Synopses & Reviews
Agricultural Experimentation Design and Analysis Thomas M. Little, University of California, Riverside F. Jackson Hills, University of California, Davis For agricultural researchers and students who want to learn the principles of designing and conducting agricultural experiments, but do not have the time to master the details of abstract mathematics, here is the ideal bookstraightforward and simple. Little and Hills offer on easy-to-understand, easy-to-apply discussions of the most common statistical methods and experimental designs used in agricultural research, with step-by-step procedures for the analysis of the results. Keeping complicated statistical notation to a minimum, the authors explain the logic behind the most common experimental designs used in agriculture. The authors show how to design your own experiments properly and to draw valid conclusions from the results. The spiral binding lets you open the book flat, so it can be easily used as a workbook for following the steps of any particular procedure.
Synopsis
A simple, straightforward presentation of basic statistical methods and experimental designs with emphasis on how to compute essential statistics. Introduces principles of experimentation and explains common experimental designs; detailed, step-by-step procedures show the logic and reasoning behind each analysis. Includes sections on correlation and regression, analysis of counts, and mean separation, with especially thorough coverage of transformations and the analysis of variance.
Table of Contents
Logic, Research, and Experiment.
Some Basic Concepts.
The Analysis of Variance and t Tests.
A Population of Mean Differences.
The Completely Randomized Design.
The Randomized Complete Block Design.
Mean Separation.
Latin Square Design.
The Split-Plot Design.
The Split-Split Plot.
The Split Block.
Subplots as Repeated Observations.
Transformations.
Linear Correlation and Regression.
Curvilinear Relations.
Shortcut Regression Methods for Equally Spaced Observations or Treatments.
Correlation and Regression for More Than Two Variables.
Analysis of Counts.
Improving Precision.
Planned Grouping of Experimental Units--Design.
Appendix.
Index.