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Titlebook: Introduction to Statistical Modelling; Annette J. Dobson Book 1983 Annette J. Dobson 1983 Calc.Generalized linear model.Logistic Regressio

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書目名稱Introduction to Statistical Modelling
編輯Annette J. Dobson
視頻videohttp://file.papertrans.cn/475/474209/474209.mp4
圖書封面Titlebook: Introduction to Statistical Modelling; Annette J. Dobson Book 1983 Annette J. Dobson 1983 Calc.Generalized linear model.Logistic Regressio
描述This book is about generalized linear models as described by NeIder and Wedderburn (1972). This approach provides a unified theoretical and computational framework for the most commonly used statistical methods: regression, analysis of variance and covariance, logistic regression, log-linear models for contingency tables and several more specialized techniques. More advanced expositions of the subject are given by McCullagh and NeIder (1983) and Andersen (1980). The emphasis is on the use of statistical models to investigate substantive questions rather than to produce mathematical descriptions of the data. Therefore parameter estimation and hypothesis testing are stressed. I have assumed that the reader is familiar with the most commonly used statistical concepts and methods and has some basic knowledge of calculus and matrix algebra. Short numerical examples are used to illustrate the main points. In writing this book I have been helped greatly by the comments and criticism of my students and colleagues, especially Anne Young. However, the choice of material, and the obscurities and errors are my responsibility and I apologize to the reader for any irritation caused by them. For
出版日期Book 1983
關鍵詞Calc; Generalized linear model; Logistic Regression; Mathematica; Matrix; Statistica; analysis of variance
版次1
doihttps://doi.org/10.1007/978-1-4899-3174-0
isbn_softcover978-0-412-24860-3
isbn_ebook978-1-4899-3174-0
copyrightAnnette J. Dobson 1983
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Exponential Family of Distributions and Generalized Linear Models,ntinuous data. For instance the examples in the previous chapter, the comparison of two means (plant growth example) and the relationship between a continuous response variable and a covariate (birthweight example), are both of this form. So, too, are generalizations of these examples to comparisons
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Estimation,res. This chapter begins by reviewing the principle of each of these methods and some properties of the estimators. Then the method of maximum likelihood is used for generalized linear models. Usually the estimates have to be obtained numerically by an iterative procedure which turns out to be close
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Contingency Tables and Log-Linear Models,ossibly ordinal scales. Each scale may have more than two categories. Unlike the methods described in previous chapters, generalized linear models for categorical data can readily be defined when there is more than one response variable. The observations consist of. in the cells of a . formed by the
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Annette J. Dobsonsts, immunologists, and infectious disease specialists.Inclu.Volume Allergic Diseases is a comprehensive, multi-authored reference work, offering a broad appeal to microbiologists, immunologists, and infectious disease specialists. The volume explores all aspects of allergic diseases, body systems a
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Annette J. Dobsonsts, immunologists, and infectious disease specialists.Inclu.Volume Allergic Diseases is a comprehensive, multi-authored reference work, offering a broad appeal to microbiologists, immunologists, and infectious disease specialists. The volume explores all aspects of allergic diseases, body systems a
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