{"product_id":"categorical-data-analysis-by-example-isbn-9781119307860","title":"Categorical Data Analysis by Example","description":"\u003cp\u003e\u003cb\u003eIntroduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying R code\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicated mathematics and without needing to wade through a large amount of prose. It is aimed at researchers with their own data ready to be analyzed and at students who would like an approachable alternative view of the subject.\u003c\/p\u003e \u003cp\u003eEach new topic in categorical data analysis is illustrated with an example that readers can apply to their own sets of data. In many cases, R code is given and excerpts from the resulting output are presented. In the context of log-linear models for cross-tabulations, two specialties of the house have been included: the use of cobweb diagrams to get visual information concerning significant interactions, and a procedure for detecting outlier category combinations. The R code used for these is available and may be freely adapted. In addition, this book:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eUses an example to illustrate each new topic in categorical data\u003c\/li\u003e \u003cli\u003eProvides a clear explanation of an important subject\u003c\/li\u003e \u003cli\u003eIs understandable to most readers with minimal statistical and mathematical backgrounds\u003c\/li\u003e \u003cli\u003eContains examples that are accompanied by R code and resulting output\u003c\/li\u003e \u003cli\u003eIncludes starred sections that provide more background details for interested readers\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eCategorical Data Analysis by Example \u003c\/i\u003eis a reference for students in statistics and researchers in other disciplines, especially the social sciences, who use categorical data. This book is also a reference for practitioners in market research, medicine, and other fields.\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAcknowledgments xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 What are categorical data? 1\u003c\/p\u003e \u003cp\u003e1.2 A typical data set 2\u003c\/p\u003e \u003cp\u003e1.3 Visualisation and crosstabulation 3\u003c\/p\u003e \u003cp\u003e1.4 Samples, populations, and random variation 4\u003c\/p\u003e \u003cp\u003e1.5 Proportion, probability and conditional probability 5\u003c\/p\u003e \u003cp\u003e1.6 Probability distributions 6\u003c\/p\u003e \u003cp\u003e1.6.1 The binomial distribution 6\u003c\/p\u003e \u003cp\u003e1.6.2 The multinomial distribution 7\u003c\/p\u003e \u003cp\u003e1.6.3 The Poisson distribution 7\u003c\/p\u003e \u003cp\u003e1.6.4 The normal distribution 7\u003c\/p\u003e \u003cp\u003e1.6.5 The chisquared (\u003ci\u003eX\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e) distribution 8\u003c\/p\u003e \u003cp\u003e1.7 *The likelihood 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Estimation and inference for categorical data 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Goodness of fit 11\u003c\/p\u003e \u003cp\u003e2.1.1 Pearson’s\u003ci\u003e X\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e goodness-of-fit statistic 11\u003c\/p\u003e \u003cp\u003e2.1.2 * The link between \u003ci\u003eX\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e and the Poisson and ­\u003csup\u003eI2 \u003c\/sup\u003edistributions 12\u003c\/p\u003e \u003cp\u003e2.1.3 The likelihood-ratio goodness-of-fit statistic, \u003ci\u003eG\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e 13\u003c\/p\u003e \u003cp\u003e2.1.4 * Why the \u003ci\u003eG\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e and \u003ci\u003eX\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e statistics usually have similar values 14\u003c\/p\u003e \u003cp\u003e2.2 Hypothesis tests for a binomial proportion (large sample) 14\u003c\/p\u003e \u003cp\u003e2.2.1 The normal score test 14\u003c\/p\u003e \u003cp\u003e2.2.2 * Link to Pearson’s \u003ci\u003eX\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e goodness-of-fit test 15\u003c\/p\u003e \u003cp\u003e2.2.3 G2 for a binomial proportion 15\u003c\/p\u003e \u003cp\u003e2.3 Hypothesis tests for a binomial proportion (small sample) 16\u003c\/p\u003e \u003cp\u003e2.3.1 One-tailed hypothesis test 16\u003c\/p\u003e \u003cp\u003e2.3.2 Two-tailed hypothesis tests 17\u003c\/p\u003e \u003cp\u003e2.4 Interval estimates for a binomial proportion 18\u003c\/p\u003e \u003cp\u003e2.4.1 Laplace’s method 18\u003c\/p\u003e \u003cp\u003e2.4.2 Wilson’s method 18\u003c\/p\u003e \u003cp\u003e2.4.3 The Agresti-Coull method 19\u003c\/p\u003e \u003cp\u003e2.4.4 Small samples and exact calculations 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 The 2 X 2 contingency table 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 23\u003c\/p\u003e \u003cp\u003e3.2 Fisher’s exact test (for independence) 24\u003c\/p\u003e \u003cp\u003e3.2.1 * Derivation of the exact test formula 26\u003c\/p\u003e \u003cp\u003e3.3 Testing independence with large cell frequencies 27\u003c\/p\u003e \u003cp\u003e3.3.1 Using Pearson’s goodness-of-fit test 27\u003c\/p\u003e \u003cp\u003e3.3.2 The Yates correction 28\u003c\/p\u003e \u003cp\u003e3.4 The 2 X 2 table in a medical context 29\u003c\/p\u003e \u003cp\u003e3.5 Measuring lack of independence (comparing proportions) 31\u003c\/p\u003e \u003cp\u003e3.5.1 Difference of proportions 31\u003c\/p\u003e \u003cp\u003e3.5.2 Relative risk 32\u003c\/p\u003e \u003cp\u003e3.5.3 Odds-ratio 33\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 The\u003ci\u003e I\u003c\/i\u003e x \u003ci\u003eJ\u003c\/i\u003e contingency table 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Notation 37\u003c\/p\u003e \u003cp\u003e4.2 Independence in the \u003ci\u003eI\u003c\/i\u003e X \u003ci\u003eJ\u003c\/i\u003e contingency table 38\u003c\/p\u003e \u003cp\u003e4.2.1 Estimation and degrees of freedom 38\u003c\/p\u003e \u003cp\u003e4.2.2 Odds-ratios and independence 39\u003c\/p\u003e \u003cp\u003e4.2.3 Goodness-of-fit and lack of fit of the independence model 39\u003c\/p\u003e \u003cp\u003e4.3 Partitioning 42\u003c\/p\u003e \u003cp\u003e4.3.1 * Additivity of G\u003csup\u003e2\u003c\/sup\u003e 42\u003c\/p\u003e \u003cp\u003e4.3.2 Rules for partitioning 44\u003c\/p\u003e \u003cp\u003e4.4 Graphical displays 44\u003c\/p\u003e \u003cp\u003e4.4.1 Mosaic plots 45\u003c\/p\u003e \u003cp\u003e4.4.2 Cobweb diagrams 45\u003c\/p\u003e \u003cp\u003e4.5 Testing independence with ordinal variables 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 The exponential family 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 51\u003c\/p\u003e \u003cp\u003e5.2 The exponential family 52\u003c\/p\u003e \u003cp\u003e5.2.1 The exponential dispersion family 53\u003c\/p\u003e \u003cp\u003e5.3 Components of a general linear model 53\u003c\/p\u003e \u003cp\u003e5.4 Estimation 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 A model taxonomy 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Underlying questions 57\u003c\/p\u003e \u003cp\u003e6.1.1 Which variables are of interest? 57\u003c\/p\u003e \u003cp\u003e6.1.2 What categories should be used? 58\u003c\/p\u003e \u003cp\u003e6.1.3 What is the type of each variable? 58\u003c\/p\u003e \u003cp\u003e6.1.4 What is the nature of each variable? 58\u003c\/p\u003e \u003cp\u003e6.2 Identifying the type of model 58\u003c\/p\u003e \u003cp\u003e7 The \u003ci\u003e2\u003c\/i\u003e X \u003ci\u003eJ\u003c\/i\u003e contingency table 61\u003c\/p\u003e \u003cp\u003e7.1 A problem with \u003ci\u003eX\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e (and G2) 61\u003c\/p\u003e \u003cp\u003e7.2 Using the logit 62\u003c\/p\u003e \u003cp\u003e7.2.1 Estimation of the logit 63\u003c\/p\u003e \u003cp\u003e7.2.2 The null model 64\u003c\/p\u003e \u003cp\u003e7.3 Individual data and grouped data 64\u003c\/p\u003e \u003cp\u003e7.4 Precision, confidence intervals, and prediction intervals 