دانلود کتاب Statistical Methods in Agriculture and Experimental Biology

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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