Comparing dependent groups -- 12. Multiple comparisons -- 13. Robust and exploratory regression -- 14. More regression methods -- 15. Rank-based and nonparametric methods. 
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