Inhaltsangabe:
Data are said to be binary when each observation falls into one of two categories, such as alive or dead, positive or negative, defective or non-defective and success or failure. In this book, it is shown how data of this type can be analyzed using a modelling approach. It is designed to meet the need of a textbook of an intermediate level which dwells on the practical aspects of modelling binary data, which incorporates recent work on checking the adequacy of fitted models and which shows how modern computational facilities can be exploited. The book begins with a description of a number of studies in which binary data have been collected. These data sets, and others, are then used to illustrate the techniques that are presented in the subsequent chapters. The majority of the examples are drawn from the agricultural, biological and medical sciences. Following this, a number of standard statistical procedures based on binomial distribution are described. The book then introduces the modelling approach, with particular emphasis on the linear logistic model, and covers the analysis of binary data from biological assays and other applications. Further topics discussed include model checking diagnostics, models for overdispersed data, analyzing data from cohort and case-control studies and statistical software packages. Suggestions for further reading are included in each chapter. The book is aimed at statisticians in the pharmaceutical industry and those engaged in agricultural, biological, industrial and medical research, who need to analyze binary data.
Reseña del editor:
Data are said to be binary when each observation falls into one of two categories, such as alive or dead, positive or negative, defective or non-defective and success or failure. In this book, it is shown how data of this type can be analyzed using a modelling approach. It is designed to meet the need of a textbook of an intermediate level which dwells on the practical aspects of modelling binary data, which incorporates recent work on checking the adequacy of fitted models and which shows how modern computational facilities can be exploited. The book begins with a description of a number of studies in which binary data have been collected. These data sets, and others, are then used to illustrate the techniques that are presented in the subsequent chapters. The majority of the examples are drawn from the agricultural, biological and medical sciences. Following this, a number of standard statistical procedures based on binomial distribution are described. The book then introduces the modelling approach, with particular emphasis on the linear logistic model, and covers the analysis of binary data from biological assays and other applications. Further topics discussed include model checking diagnostics, models for overdispersed data, analyzing data from cohort and case-control studies and statistical software packages. Suggestions for further reading are included in each chapter. The book is aimed at statisticians in the pharmaceutical industry and those engaged in agricultural, biological, industrial and medical research, who need to analyze binary data.
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