Titlebook: Exact Statistical Methods for Data Analysis; Samaradasa Weerahandi Book 1995 Springer Science+Business Media New York 1995 data analysis.a

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Preliminary Notions,aling with statistical problems where it is of interest to make statistical statements about certain parameters of a population from which a set of data have been generated. It is assumed that the characteristics of interest in the population can be represented by a set of random variables and the p

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Review of Special Distributions,ide formulae for computing exact p-values even for testing parameters of special distributions. The purpose of this chapter is to develop procedures of significance testing for the parameters of widely used distributions, such as the binomial distribution. The problems of point estimation and interv

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Generalized p-Values,an fixed-level testing. Most of all, p-values can be employed to test parameters of discrete distributions such as binomial and Poisson distributions, thus making exact nonparametric testing possible. Non-randomized fixed-level tests at standard nominal levels do not usually exist for discrete distr

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Generalized Confidence Intervals,ional confidence intervals will find the generalization useful to obtain excellent approximate interval estimates for problems such the interval estimation in mixed models. According to simulation studies, such approximations have outperformed more complicated approximations reported in the literatu

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Analysis of Variance,re than two normal populations. In practical applications, often we need to compare several population means. For example, an investigator might wish to compare several brands of a product, or diet plans, or methods of instruction, or advertising methods, and so on. When samples are taken from under

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Mixed Models,mpeting treatments) of each factor were considered as deliberate choices of an experimenter and the models we encountered in that chapter allowed the experimenter to compare the effects of competing treatments. The models that we studied in the previous chapter are called .. In some other applicatio

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Regression, of levels. This is indeed the case in many designed experiments. In other designed experiments, and in many other situations, we encounter response variables that depend linearly on one or more continuous variables. Both of these are particular cases of what is known as . The purpose of this chapte