{"product_id":"choosing-and-using-statistics-9781405198394","title":"Choosing and Using Statistics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChoosing and Using Statistics\u003c\/i\u003e 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 approach to statistics with a strong focus on what is actually needed. There are chapters giving useful advice on the basics of statistics and guidance on the presentation of data. The book is built around a key to selecting the correct 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 (new to this edition) the free program, R. Only the basics of formal statistics are described and the emphasis is on jargon-free English but any unfamiliar words can be looked up in the extensive glossary. This new 3\u003csup\u003erd\u003c\/sup\u003e edition of \u003ci\u003eChoosing and Using Statistics\u003c\/i\u003e is a must for all students who use a computer package to apply statistics in practical and project work.  \u003cp\u003eFeatures ne\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Written in a concise and direct style, this book presents a selection of some of the most widely used statistical tests and data exploration techniques.\" (Biological Conservation, 1 March 2012)\u003cbr\u003e \u003cbr\u003e   \u003c\/p\u003e\u003cp\u003e\"Written in a concise and direct style, this book presents a selection of some of the most widely used statistical tests and data exploration techniques ... In general, this book is a very good primer for students with no statistical expertise.\" (Biological Conservation Reviews, 2011)\u003c\/p\u003e \u003cp\u003e\"This book makes everything so easy. Complicated tests are effortlessly condensed, and the instructions are almost too easy to follow. Diagrams and sample data sets are used frequently so you can practise using tests before applying them to your own data sets, whilst the logical layout guides you toward the correct test for both your data, and what you want to prove (or disprove).\" (\u003ci\u003eAnimals \u0026amp; Men\u003c\/i\u003e, February 2011)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface xiii  \u003cp\u003eThe third edition xiv\u003c\/p\u003e \u003cp\u003eHow to use this book xiv\u003c\/p\u003e \u003cp\u003ePackages used xv\u003c\/p\u003e \u003cp\u003eExample data xv\u003c\/p\u003e \u003cp\u003eAcknowledgements for the first edition xv\u003c\/p\u003e \u003cp\u003eAcknowledgements for the second edition xv\u003c\/p\u003e \u003cp\u003eAcknowledgements for the third edition xvi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Eight steps to successful data analysis 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The basics 2\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eObservations 2\u003c\/p\u003e \u003cp\u003eHypothesis testing 2\u003c\/p\u003e \u003cp\u003eP-values 3\u003c\/p\u003e \u003cp\u003eSampling 3\u003c\/p\u003e \u003cp\u003eExperiments 4\u003c\/p\u003e \u003cp\u003eStatistics 4\u003c\/p\u003e \u003cp\u003eDescriptive statistics 5\u003c\/p\u003e \u003cp\u003eTests of difference 5\u003c\/p\u003e \u003cp\u003eTests of relationships 5\u003c\/p\u003e \u003cp\u003eTests for data investigation 6\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Choosing a test: a key 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRemember: eight steps to successful data analysis 7\u003c\/p\u003e \u003cp\u003eThe art of choosing a test 7\u003c\/p\u003e \u003cp\u003eA key