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Academic & Professional Books  Reference  Data Analysis & Modelling  Data Analysis & Statistics

Statistical Methods in Agriculture and Experimental Biology

By: Roger Mead, Robert N Curnow and Anne M Hasted
472 pages, Figs, tabs
Publisher: CRC Press
Statistical Methods in Agriculture and Experimental Biology
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  • Statistical Methods in Agriculture and Experimental Biology ISBN: 9781584881872 Edition: 3 Paperback Dec 2002 Usually dispatched within 1-2 weeks
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About this book Contents Customer reviews Related titles

About this book

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.

Contents

INTRODUCTIONThe Need for StatisticsTypes of DataThe Use of Computers in StatisticsPROBABILITY AND DISTRIBUTIONSProbabilityPopulations and SamplesMeans and VariancesThe Normal DistributionSampling DistributionsESTIMATION AND HYPOTHESIS TESTINGEstimation of the Population MeanTesting Hypotheses about the Population MeanPopulation Variance UnknownComparison of SamplesA Pooled Estimate of VarianceA SIMPLE EXPERIMENTRandomization and ReplicationAnalysis of a Completely Randomized Design with Two TreatmentsA Completely Randomized Design with Several TreatmentsTesting Overall Variation Between the TreatmentsCONTROL OF RANDOM VARIATION BY BLOCKINGLocal Control of VariationAnalysis of a Randomized Block DesignMeaning of the Error Mean SquareLatin Square DesignsMultiple Latin Squares DesignThe Benefit of Blocking and the Use of Natural BlocksPARTICULAR QUESTIONS ABOUT TREATMENTSTreatment StructureTreatment ContrastsFactorial Treatment StructureMain Effects and InteractionsAnalysis of Variance for a Two-Factor ExperimentPartial Factorial StructureComparing Treatment Means - Are Multiple Comparison Methods Helpful?MORE ON FACTORIAL TREATMENT STRUCTUREMore than Two FactorsFactors with Two LevelsThe Double Benefit of Factorial StructureMany Factors and Small BlocksThe Analysis of Confounded ExperimentsSplit Plot ExperimentsAnalysis of a Split Plot ExperimentExperiments Repeated at Different SitesTHE ASSUMPTIONS BEHIND THE ANALYSISOur AssumptionsNormalityVariance HomogeneityAdditivityTransformations of Data for Theoretical ReasonsA More General Form of AnalysisEmpirical Detection of the Failure of Assumptions and Selection of Appropriate TransformationsPractice and PresentationSTUDYING LINEAR RELATIONSHIPSLinear RegressionAssessing the Regression LineInferences about the Slope of a LinePrediction Using a Regression LineCorrelationTesting Whether the Regression is LinearRegression Analysis Using Computer PackagesMORE COMPLEX RELATIONSHIPSMaking the Crooked StraightTwo Independent VariablesTesting the Components of a Multiple RelationshipMultiple RegressionPossible Problems in Computer Multiple RegressionLINEAR MODELSThe Use of ModelsModels for Factors and VariablesComparison of RegressionsFitting Parallel LinesCovariance AnalysisRegression in the Analysis of Treatment VariationNONLINEAR MODELSAdvantages of Linear and Nonlinear ModelsFitting Nonlinear Models to DataInferences about Nonlinear ParametersExponential ModelsInverse Polynomial ModelsLogistic Models for Growth CurvesTHE ANALYSIS OF PROPORTIONSData in the Form of FrequenciesThe 2 2 Contingency TableMore than Two Situations or More than Two OutcomesGeneral Contingency TablesEstimation of ProportionsSample Sizes for Estimating ProportionsMODELS AND DISTRIBUTIONS FOR FREQUENCY DATAModels for Frequency D

Customer Reviews

By: Roger Mead, Robert N Curnow and Anne M Hasted
472 pages, Figs, tabs
Publisher: CRC Press
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