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Statistics for the Biological Sciences Books

You are currently browsing 11–20 of 160 new and published books in the subject of Statistics for the Biological Sciences — sorted by publish date from newer books to older books.

For books that are not yet published; please browse forthcoming books.

New and Published Books – Page 2

  1. A Handbook of Statistical Graphics Using SAS ODS

    By Geoff Der, Brian S. Everitt

    Easily Use SAS to Produce Your Graphics Diagrams, plots, and other types of graphics are indispensable components in nearly all phases of statistical analysis, from the initial assessment of the data to the selection of appropriate statistical models to the diagnosis of the chosen models once they...

    Published August 14th 2014 by Chapman and Hall/CRC

  2. Analysis of Categorical Data with R

    By Christopher R. Bilder, Thomas M. Loughin

    Series: Chapman & Hall/CRC Texts in Statistical Science

    Learn How to Properly Analyze Categorical DataAnalysis of Categorical Data with R presents a modern account of categorical data analysis using the popular R software. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses...

    Published August 10th 2014 by Chapman and Hall/CRC

  3. Analysis of Capture-Recapture Data

    By Rachel S. McCrea, Byron J. T. Morgan

    Series: Chapman & Hall/CRC Interdisciplinary Statistics

    An important first step in studying the demography of wild animals is to identify the animals uniquely through applying markings, such as rings, tags, and bands. Once the animals are encountered again, researchers can study different forms of capture-recapture data to estimate features, such as the...

    Published July 31st 2014 by Chapman and Hall/CRC

  4. Linear Mixed Models

    A Practical Guide Using Statistical Software, Second Edition

    By Brady T. West, Kathleen B. Welch, Andrzej T Galecki

    Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues...

    Published July 16th 2014 by Chapman and Hall/CRC

  5. SAS and R

    Data Management, Statistical Analysis, and Graphics, Second Edition

    By Ken Kleinman, Nicholas J. Horton

    An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent TasksThe first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis,...

    Published July 16th 2014 by Chapman and Hall/CRC

  6. Linear Models with R, Second Edition

    By Julian J. Faraway

    Series: Chapman & Hall/CRC Texts in Statistical Science

    A Hands-On Way to Learning Data Analysis Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with...

    Published June 30th 2014 by Chapman and Hall/CRC

  7. A Handbook of Statistical Analyses using R, Third Edition

    By Torsten Hothorn, Brian S. Everitt

    Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive...

    Published June 24th 2014 by Chapman and Hall/CRC

  8. Introduction to Multivariate Analysis

    Linear and Nonlinear Modeling

    By Sadanori Konishi

    Series: Chapman & Hall/CRC Texts in Statistical Science

    Select the Optimal Model for Interpreting Multivariate Data Introduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random...

    Published June 5th 2014 by Chapman and Hall/CRC

  9. Robust Response Surfaces, Regression, and Positive Data Analyses

    By Rabindra Nath Das

    Although widely used in science and technology for experimental data generating, modeling, and optimization, the response surface methodology (RSM) has many limitations. Showing how robust response surface methodology (RRSM) can overcome these limitations, Robust Response Surfaces, Regression, and...

    Published May 20th 2014 by Chapman and Hall/CRC

  10. Implementing Reproducible Research

    Edited by Victoria Stodden, Friedrich Leisch, Roger D. Peng

    Series: Chapman & Hall/CRC The R Series

    In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must...

    Published April 13th 2014 by Chapman and Hall/CRC