## Biometryobert R. Sokal, Robert R. Sokal, F. James Rohlf, James F. Rohlf, University Robert R Sokal, University F James Rohlf Some Definitions. The development of biometry. The statistical frame of mind. Data in biology. Samples and populations. Variables in biology. Accuracy and precision of data. Derived variables. Frequency distribuitions. The handling of data. Computers. Software. Efficiency and economy in data processing. Descriptive statistics. The arithmetic mean. Other means. The median. The mode. The range. The standard deviation.Sample statistics and parametrs. Coding data before computation. Computing means and standard deviations. The coefficient of variation. Introduction to probability distribution: Binomial and poison. probability, random sampling, and hypothesis testing. the binomial distribuition. The poisson distribuition. Other discrete probability distributions. The normal probability distribuition. Estimation and hypothesis testing. introduction to the analysis of variance. Single-classification analysis of variance. Nested analysis of variance. Two-way analysis of variance. Multiway analysis of variance. Assumptions of analysis of variance. A fundamental assumption. Independence. Homogeneity of variances. Normality. Additivity. Transformations. The logarithmic, the square-root, the box-cox and the arcsine transformation. Nonparametric methods in lieu of single-classification anovas and two-way anova. Linear regression. Correlation. Multiple and curvilinear regression. Analysis of frequencies. Miscellaneous methods. Mathematical proofs. |

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User Review - amarcobio - LibraryThingAny serious biologist must keep a copy of Biometry in his shelf. This is the bible for frequentism-based statistics in biosciences. Read full review

### Contents

INTRODUCTION | 1 |

DATA IN BIOLOGY | 8 |

THE HANDLING OF DATA | 33 |

DESCRIPTIVE STATISTICS | 39 |

THE NORMAL PROBABILITY DISTRIBUTION | 98 |

Applications of the Normal Distribution | 109 |

ESTIMATION AND HYPOTHESIS TESTING | 127 |

INTRODUCTION TO THE ANALYSIS | 179 |

l AssumPTIONS OF ANALYSIs OF WARIANCE | 392 |

l LINEAR REGRESSION 451 | L-51 |

coRRELATION | 555 |

MULTIPLE AND CURVILINEAR REGRESSION | 609 |

ANALYSIS OF FREQUENCIES | 685 |

MISCELLANEOUS METHODS | 794 |

MATHEMATICAL PROOFS | 833 |

BIBLIOGRAPHY | 850 |

SINGLECLASSIFICATION ANALYSIS | 207 |

NESTED ANALYSIS OF WARIANCE | 272 |

TwoWAY ANALYSIS OF WARIANCE | 321 |

I2 MULTIWAY ANALYSIS OF WARIANCE | 369 |

AUTHOR INDEX | 865 |

871 | |

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### Common terms and phrases

alternative hypothesis analysis of variance anova table approximate average binomial birth weights calculate column comparisons computed confidence limits correlation coefficient covariance critical value curve degrees of freedom density equation estimate example expected frequencies experiment experimentwise error rate Expression factor females Figure formula frequency distribution function G-test genetic given groups independent variables individual interaction linear regression logarithms magnitude measure median method Model MSwithin multiple regression nested anova normal distribution null hypothesis observed frequencies obtain pairs partial regression coefficients path coefficients Poisson population predictor probability procedure proportion quantity random ranks rats regression line replicates represent sample mean sample statistic shows significance test single-classification slope Source of variation species standard deviation standard error Statistical Table student subgroups sum of squares tion transformation treatment effects two-way anova type I error variance component variation df SS yields zero