Statistical Methods in Agriculture and Experimental Biology,Third Edition / Edition 3

Statistical Methods in Agriculture and Experimental Biology,Third Edition / Edition 3

by Roger Mead, Robert N. Curnow, Anne M. Hasted
     
 

ISBN-10: 1584881879

ISBN-13: 9781584881872

Pub. Date: 08/28/2002

Publisher: Taylor & Francis

The third edition of this popular introductory text maintains the character that won worldwide respect for its predecessors but features a number of enhancements that broaden its scope, increase its utility, and bring the treatment thoroughly up to date. It provides complete coverage of the statistical ideas and methods essential to students in agriculture or

Overview

The third edition of this popular introductory text maintains the character that won worldwide respect for its predecessors but features a number of enhancements that broaden its scope, increase its utility, and bring the treatment thoroughly up to date. It provides complete coverage of the statistical ideas and methods essential to students in agriculture or experimental biology. In addition to covering fundamental methodology, this treatment also includes more advanced topics that the authors believe help develop an appreciation of the breadth of statistical methodology now available. The emphasis is not on mathematical detail, but on ensuring students understand why and when various methods should be used.

New in the Third Edition:

  • A chapter on the two simplest yet most important methods of multivariate analysis
  • Increased emphasis on modern computer applications
  • Discussions on a wider range of data types and the graphical display of data
  • Analysis of mixed cropping experiments and on-farm experiments
  • Product Details

    ISBN-13:
    9781584881872
    Publisher:
    Taylor & Francis
    Publication date:
    08/28/2002
    Series:
    Texts in Statistical Science Series
    Edition description:
    REV
    Pages:
    488
    Product dimensions:
    5.90(w) x 9.10(h) x 1.00(d)

    Table of Contents

    INTRODUCTION
    The Need for Statistics
    Types of Data
    The Use of Computers in Statistics

    PROBABILITY AND DISTRIBUTIONS
    Probability
    Populations and Samples
    Means and Variances
    The Normal Distribution
    Sampling Distributions

    ESTIMATION AND HYPOTHESIS TESTING
    Estimation of the Population Mean
    Testing Hypotheses about the Population Mean
    Population Variance Unknown
    Comparison of Samples
    A Pooled Estimate of Variance

    A SIMPLE EXPERIMENT
    Randomization and Replication
    Analysis of a Completely Randomized Design with Two Treatments
    A Completely Randomized Design with Several Treatments
    Testing Overall Variation Between the Treatments

    CONTROL OF RANDOM VARIATION BY BLOCKING
    Local Control of Variation
    Analysis of a Randomized Block Design
    Meaning of the Error Mean Square
    Latin Square Designs
    Multiple Latin Squares Design
    The Benefit of Blocking and the Use of Natural Blocks

    PARTICULAR QUESTIONS ABOUT TREATMENTS
    Treatment Structure
    Treatment Contrasts
    Factorial Treatment Structure
    Main Effects and Interactions
    Analysis of Variance for a Two-Factor Experiment
    Partial Factorial Structure
    Comparing Treatment Means - Are Multiple Comparison Methods Helpful?

    MORE ON FACTORIAL TREATMENT STRUCTURE
    More than Two Factors
    Factors with Two Levels
    The Double Benefit of Factorial Structure
    Many Factors and Small Blocks
    The Analysis of Confounded Experiments
    Split Plot Experiments
    Analysis of a Split Plot Experiment
    Experiments Repeated at Different Sites

    THE ASSUMPTIONS BEHIND THE ANALYSIS
    Our Assumptions
    Normality
    Variance Homogeneity
    Additivity
    Transformations of Data for Theoretical Reasons
    A More General Form of Analysis
    Empirical Detection of the Failure of Assumptions and Selection of Appropriate Transformations
    Practice and Presentation

    STUDYING LINEAR RELATIONSHIPS
    Linear Regression
    Assessing the Regression Line
    Inferences about the Slope of a Line
    Prediction Using a Regression Line
    Correlation
    Testing Whether the Regression is Linear
    Regression Analysis Using Computer Packages

    MORE COMPLEX RELATIONSHIPS
    Making the Crooked Straight
    Two Independent Variables
    Testing the Components of a Multiple Relationship
    Multiple Regression
    Possible Problems in Computer Multiple Regression

    LINEAR MODELS
    The Use of Models
    Models for Factors and Variables
    Comparison of Regressions
    Fitting Parallel Lines
    Covariance Analysis
    Regression in the Analysis of Treatment Variation

    NONLINEAR MODELS
    Advantages of Linear and Nonlinear Models
    Fitting Nonlinear Models to Data
    Inferences about Nonlinear Parameters
    Exponential Models
    Inverse Polynomial Models
    Logistic Models for Growth Curves

    THE ANALYSIS OF PROPORTIONS
    Data in the Form of Frequencies
    The 2 × 2 Contingency Table
    More than Two Situations or More than Two Outcomes
    General Contingency Tables
    Estimation of Proportions
    Sample Sizes for Estimating Proportions

    MODELS AND DISTRIBUTIONS FOR FREQUENCY DATA
    Models for Frequency Data
    Testing the Agreement of Frequency Data with Simple Models
    Investigating More Complex Models
    The Binomial Distribution
    The Poisson Distribution
    Generalized Models for Analyzing Experimental Data
    Log-Linear Models
    Logit Analysis of Response Data

    MAKING AND ANALYZING SEVERAL EXPERIMENTAL MEASUREMENTS
    Different Measurements on the Same Units
    Interdependence of Different Variables
    Repeated Measurements
    Joint (Bivariate) Analysis
    Indices of Combined Yield
    Investigating Relationships with Experimental Data

    ANALYZING AND SUMMARIZING MANY MEASUREMENTS
    Introduction to Multivariate Data
    Principal Component Analysis
    Covariance or Correlation Matrix
    Cluster Analysis
    Similarity and Dissimilarity Measures
    Hierarchical Clustering
    Comparison of PCA and Cluster Analysis

    CHOOSING THE MOST APPROPRIATE EXPERIMENTAL DESIGN
    The Components of Design; Units and Treatments
    Replication and Precision
    Different Levels of Variation and Within-Unit Replication
    Variance Components and Split Plot Designs
    Randomization
    Managing with Limited Resources
    Factors with Quantitative Levels
    Screening and Selection
    On-Farm Experiments

    SAMPLING FINITE POPULATIONS
    Experiments and Sample Surveys
    Simple Random Sampling
    Stratified Random Sampling
    Cluster Sampling, Multistage Sampling and
    Sampling Proportional to Size
    Ratio and Regression Estimates

    REFERENCES
    APPENDIX
    INDEX

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