Distribution-free statistical methods
Distribution-free statistical methods enable users to make statistical inferences with minimum assumptions about the population in question. They are widely used, especially in the areas of medical and psychological research. This new edition is aimed at senior undergraduate and graduate level. It also includes a discussion of new techniques that have arisen as a result of improvements in statistical computing. Interest in estimation techniques has particularly grown, and this section of the book has been expanded accordingly. Finally, Distribution-Free Statistical Methods includes more examples with actual data sets appearing in the text.
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Onesample location problems
Miscellaneous onesample problems
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analysis of variance applied approximately normal bivariate bution calculations Chapter conditional distribution conditional null distribution conditionally confidence coefficient confidence interval confidence limits covariance matrix critical region data of Example defined denote density discussed distri distribution function distribution of Q distribution-free methods efficacy efficiency enumeration estimate of 9 estimating equation exact distribution exact inference exact joint formula given giving graph hence Hypothesis testing independent interquartile range invariant with respect joint confidence region joint distribution large-sample location parameter M-estimates mean statistic normal approximation normal distribution nuisance parameter null hypothesis observed value obtained one-sample pairwise slopes permutation point estimate population possible problem random variables rank statistics reject H0 replaced restricted randomization sample median scores Section sign statistic solution straight-line regression straightforward suitable conditions Suppose Symmetric distributions tabulated test procedure test statistic Testing H0 theorem transformations two-sample Wilcoxon Y-samples