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Titlebook: Analyzing Medical Data Using S-PLUS; Brian Everitt,Sophia Rabe-Hesketh Book 2001 Springer Science+Business Media New York 2001 Analysis of

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期刊全称Analyzing Medical Data Using S-PLUS
影响因子2023Brian Everitt,Sophia Rabe-Hesketh
视频video
发行地址Written specifically for the medical field.Uses S-PLUS to apply a variety of statistical methods.Contains a mix of real data examples and background theory
学科分类Statistics for Biology and Health
图书封面Titlebook: Analyzing Medical Data Using S-PLUS;  Brian Everitt,Sophia Rabe-Hesketh Book 2001 Springer Science+Business Media New York 2001 Analysis of
影响因子Each chapter will consist of basic statistical theory, simple examples of S-PLUS code, more complex examples of S-PLUS code, and exercises. All data sets will be taken from genuine medical investigations and will be made available, if possible, on a web site. All examples will contain extensive graphical analysis to highlight one of the prime features of S-PLUS. The book would complement Venables and Ripley (VR). However, there is far less about the details of S-PLUS and probably less technical descriptions of techniques. The book concentrates solely on medical data sets trying to demonstrate the flexibility of S-PLUS and its huge advantages, particularly for applied medical statisticians.
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Theoretische Ansätze zur Dividendenpolitike data in some way. Which graphs and which summary statistics are most appropriate will largely depend on the type of observations and measurements that have been recorded. In this chapter, we shall illustrate the possibilities using a number of data sets containing continuous or categorical variables.
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Grundlagen der Dividendenpolitikpopulation—is central to statistics in general, and medical statistics in particular. In this chapter, we shall look at some basic inferential methods, beginning with those most suitable for continuous variables having, approximately at least, a normal distribution.
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https://doi.org/10.1057/9780230118737ss modeling survival using several explanatory variables simultaneously analogously to linear regression or generalized linear models. The most popular, and in many cases most useful, regression model for survival data in medicine is Cox’s regression model.
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Divine Free Action in Avicenna and AnselmAll of the models discussed so far assume that the dependent variable is continuous and normally distributed. In this chapter, we introduce ., which include the regression and ANOVA models of previous chapters, but can also be used for modeling non-normally distributed response variables, in particular categorical variables.
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Generalized Linear Models I: Logistic Regression,All of the models discussed so far assume that the dependent variable is continuous and normally distributed. In this chapter, we introduce ., which include the regression and ANOVA models of previous chapters, but can also be used for modeling non-normally distributed response variables, in particular categorical variables.
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