to assist in your choice of statistical test 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Hypothesis testing, sampling and experimental design 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHypothesis testing 23\u003c\/p\u003e \u003cp\u003eAcceptable errors 23\u003c\/p\u003e \u003cp\u003eP-values 24\u003c\/p\u003e \u003cp\u003eSampling 25\u003c\/p\u003e \u003cp\u003eChoice of sample unit 25\u003c\/p\u003e \u003cp\u003eNumber of sample units 26\u003c\/p\u003e \u003cp\u003ePositioning of sample units to achieve a random sample 26\u003c\/p\u003e \u003cp\u003eTiming of sampling 27\u003c\/p\u003e \u003cp\u003eExperimental design 27\u003c\/p\u003e \u003cp\u003eControl 28\u003c\/p\u003e \u003cp\u003eProcedural controls 28\u003c\/p\u003e \u003cp\u003eTemporal control 28\u003c\/p\u003e \u003cp\u003eExperimental control 29\u003c\/p\u003e \u003cp\u003eStatistical control 29\u003c\/p\u003e \u003cp\u003eSome standard experimental designs 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Statistics, variables and distributions 32\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat are statistics? 32\u003c\/p\u003e \u003cp\u003eTypes of statistics 33\u003c\/p\u003e \u003cp\u003eDescriptive statistics 33\u003c\/p\u003e \u003cp\u003eParametric statistics 33\u003c\/p\u003e \u003cp\u003eNon-parametric statistics 33\u003c\/p\u003e \u003cp\u003eWhat is a variable? 33\u003c\/p\u003e \u003cp\u003eTypes of variables or scales of measurement 34\u003c\/p\u003e \u003cp\u003eMeasurement variables 34\u003c\/p\u003e \u003cp\u003eContinuous variables 34\u003c\/p\u003e \u003cp\u003eDiscrete variables 35\u003c\/p\u003e \u003cp\u003eHow accurate do I need to be? 35\u003c\/p\u003e \u003cp\u003eRanked variables 35\u003c\/p\u003e \u003cp\u003eAttributes 35\u003c\/p\u003e \u003cp\u003eDerived variables 36\u003c\/p\u003e \u003cp\u003eTypes of distribution 36\u003c\/p\u003e \u003cp\u003eDiscrete distributions 36\u003c\/p\u003e \u003cp\u003eThe Poisson distribution 36\u003c\/p\u003e \u003cp\u003eThe binomial distribution 37\u003c\/p\u003e \u003cp\u003eThe negative binomial distribution 39\u003c\/p\u003e \u003cp\u003eThe hypergeometric distribution 39\u003c\/p\u003e \u003cp\u003eContinuous distributions 40\u003c\/p\u003e \u003cp\u003eThe rectangular distribution 40\u003c\/p\u003e \u003cp\u003eThe normal distribution 40\u003c\/p\u003e \u003cp\u003eThe standardized normal distribution 40\u003c\/p\u003e \u003cp\u003eConvergence of a Poisson distribution to a normal distribution 41\u003c\/p\u003e \u003cp\u003eSampling distributions and the 'central limit theorem' 41\u003c\/p\u003e \u003cp\u003eDescribing the normal distribution further 41\u003c\/p\u003e \u003cp\u003eSkewness 41\u003c\/p\u003e \u003cp\u003eKurtosis 43\u003c\/p\u003e \u003cp\u003eIs a distribution normal? 43\u003c\/p\u003e \u003cp\u003eTransformations 43\u003c\/p\u003e \u003cp\u003eAn example 44\u003c\/p\u003e \u003cp\u003eThe angular transformation 44\u003c\/p\u003e \u003cp\u003eThe logit transformation 45\u003c\/p\u003e \u003cp\u003eThe t-distribution 46\u003c\/p\u003e \u003cp\u003eConfidence intervals 47\u003c\/p\u003e \u003cp\u003eThe chi-square distribution 47\u003c\/p\u003e \u003cp\u003eThe exponential distribution 47\u003c\/p\u003e \u003cp\u003eNon-parametric 'distributions' 48\u003c\/p\u003e \u003cp\u003eRanking, quartiles and the interquartile range 48\u003c\/p\u003e \u003cp\u003eBox and whisker plots 