## Statistical Methods for the Analysis of Biomedical DataThe new edition adds a chapter on multiple linear regression in biomedical research, with sections including the multiple linear regressions model and least squares; the ANOVA table, parameter estimates, and confidence intervals; partial f-tests; polynomial regression; and analysis of covariance. * Organized by problem rather than method, so it guides readers to the correct technique for solving the problem at hand. |

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### Contents

1 | |

9 | |

3 Basic Probability Concepts | 49 |

4 Further Aspects of Probability | 79 |

5 Confidence Intervals and Hypothesis Testing General Considerations and Applications | 119 |

6 Comparison of Two Groups tTests and Rank Tests | 151 |

7 Comparison of Two Groups ChiSquare and Related Procedures | 217 |

8 Tests of Independence and Measures of Association for Two Random Variables | 263 |

10 Comparing More than Two Groups of Observations Analysis of Variance for Comparing Groups | 359 |

11 Comparing More than Two Groups of Observations Rank Analysis of Variance for Group Comparisons | 417 |

12 Comparing More than Two Groups of Observations ChiSquare and Related Procedures | 441 |

13 Special Topics in Analysis of Epidemiologic and Clinical Data Studying Association between a Disease and a Characteristic | 461 |

14 Estimation and Comparison of Survival Curves | 509 |

15 Multiple Linear Regression Methods Predicting One Variable from Two or More Other Variables | 541 |

Appendix | 623 |

673 | |

### Other editions - View all

Statistical Methods for the Analysis of Biomedical Data Robert F. Woolson,William R. Clarke No preview available - 2002 |

### Common terms and phrases

a-level a-level test Alternative hypothesis analysis of variance ANOVA Appendix assumptions binomial Chapter chi-square coefﬁcient comparisons Completely Random Design compute conﬁdence interval Decision rule deﬁned Deﬁnition degrees of freedom denoted determine disease Dose drug equal Example ﬁgures ﬁnd ﬁrst ﬁve frequency given H0 in favor Hence independent variables large-sample LDL cholesterol level of signiﬁcance logistic regression measure methods multiple linear regression normal distribution null hypothesis observations odds ratio outcome p-value pairs pairwise partial F patients Percent Ideal Weight placebo population Pr[Z predicted probability distribution problem random variable rank regression analysis Reject H0 residuals sample mean sample variance schizophrenia selected serum creatinine signiﬁcance level signiﬁcant simple linear regression speciﬁc standard deviation standard error statistically signiﬁcant sum of squares systolic blood pressure Table A.4 test H0 test procedure test statistic test the null Total treatment groups Tukey’s two-sample t-test zero