Nonparametric Statistical MethodsThis Second Edition of Myles Hollander and Douglas A. Wolfe's successful Nonparametric Statistical Methods meets the needs of a new generation of users, with completely up-to-date coverage of this important statistical area. Like its predecessor, the revised edition, along with its companion ftp site, aims to equip readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for a given situation. An extensive array of examples drawn from actual experiments illustrates clearly how to use nonparametric approaches to handle one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. An ideal text for an upper-level undergraduate or first-year graduate course, Nonparametric Statistical Methods, Second Edition is also an invaluable source for professionals who want to keep abreast of the latest developments within this dynamic branch of modern statistics. |
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Page 27
... variables being continuous , but owing to limitations in the precision of the timing device , the outcomes that are recorded are outcomes of a discrete random variable . [ 141 ] Let T1 . Contrast : eters is a parameter to the condition ...
... variables being continuous , but owing to limitations in the precision of the timing device , the outcomes that are recorded are outcomes of a discrete random variable . [ 141 ] Let T1 . Contrast : eters is a parameter to the condition ...
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... random variable ) : Let X be a discrete random variable that can assume the values x1 , x2 , ... with corresponding probabilities P1 = P ( X = x1 } , P2 = P { X = x2 } , .... Then the expectation of X is equal to Exp . We use the ...
... random variable ) : Let X be a discrete random variable that can assume the values x1 , x2 , ... with corresponding probabilities P1 = P ( X = x1 } , P2 = P { X = x2 } , .... Then the expectation of X is equal to Exp . We use the ...
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... random variable X is represented by a curve on a graph . The curve never lies below the horizontal axis , and the total area between the curve and the horizontal axis is one . The probability of the event { a ≤ X ≤ b } is represented ...
... random variable X is represented by a curve on a graph . The curve never lies below the horizontal axis , and the total area between the curve and the horizontal axis is one . The probability of the event { a ≤ X ≤ b } is represented ...
Contents
CHAPTER | 1 |
THE TWOWAY LAYOUT | 4 |
THE DICHOTOMOUS DATa Problem | 20 |
Copyright | |
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accept alternatives applied associated Assumptions asymptotic asymptotically distribution-free average calculate Chapter coefficient Comment Comments compute confidence interval configurations consistent constant continuous corresponding data of Table defined denote depend described designed determination deviation differences discussed distribution distribution-free distribution-free test effect efficiency equal EQUIVALENT error estimator Example given Hodges hypothesis illustrate independent jackknife large sample approximation Lehmann mean median method Moses n₁ n₂ nonparametric normal Note null distribution observations obtained ordered pairs parameter particular perform point estimator population positive possible probability problem procedures Properties proposed R₁ R₂ random variable rank sum REFERENCES reject respect satisfies scale Section signed rank significance specified statistic subgroup subjects symmetric Table tends test procedures theory tion treatment true two-sample two-sided underlying population USAGE values variance versus Wilcoxon X₁ Y₁