48\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Descriptive and presentational techniques 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGeneral advice 49\u003c\/p\u003e \u003cp\u003eDisplaying data: summarizing a single variable 49\u003c\/p\u003e \u003cp\u003eBox and whisker plot (box plot) 49\u003c\/p\u003e \u003cp\u003eDisplaying data: showing the distribution of a single variable 50\u003c\/p\u003e \u003cp\u003eBar chart: for discrete data 50\u003c\/p\u003e \u003cp\u003eHistogram: for continuous data 51\u003c\/p\u003e \u003cp\u003ePie chart: for categorical data or attribute data 52\u003c\/p\u003e \u003cp\u003eDescriptive statistics 52\u003c\/p\u003e \u003cp\u003eStatistics of location or position 52\u003c\/p\u003e \u003cp\u003eArithmetic mean 53\u003c\/p\u003e \u003cp\u003eGeometric mean 53\u003c\/p\u003e \u003cp\u003eHarmonic mean 53\u003c\/p\u003e \u003cp\u003eMedian 53\u003c\/p\u003e \u003cp\u003eMode 53\u003c\/p\u003e \u003cp\u003eStatistics of distribution, dispersion or spread 55\u003c\/p\u003e \u003cp\u003eRange 55\u003c\/p\u003e \u003cp\u003eInterquartile range 55\u003c\/p\u003e \u003cp\u003eVariance 55\u003c\/p\u003e \u003cp\u003eStandard deviation (SD) 55\u003c\/p\u003e \u003cp\u003eStandard error (SE) 56\u003c\/p\u003e \u003cp\u003eConfidence intervals (CI) or confidence limits 56\u003c\/p\u003e \u003cp\u003eCoefficient of variation 56\u003c\/p\u003e \u003cp\u003eOther summary statistics 56\u003c\/p\u003e \u003cp\u003eSkewness 57\u003c\/p\u003e \u003cp\u003eKurtosis 57\u003c\/p\u003e \u003cp\u003eUsing the computer packages 57\u003c\/p\u003e \u003cp\u003eGeneral 57\u003c\/p\u003e \u003cp\u003eDisplaying data: summarizing two or more variables 62\u003c\/p\u003e \u003cp\u003eBox and whisker plots (box plots) 62\u003c\/p\u003e \u003cp\u003eError bars and confidence intervals 63\u003c\/p\u003e \u003cp\u003eDisplaying data: comparing two variables 63\u003c\/p\u003e \u003cp\u003eAssociations 63\u003c\/p\u003e \u003cp\u003eScatterplots 64\u003c\/p\u003e \u003cp\u003eMultiple scatterplots 64\u003c\/p\u003e \u003cp\u003eTrends, predictions and time series 65\u003c\/p\u003e \u003cp\u003eLines 65\u003c\/p\u003e \u003cp\u003eFitted lines 67\u003c\/p\u003e \u003cp\u003eConfidence intervals 67\u003c\/p\u003e \u003cp\u003eDisplaying data: comparing more than two variables 68\u003c\/p\u003e \u003cp\u003eAssociations 68\u003c\/p\u003e \u003cp\u003eThree-dimensional scatterplots 68\u003c\/p\u003e \u003cp\u003eMultiple trends, time series and predictions 69\u003c\/p\u003e \u003cp\u003eMultiple fitted lines 69\u003c\/p\u003e \u003cp\u003eSurfaces 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The tests 1: tests to look at differences 72\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDo frequency distributions differ? 72\u003c\/p\u003e \u003cp\u003eQuestions 72\u003c\/p\u003e \u003cp\u003eG-test 72\u003c\/p\u003e \u003cp\u003eAn example 73\u003c\/p\u003e \u003cp\u003eChi-square test 75\u003c\/p\u003e \u003cp\u003eAn example 76\u003c\/p\u003e \u003cp\u003eKolmogorov–Smirnov test 86\u003c\/p\u003e \u003cp\u003eAn example 87\u003c\/p\u003e \u003cp\u003eAnderson–Darling test 89\u003c\/p\u003e \u003cp\u003eShapiro–Wilk test 90\u003c\/p\u003e \u003cp\u003eGraphical tests for normality 90\u003c\/p\u003e \u003cp\u003eDo the observations from two groups differ? 