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Contrôle de qualité --- quality controls --- Méthode statistique --- Statistical methods --- Normality test --- Distributional assumption
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Disease. --- Health. --- #A9202A --- Normalcy --- Normality --- Normalities --- Diseases --- Geneeskunde --- encyclopedieën --- encyclopedieën. --- Disease --- Health
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Nonparametric statistics provide a scientific methodology for cases where customary statistics are not applicable. Nonparametric statistics are used when the requirements for parametric analysis fail, such as when data are not normally distributed or the sample size is too small. The method provides an alternative for such cases and is often nearly as powerful as parametric statistics. Another advantage of nonparametric statistics is that it offers analytical methods that are not available otherwise. In social sciences, often, it is not possible to obtain measurements, which renders customary analysis impossible. For example, it is not possible to measure utility but is possible to rank preference, which is based on the unmeasurable utility. Nonparametric methods provide theoretically valid options for analysis, making the use of unscientific methods unnecessary. Nonparametric methods are intuitive and simple to comprehend, which helps researchers in the social sciences understand the methods in spite of lacking mathematical rigor needed in analytical methods customarily used in science. The only prerequisite for this book is high school level elementary algebra. This book is a methodology book and bypasses theoretical proofs while providing comprehensive explanations of the logic behind the methods and ample examples, which are all solved using direct computations as well as by using Stata. The book is arranged into two integrated volumes. Although each volume, and for that matter each chapter, can be used separately, it is advisable to read as much of both volumes as possible; because familiarity with what is applicable for different problems will enhance capabilities. It is recommended that everyone read the Introduction and Chapter 1 because determining whether data are random or normally distributed is essential in the selection of parametric versus nonparametric methods.
Nonparametric statistics. --- Nonparametric statistics --- median --- order statistics --- rank --- one sample --- two samples --- several samples --- multiple comparison --- normality --- skewness
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Nonparametric statistics provide a scientific methodology for cases where customary statistics are not applicable. Nonparametric statistics are used when the requirements for parametric analysis fail, such as when data are not normally distributed or the sample size is too small. The method provides an alternative for such cases and is often nearly as powerful as parametric statistics. Another advantage of nonparametric statistics is that it offers analytical methods that are not available otherwise. In social sciences, often, it is not possible to obtain measurements, which renders customary analysis impossible. For example, it is not possible to measure utility but is possible to rank preference, which is based on the unmeasurable utility. Nonparametric methods provide theoretically valid options for analysis, making the use of unscientific methods unnecessary. Nonparametric methods are intuitive and simple to comprehend, which helps researchers in the social sciences understand the methods in spite of lacking mathematical rigor needed in analytical methods customarily used in science. The only prerequisite for this book is high school level elementary algebra. This book is a methodology book and bypasses theoretical proofs while providing comprehensive explanations of the logic behind the methods and ample examples, which are all solved using direct computations as well as by using Stata. The book is arranged into two integrated volumes. Although each volume, and for that matter each chapter, can be used separately, it is advisable to read as much of both volumes as possible; because familiarity with what is applicable for different problems will enhance capabilities. It is recommended that everyone read the Introduction and Chapter 1 because determining whether data are random or normally distributed is essential in the selection of parametric versus nonparametric methods.
Nonparametric statistics. --- Nonparametric statistics --- median --- order statistics --- rank --- one sample --- two samples --- several samples --- multiple comparison --- normality --- skewness
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Biomedical Technology. --- Health. --- Engineering --- Biomedical Engineering --- Normalcy --- Normality --- Normalities --- Biomedical Technologies --- Technology, Biomedical --- Technology, Health --- Technology, Health Care --- Health Care Technology --- Health Technology --- Medical Informatics
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Health. --- Health --- #SBIB:316.334.3M11 --- Personal health --- Wellness --- Medicine --- Physiology --- Diseases --- Holistic medicine --- Hygiene --- Well-being --- Normalcy --- Normality --- Normalities --- Medische sociologie: concepten en theorieën
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Attitude to Health. --- Health. --- #SBIB:17H20 --- Normalcy --- Normality --- Normalities --- Health Attitude --- Attitude, Health --- Attitudes, Health --- Health Attitudes --- Health, Attitude to --- Public Opinion --- Sociale wijsbegeerte: algemeen --- Medische psychologie --- ATTITUDE TO HEALTH --- Health --- gezondheidspsychologie --- ATTITUDE TO HEALTH. --- gezondheidspsychologie. --- Gezondheidspsychologie. --- Attitude to Health
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Health --- Men --- Health and hygiene --- Health. --- Men. --- Health and hygiene. --- Human males --- Boys --- Normalcy --- Normality --- Normalities --- Human beings --- Males --- Effeminacy --- Masculinity --- Health Sciences --- Life Sciences --- Social Sciences --- Andrology --- Diagnostics --- Public health --- Gender Studies --- Hommes --- Santé et hygiène
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Nursing --- Soins infirmiers --- Nursing. --- Health. --- Nursing Care. --- Research --- Recherche --- Research. --- Clinical nursing --- Nurses and nursing --- Nursing process --- Care, Nursing --- Management, Nursing Care --- Nursing Care Management --- Disease --- Patient Care --- Nursings --- Normalcy --- Normality --- Normalities --- nursing --- Care of the sick --- Medicine
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This textbook is devoted to the general asymptotic theory of statistical experiments. Local asymptotics for statistical models in the sense of local asymptotic (mixed) normality or local asymptotic quadraticity make up the core of the book. Numerous examples deal with classical independent and identically distributed models and with stochastic processes. The book can be read in different ways, according to possibly different mathematical preferences of the reader. One reader may focus on the statistical theory, and thus on the chapters about Gaussian shift models, mixed normal and quadratic models, and on local asymptotics where the limit model is a Gaussian shift or a mixed normal or a quadratic experiment (LAN, LAMN, LAQ). Another reader may prefer an introduction to stochastic process models where given statistical results apply, and thus concentrate on subsections or chapters on likelihood ratio processes and some diffusion type models where LAN, LAMN or LAQ occurs. Finally, readers might put together both aspects. The book is suitable for graduate students starting to work in statistics of stochastic processes, as well as for researchers interested in a precise introduction to this area.
Mathematical statistics --- Asymptotic distribution (Probability theory) --- Asymptotic expansions --- Central limit theorem --- Distribution (Probability theory) --- Asymptotic theory. --- General Asymptotic Theory. --- LAMN. --- LAN. --- LAQ. --- Local Asymptotic Normality. --- Local Asymptotic Quadraticity. --- Local Asymptotic. --- Statistical Experiment. --- Statistical Model.
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