Distribution-Free Statistical Methods, Second EditionDistribution-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. |
Contents
Basic concepts in distributionfree methods 1125578 | xii |
Onesample location problems | 21 |
CONTENTS ix | 63 |
Miscellaneous onesample problems | 76 |
Twosample problems | 79 |
Straight line regression | 119 |
Multiple regression and general linear models | 165 |
Bivariate problems | 205 |
Miscellaneous complements | 231 |
References | 249 |
Other editions - View all
Common terms and phrases
A₁ A1 and A2 applied approximately normal argument asymptotically b₁ calculations Chapter conditional distribution conditional null distribution confidence interval confidence limits contingency table covariance matrix data of Example defined density estimation derived distribution function distribution of A1 estimating equation exact confidence exact joint fn(x following table formula given gives H₁ Hence Hypothesis testing independent inference interquartile range joint confidence region joint distribution large sample least squares linear M-estimates mean mean statistic MINITAB normal approximation normal distribution Note null distribution null hypothesis observed value obtained parameter permutation point estimate population possible problem R₁ random variables Rank(X replaced residuals S₁ S1 and S2 sample median Section sgn(x sgn(Y sign statistic solution ẞ₁ ẞx standard deviation standard error standard normal straight line regression straightforward Suppose tabulated test statistic two-sample two-sided variance W₁ Wilcoxon x₁ Y-sample