69\u003c\/p\u003e \u003cp\u003e7.4.1 Prediction intervals 70\u003c\/p\u003e \u003cp\u003e7.5 Logistic regression with a categorical explanatory variable 70\u003c\/p\u003e \u003cp\u003e7.5.1 Parameter estimates with categorical variables (\u003ci\u003eJ\u003c\/i\u003e \u0026gt; \u003ci\u003e2\u003c\/i\u003e) 73\u003c\/p\u003e \u003cp\u003e7.5.2 The dummy variable representation of a categorical variable 74\u003c\/p\u003e \u003cp\u003e8 Logistic regression with several explanatory variables 77\u003c\/p\u003e \u003cp\u003e8.1 Degrees of freedom when there are no interactions 77\u003c\/p\u003e \u003cp\u003e8.2 Getting a feel for the data 79\u003c\/p\u003e \u003cp\u003e8.3 Models with two variable interactions 81\u003c\/p\u003e \u003cp\u003e8.3.1 Link to the testing of independence between two variables 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Model selection and diagnostics 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 85\u003c\/p\u003e \u003cp\u003e9.1.1 Ockham’s razor 86\u003c\/p\u003e \u003cp\u003e9.2 Notation for interactions and for models 87\u003c\/p\u003e \u003cp\u003e9.3 Stepwise methods for model selection using G2 89\u003c\/p\u003e \u003cp\u003e9.3.1 Forward selection 89\u003c\/p\u003e \u003cp\u003e9.3.2 Backward elimination 91\u003c\/p\u003e \u003cp\u003e9.3.3 Complete stepwise 93\u003c\/p\u003e \u003cp\u003e9.4 AIC and related measures 93\u003c\/p\u003e \u003cp\u003e9.5 The problem caused by rare combinations of events 95\u003c\/p\u003e \u003cp\u003e9.5.1 Tackling the problem 96\u003c\/p\u003e \u003cp\u003e9.6 Simplicity versus accuracy 98\u003c\/p\u003e \u003cp\u003e9.7 DFBETAS 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Multinomial logistic regression 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 A single continuous explanatory variable 103\u003c\/p\u003e \u003cp\u003e10.2 Nominal categorical explanatory variables 106\u003c\/p\u003e \u003cp\u003e10.3 Models for an ordinal response variable 108\u003c\/p\u003e \u003cp\u003e10.3.1 Cumulative logits 108\u003c\/p\u003e \u003cp\u003e10.3.2 Proportional odds models 109\u003c\/p\u003e \u003cp\u003e10.3.3 Adjacent-category logit models 114\u003c\/p\u003e \u003cp\u003e10.3.4 Continuation-ratio logit models 115\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Log-linear models for \u003ci\u003eI\u003c\/i\u003e X \u003ci\u003eJ\u003c\/i\u003e tables 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 The saturated model 119\u003c\/p\u003e \u003cp\u003e11.1.1 Cornered constraints 120\u003c\/p\u003e \u003cp\u003e11.1.2 Centered constraints 122\u003c\/p\u003e \u003cp\u003e11.2 The independence model for an \u003ci\u003eI\u003c\/i\u003e \u003ci\u003eX J\u003c\/i\u003e table 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Log-linear models for \u003ci\u003eI\u003c\/i\u003e X \u003ci\u003eJ\u003c\/i\u003e X \u003ci\u003eK\u003c\/i\u003e tables 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Mutual independence: A=B=C 131\u003c\/p\u003e \u003cp\u003e12.2 The model AB=C 131\u003c\/p\u003e \u003cp\u003e12.3 Conditional independence and independence 133\u003c\/p\u003e \u003cp\u003e12.4 The model AB=AC 134\u003c\/p\u003e \u003cp\u003e12.5 The models AB=AC=BC and ABC 135\u003c\/p\u003e \u003cp\u003e12.6 Simpson’s paradox 135\u003c\/p\u003e \u003cp\u003e12.7 Connection between log-linear models and logistic regression 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Implications and uses of Birch’s result 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Birch’s result 141\u003c\/p\u003e \u003cp\u003e13.2 Iterative scaling 142\u003c\/p\u003e \u003cp\u003e13.3 The hierarchy constraint 143\u003c\/p\u003e \u003cp\u003e13.4 Inclusion of the all-factor interaction 144\u003c\/p\u003e \u003cp\u003e13.5 Mostellerising 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Model selection for log-linear models 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Three variables 150\u003c\/p\u003e \u003cp\u003e14.2 More than three