92\u003c\/p\u003e \u003cp\u003ePaired data 92\u003c\/p\u003e \u003cp\u003ePaired t-test 92\u003c\/p\u003e \u003cp\u003eWilcoxon signed ranks test 96\u003c\/p\u003e \u003cp\u003eSign test 99\u003c\/p\u003e \u003cp\u003eUnpaired data 103\u003c\/p\u003e \u003cp\u003et-test 103\u003c\/p\u003e \u003cp\u003eOne-way ANOVA 111\u003c\/p\u003e \u003cp\u003eMann–Whitney U 119\u003c\/p\u003e \u003cp\u003eDo the observations from more than two groups differ? 123\u003c\/p\u003e \u003cp\u003eRepeated measures 123\u003c\/p\u003e \u003cp\u003eFriedman test (for repeated measures) 123\u003c\/p\u003e \u003cp\u003eRepeated-measures ANOVA 127\u003c\/p\u003e \u003cp\u003eIndependent samples 128\u003c\/p\u003e \u003cp\u003eOne-way ANOVA 129\u003c\/p\u003e \u003cp\u003ePost hoc testing: after one-way ANOVA 138\u003c\/p\u003e \u003cp\u003eKruskal–Wallis test 142\u003c\/p\u003e \u003cp\u003ePost hoc testing: after the Kruskal–Wallis test 145\u003c\/p\u003e \u003cp\u003eThere are two independent ways of classifying the data 145\u003c\/p\u003e \u003cp\u003eOne observation for each factor combination (no replication) 146\u003c\/p\u003e \u003cp\u003eFriedman test 146\u003c\/p\u003e \u003cp\u003eTwo-way ANOVA (without replication) 152\u003c\/p\u003e \u003cp\u003eMore than one observation for each factor combination (with\u003c\/p\u003e \u003cp\u003ereplication) 160\u003c\/p\u003e \u003cp\u003eInteraction 160\u003c\/p\u003e \u003cp\u003eTwo-way ANOVA (with replication) 163\u003c\/p\u003e \u003cp\u003eAn example 164\u003c\/p\u003e \u003cp\u003eScheirer–Ray–Hare test 175\u003c\/p\u003e \u003cp\u003eAn example 175\u003c\/p\u003e \u003cp\u003eThere are more than two independent ways to classify the data 182\u003c\/p\u003e \u003cp\u003eMultifactorial testing 182\u003c\/p\u003e \u003cp\u003eThree-way ANOVA (without replication) 183\u003c\/p\u003e \u003cp\u003eThree-way ANOVA (with replication) 184\u003c\/p\u003e \u003cp\u003eAn example 184\u003c\/p\u003e \u003cp\u003eMultiway ANOVA 191\u003c\/p\u003e \u003cp\u003eNot all classifications are independent 192\u003c\/p\u003e \u003cp\u003eNon-independent factors 192\u003c\/p\u003e \u003cp\u003eNested factors 192\u003c\/p\u003e \u003cp\u003eRandom or fixed factors 193\u003c\/p\u003e \u003cp\u003eNested or hierarchical designs 193\u003c\/p\u003e \u003cp\u003eTwo-level nested-design ANOVA 193\u003c\/p\u003e \u003cp\u003eAn example 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The tests 2: tests to look at relationships 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIs there a correlation or association between two variables? 199\u003c\/p\u003e \u003cp\u003eObservations assigned to categories 199\u003c\/p\u003e \u003cp\u003eChi-square test of association 199\u003c\/p\u003e \u003cp\u003eAn example 200\u003c\/p\u003e \u003cp\u003eCramér coefficient of association 208\u003c\/p\u003e \u003cp\u003ePhi coefficient of association 209\u003c\/p\u003e \u003cp\u003eObservations assigned a value 209\u003c\/p\u003e \u003cp\u003e'Standard' correlation (Pearson's product-moment correlation) 210\u003c\/p\u003e \u003cp\u003eAn example 210\u003c\/p\u003e \u003cp\u003eSpearman's rank-order correlation 214\u003c\/p\u003e \u003cp\u003eAn example 215\u003c\/p\u003e \u003cp\u003eKendall rank-order correlation 218\u003c\/p\u003e \u003cp\u003eAn example 218\u003c\/p\u003e \u003cp\u003eRegression 219\u003c\/p\u003e \u003cp\u003eAn example 220\u003c\/p\u003e \u003cp\u003eIs there a cause-and-effect relationship between two variables? 