Statistical Methods in Agriculture and Experimental Biology: 3rd Edition (Paperback) book cover

Statistical Methods in Agriculture and Experimental Biology

3rd Edition

By Roger Mead

Chapman and Hall/CRC

488 pages

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Description

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
  • 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

    Subject Categories

    BISAC Subject Codes/Headings:
    MAT029000
    MATHEMATICS / Probability & Statistics / General