variables 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Incomplete tables, dummy variables, and outliers 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Incomplete tables 157\u003c\/p\u003e \u003cp\u003e15.1.1 Degrees of freedom 158\u003c\/p\u003e \u003cp\u003e15.2 Quasi-independence 159\u003c\/p\u003e \u003cp\u003e15.3 Dummy variables 159\u003c\/p\u003e \u003cp\u003e15.4 Detection of outliers 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Panel data and repeated measures 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 The mover-stayer model 166\u003c\/p\u003e \u003cp\u003e16.2 The loyalty model 168\u003c\/p\u003e \u003cp\u003e16.3 Symmetry 169\u003c\/p\u003e \u003cp\u003e16.4 Quasi-symmetry 170\u003c\/p\u003e \u003cp\u003e16.5 The loyalty-distance model 172\u003c\/p\u003e \u003cp\u003eA R code for Cobweb function 175\u003c\/p\u003e \u003cp\u003eIndex 179\u003c\/p\u003e \u003cp\u003eAuthor Index 183\u003c\/p\u003e \u003cp\u003eIndex of Examples 185\u003c\/p\u003e \u003cp\u003e\"Concise introduction to dealing with categorical data (with supporting R code) which will help the general data scientist.\" (\u003cb\u003eRaspberry Pi\u003cb\u003e March 2017) \u003c\/b\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eGRAHAM J. G. UPTON\u003c\/b\u003e is formerly Professor of Applied Statistics, Department of Mathematical Sciences, University of Essex. Dr. Upton is author of \u003ci\u003eThe Analysis\u003c\/i\u003e \u003ci\u003eof Cross-tabulated Data \u003c\/i\u003e(1978) and joint author of \u003ci\u003eSpatial Data Analysis by Example\u003c\/i\u003e (2 volumes, 1995), both published by Wiley. He is the lead author of \u003ci\u003eThe Oxford\u003c\/i\u003e \u003ci\u003eDictionary of Statistics \u003c\/i\u003e(OUP, 2014). His books have been translated into Japanese, Russian, and Welsh.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying R code\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicated mathematics and without needing to wade through a large amount of prose. It is aimed at researchers with their own data ready to be analyzed and at students who would like an approachable alternative view of the subject.\u003c\/p\u003e \u003cp\u003eEach new topic in categorical data analysis is illustrated with an example that readers can apply to their own sets of data. In many cases, R code is given and excerpts from the resulting output are presented. In the context of log-linear models for cross-tabulations, two specialties of the house have been included: the use of cobweb diagrams to get visual information concerning significant interactions, and a procedure for detecting outlier category combinations. The R code used for these is available and may be freely adapted. In addition, this book:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eUses an example to illustrate each new topic in categorical data\u003c\/li\u003e \u003cli\u003eProvides a clear explanation of an important subject\u003c\/li\u003e \u003cli\u003eIs understandable to most readers with minimal statistical and mathematical backgrounds\u003c\/li\u003e \u003cli\u003eContains examples that are accompanied by R code and resulting output\u003c\/li\u003e \u003cli\u003eIncludes starred sections that provide more background details for interested readers\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eCategorical Data Analysis by Example \u003c\/i\u003eis a reference for students in statistics and researchers in other disciplines, especially the social sciences, who use categorical data. This book is also a reference for practitioners in market research, medicine, and other fields.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988892991717,"sku":"NP9781119307860","price":91.5,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119307860.jpg?v=1761781948","url":"https:\/\/k12savings.com\/es\/products\/categorical-data-analysis-by-example-isbn-9781119307860","provider":"K12savings","version":"1.0","type":"link"}