220\u003c\/p\u003e \u003cp\u003eQuestions 220\u003c\/p\u003e \u003cp\u003e'Standard' linear regression 221\u003c\/p\u003e \u003cp\u003ePrediction 221\u003c\/p\u003e \u003cp\u003eInterpreting r2 222\u003c\/p\u003e \u003cp\u003eComparison of regression and correlation 222\u003c\/p\u003e \u003cp\u003eResiduals 222\u003c\/p\u003e \u003cp\u003eConfidence intervals 222\u003c\/p\u003e \u003cp\u003ePrediction interval 223\u003c\/p\u003e \u003cp\u003eAn example 223\u003c\/p\u003e \u003cp\u003eKendall robust line-fit method 230\u003c\/p\u003e \u003cp\u003eLogistic regression 230\u003c\/p\u003e \u003cp\u003eAn example 231\u003c\/p\u003e \u003cp\u003eModel II regression 235\u003c\/p\u003e \u003cp\u003ePolynomial, cubic and quadratic regression 235\u003c\/p\u003e \u003cp\u003eTests for more than two variables 236\u003c\/p\u003e \u003cp\u003eTests of association 236\u003c\/p\u003e \u003cp\u003eQuestions 236\u003c\/p\u003e \u003cp\u003eCorrelation 236\u003c\/p\u003e \u003cp\u003ePartial correlation 237\u003c\/p\u003e \u003cp\u003eKendall partial rank-order correlation 237\u003c\/p\u003e \u003cp\u003eCause(s) and effect(s) 237\u003c\/p\u003e \u003cp\u003eQuestions 237\u003c\/p\u003e \u003cp\u003eRegression 237\u003c\/p\u003e \u003cp\u003eAnalysis of covariance (ANCOVA) 238\u003c\/p\u003e \u003cp\u003eMultiple regression 242\u003c\/p\u003e \u003cp\u003eStepwise regression 242\u003c\/p\u003e \u003cp\u003ePath analysis 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 The tests 3: tests for data exploration 244\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTypes of data 244\u003c\/p\u003e \u003cp\u003eObservation, inspection and plotting 244\u003c\/p\u003e \u003cp\u003ePrincipal component analysis (PCA) and factor analysis 244\u003c\/p\u003e \u003cp\u003eAn example 245\u003c\/p\u003e \u003cp\u003eCanonical variate analysis 251\u003c\/p\u003e \u003cp\u003eDiscriminant function analysis 251\u003c\/p\u003e \u003cp\u003eAn example 251\u003c\/p\u003e \u003cp\u003eMultivariate analysis of variance (MANOVA) 256\u003c\/p\u003e \u003cp\u003eAn example 256\u003c\/p\u003e \u003cp\u003eMultivariate analysis of covariance (MANCOVA) 259\u003c\/p\u003e \u003cp\u003eCluster analysis 259\u003c\/p\u003e \u003cp\u003eDECORANA and TWINSPAN 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSymbols and letters used in statistics 264\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGreek letters 264\u003c\/p\u003e \u003cp\u003eSymbols 264\u003c\/p\u003e \u003cp\u003eUpper-case letters 265\u003c\/p\u003e \u003cp\u003eLower-case letters 266\u003c\/p\u003e \u003cp\u003e\u003cb\u003eGlossary 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAssumptions of the tests 282\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat if the assumptions are violated? 284\u003c\/p\u003e \u003cp\u003e\u003cb\u003eHints and tips 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUsing a computer 285\u003c\/p\u003e \u003cp\u003eSampling 286\u003c\/p\u003e \u003cp\u003eStatistics 286\u003c\/p\u003e \u003cp\u003eDisplaying the data 287\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA table of statistical tests 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 291\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default Title","offer_id":49407931285847,"sku":"9781405198394","price":31.3,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781405198394.jpg?v=1730500997","url":"https:\/\/bookcurl.com\/products\/choosing-and-using-statistics-9781405198394","provider":"Book Curl","version":"1.0","type":"link"}