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    1. Naturvetenskap och teknik
    2. Matematik och naturvetenskap
    3. Matematik
    4. Matematisk statistik

    Choosing and Using Statistics

    A Biologist's Guide

    AvCalvin Dytham

    Häftad, Engelska, 2026

    504 kr

    Kommande

    Fler format och utgåvor

    Inbunden

    1 685 kr

    Häftad

    454 kr

    E-bok

    489 kr

    E-bok

    552 kr

    Beskrivning

    Choosing and Using Statistics remains an invaluable guide for students using a computer package to analyse data from research projects and practical class work. The text takes a pragmatic and straightforward approach to the application of statistical tests in biology. There are chapters giving useful advice on the basics of statistics, experimental design and guidance on the presentation of data. The book is built around a key to selecting an appropriate statistical test and then gives clear guidance on how to carry out the test and interpret the output from four commonly used computer packages: SPSS, Minitab, Excel, and R. Only the basics of formal statistics are described and the emphasis is on jargon-free English. Key features of the book are that it: Uses a simple key and flow chart to help you choose the right statistical testIs aimed at students using statistics for projects and in practical classesIncludes an extensive glossary and key to symbols to explain any statistical jargonAssumes no previous knowledge or experience of statisticsThis fourth edition of Choosing and Using Statistics is a trusted companion for all students who use a computer package to apply statistics in practical and project work.

    Produktinformation

    • Utgivningsdatum:2026-10-22
    • Mått:178 x 254 x undefined mm
    • Format:Häftad
    • Språk:Engelska
    • Antal sidor:400
    • Upplaga:4
    • Förlag:John Wiley & Sons Inc
    • ISBN:9781119838128

    Utforska kategorier

    • Matematisk statistik inom Naturvetenskap och teknik
    • Biologi inom Naturvetenskap och teknik

    Mer om författaren

    Calvin Dytham has a wide range of research interests in ecology and evolutionary biology from bacteria-phage interactions to human evolution. He taught statistics to undergraduate and postgraduate students for more than thirty years and is now Professor Emeritus in the Department of Biology at the University of York, UK.

    Innehållsförteckning

    • PrefaceHow to use this bookPackages usedExample dataAcknowledgements1 - Eight steps to successful data analysis2 - The basicsObservationsHypothesis testingP-valuesSamplingExperimentsStatistics:Descriptive statisticsTests of differenceTests of relationshipsTests for data investigation3 - Choosing a test: a keyRemember - eight steps to successful data analysisThe art of choosing a testA key to assist in your choice of statistical test4 - Hypothesis testing, sampling and experimental designHypothesis testingAcceptable errorsP-valuesSampling Choice of sample unitNumber of sample unitsPositioning of sample units to achieve a random sampleTiming of samplingExperimental designControlProcedural controlsTemporal controlExperimental controlStatistical controlSome standard experimental designs5 - Statistics, variables and distributionsWhat are statistics?Types of statisticsDescriptive statisticsParametric statisticsNon-parametric statisticsWhat is a variable?Types of variables or scales of measurement Measurement variablesContinuous variablesDiscrete variablesHow accurate do I need to be?Ranked variablesAttributesDerived variablesTypes of DistributionsWhy do you need to know about distributions?Discrete distributionsPoisson distributionBinomial distributionNegative binomial distributionHypergeometric distributionContinuous distributionsRectangular distributionNormal distribution Standardised normal distributionConvergence of a Poisson distribution to a normal distributionSampling distributions and the ‘central limit theorem’Describing the normal distribution furtherSkewnessKurtosisIs a distribution normal?TransformationsAngular transformationLogit transformationt-distributionConfidence intervalsChi-square distributionExponential distributionNon-parametric ‘distributions’Ranking, quartiles and the inter-quartile rangeBox and whisker Plots6 - Descriptive and presentational techniquesGeneral adviceDisplaying data: summarising a single variableBox and whisker (Box plot)Displaying data: showing the distribution of a single variableBar chartHistogramNumber of classes to display in a histogramPie chartDescriptive statisticsStatistics of locationArithmetic meanGeometric meanHarmonic meanMedianModeStatistics of distribution, dispersion or spreadRangeInterquartile rangeVarianceStandard deviationStandard errorConfidence intervalsCoefficient of variationOther summary statisticsSkewnessKurtosisUsing the computer packagesDisplaying data: summarising two or more variablesBox and whisker (box plots)Error bars and confidence intervalsDisplaying data: comparing two variablesAssociationsScatterplotsMultiple scatterplotsTrends, predictions and time seriesLinesFitted linesConfidence intervalsDisplaying data: comparing more than two variablesAssociationsThree-dimensional scatterplotsMultiple trends, time series and predictionsMultiple fitted linesSurfaces7 - The tests 1: tests to look at differencesDo frequency distributions differ?QuestionsG-testChi-square test (2)Kolmogorov-Smirnov testAnderson-Darling testShapiro-Wilk test Do the observations from two groups differ?Paired dataPaired t-testWilcoxon’s signed ranks testSign test Unpaired dataT-test  One-way ANOVAMann-Whitney UDo the observations from more than two groups differ?Repeated measures  Friedman test (for repeated measures)Repeated measures ANOVAIndependent samplesOne-way ANOVAPost hoc testing: after one-way ANOVAKruskal-Wallis testPost hoc testing: after the Kruskal-Wallis testThere are two independent ways of classifying the dataOne observation for each factor combination (no replication)Friedman testTwo-way ANOVA (without replication)More than one observation for each factor combination (with replication)InteractionTwo-way ANOVA (with replication)Scheirer-Ray-Hare testThere are more than two independent ways to classify the dataMulti-factorial testingThree-way ANOVA (without replication)Three-way ANOVA (with replication)Multi-way ANOVANot all classifications are independentNon-independent factorsNested factorsRandom or fixed factorsNested / hierarchical designsTwo-level nested design ANOVA8 – The tests 2: tests to look at relationshipsIs there a correlation or association between two variables?Observations assigned to categoriesChi-square test of associationCramér coefficient of associationPhi coefficient of associationObservations assigned a value“Standard” correlation (Pearson’s product-moment correlation)Spearman’s rank-order correlationKendall’s rank-order correlationIs there a ‘cause and effect’ relationship between two variables?Questions“Standard” linear regressionPredictionInterpreting r2Comparison of regression and correlationResidualsConfidence intervalsPrediction intervalsKendall’s robust line-fit methodJonckheer-Terpstra testLogistic regressionModel II regressionPolynomial, cubic and quadratic regressionTests for more than two variablesTests of associationQuestionsCorrelationPartial correlationKendall’s partial rank-order correlationCause(s) and effect(s)Questions.RegressionANCOVA (analysis of covariance)Multiple regressionStepwise regressionPath analysis9 - The tests 3: tests for data explorationTypes of dataObservation, inspection and plottingPrincipal component and factor analysis (PCA)Canonical variate analysis (CVA)Discriminant function analysisMultivariate analysis of variance (MANOVA)Multivariate analysis of covariance (MANCOVA)Cluster analysis10 - Symbols and letters used in statisticsGreek lettersSymbolsUpper case lettersLower case letters11 - Glossary12 - Assumptions of the testsTest by test list of assumptionsWhat if the assumptions are violated?13 - Hints and tipsUsing a computerSamplingStatisticsDisplaying the data14 - A table of statistical tests