## 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. |

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### 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 |

### Common terms and phrases

alternative analysis applied approximately argument assume calculations Chapter choice conditional conditional distribution confidence interval confidence limits confidence region consistent covariance matrix defined density depend derived determined difference discussed distribution function distribution-free effect efficiency equal estimating equation exact Example expectation expression fixed formula given gives H₁ Hence Hypothesis testing identical illustrated independent inference known least squares linear listed mean measure method normal normal approximation Note null distribution observed obtained parameter permutation point estimate population possible practice probability problem procedure random random variables rank Refer regression relative replaced residuals respect sample median shown shows significance similar simple solution specifies standard standard error statistic suitable Suppose symmetric tabulated test statistic transformations treatment usual values variance write