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Experimental design

Most popular at the top

  • Risk, Ambiguity and Decisionby Daniel Ellsberg

    Taylor and Francis 2015; US$ 47.95

    Ellsberg elaborates on "Risk, Ambiguity, and the Savage Axioms" and mounts a powerful challenge to the dominant theory of rational decision in this book. more...

  • SAS Essentialsby Alan C. Elliott; Wayne A. Woodward

    Wiley 2015; US$ 64.95 US$ 56.29

    A step-by-step introduction to using SAS® statistical software as a foundational approach to data analysis and interpretation Presenting a straightforward introduction from the ground up, SAS® Essentials: Mastering SAS for Data Analytics, Second Edition illustrates SAS using hands-on learning techniques and numerous real-world examples.... more...

  • Extending the Linear Model with Rby Julian J. Faraway

    CRC Press 2016; US$ 99.95

    Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those... more...

  • Linear Mixed Models for Longitudinal Databy Geert Verbeke; Geert Molenberghs

    Springer New York 2008; US$ 78.33

    This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing... more...

  • Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectivesby Andrew Gelman; Xiao-Li Meng

    Wiley 2004; US$ 145.00 US$ 125.67

    This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated... more...

  • Decision Spaceby Paul Weirich

    Cambridge University Press 2001; US$ 58.00

    This book, first published in 2001, combines traditional and novel methods of option evaluation into one systematic and versatile method. more...

  • Bayesian Models for Categorical Databy Peter Congdon

    Wiley 2005; US$ 137.00 US$ 118.73

    The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing... more...

  • Analysis of Messy Data, Volume IIIby George A. Milliken; Dallas E. Johnson

    CRC Press 2001; US$ 139.95

    Analysis of covariance is a very useful but often misunderstood methodology for analyzing data where important characteristics of the experimental units are measured but not included as factors in the design. Analysis of Messy Data, Volume 3: Analysis of Covariance takes the unique approach of treating the analysis of covariance problem by looking... more...

  • Analysis of Pretest-Posttest Designsby Peter L. Bonate

    CRC Press 2000; US$ 159.95

    How do you analyze pretest-posttest data? Difference scores? Percent change scores? ANOVA? In medical, psychological, sociological, and educational studies, researchers often design experiments in which they collect baseline (pretest) data prior to randomization. However, they often find it difficult to decide which method of statistical analysis is... more...

  • The Theory of the Design of Experimentsby D.R. Cox; Nancy Reid

    CRC Press 2000; US$ 139.95

    Why study the theory of experiment design? Although it can be useful to know about special designs for specific purposes, experience suggests that a particular design can rarely be used directly. It needs adaptation to accommodate the circumstances of the experiment. Successful designs depend upon adapting general theoretical principles to the special... more...