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  • Edition: 4th
  • Format: Hardcover
  • Copyright: 2011-09-16
  • Publisher: W. H. Freeman

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Sokal and Rohlf's classic text deals with statistics from numerous areas of biological research, focusing on practical applications and incorporates computer calculations.

Table of Contents

Prefacep. xiii
Notes on the Fourth Editionp. xvii
Introductionp. 1
Some Definitionsp. 1
The Development of Biometryp. 3
The Statistical Frame of Mindp. 5
Data in Biologyp. 9
Samples and Populationsp. 9
Variables in Biologyp. 11
Accuracy and Precision of Datap. 13
Derived Variablesp. 16
Frequency Distributionsp. 19
Computers and Data Analysisp. 33
Computersp. 33
Softwarep. 35
Efficiency and Economy in Data Processingp. 37
Descriptive Statisticsp. 39
The Arithmetic Meanp. 40
Other Meansp. 44
The Medianp. 45
The Modep. 47
Sample Statistics and Parametersp. 49
The Rangep. 49
The Standard Deviationp. 51
Coding Data Before Computationp. 54
The Coefficient of Variationp. 55
Introduction to Probability Distributions: Binomial and Poissonp. 59
Probability, Random Sampling, and Hypothesis Testingp. 60
The Binomial Distributionp. 68
The Poisson Distributionp. 78
Other Discrete Probability Distributionsp. 87
The Normal Probability Distributionp. 93
Frequency Distributions of Continuous Variablesp. 93
Properties of the Normal Distributionp. 95
A Model for the Normal Distributionp. 100
Applications of the Normal Distributionp. 102
Fitting a Normal Distribution to Observed Datap. 104
Skewness and Kurtosisp. 106
Graphic Methodsp. 108
Other Continuous Distributionsp. 117
Hypothesis Testing and Interval Estimationp. 119
Introduction to Hypothesis Testing: Randomization Approachesp. 120
Distribution and Variance of Meansp. 131
Distribution and Variance of Other Statisticsp. 137
The t-Distributionp. 140
More on Hypothesis Testing: Normally Distributed Datap. 142
Power of a Testp. 146
Tests of Simple Hypotheses Using the Normal and f-Distributionsp. 148
The Chi-Square Distributionp. 154
Testing the Hypothesis H0: 2 = 20p. 156
Introduction to Interval Estimation (Confidence Limits)p. 157
Confidence Limits Using Sample Standard Deviationsp. 162
Confidence Limits for Variancesp. 167
The Jackknife and the Bootstrapp. 168
Introduction to Analysis of Variancep. 177
Variances of Samples and Their Meansp. 178
TheF-Distributionp. 182
The Hypothesis H0: 21 = 22p. 187
Heterogeneity Among Sample Meansp. 190
Partitioning the Total Sum of Squares and Degrees of Freedomp. 197
Model I Anovap. 200
Model II Anovap. 203
Single-Classification Analysis of Variancep. 207
Computational Formulasp. 208
General Case: Unequal and Equal np. 208
Special Case: Two Groupsp. 220
Comparisons Among Means in a Model I Anova: Essential Backgroundp. 228
Comparisons Among Means: Special Methodsp. 246
Nested Analysis of Variancep. 277
Nested Anova: Designp. 277
Nested Anova: Computationp. 280
Nested Anovas with Unequal Sample Sizesp. 301
Two-Way and Multiway Analysis of Variancep. 319
Two-Way Anova: Designp. 319
Two-Way Anova with Equal Replication: Computationp. 321
Two-Way Anova: Hypothesis Testingp. 331
Two-Way Anova Without Replicationp. 340
Paired Comparisonsp. 349
The Factorial Designp. 354
A Three-Way Factorial Designp. 355
Higher-Order Factorial Anovasp. 365
Other Designsp. 370
Anova by Computerp. 372
Statistical Power and Sample Size in the Analysis of Variancep. 379
Effect Sizep. 379
Noncentral t- and F-Distributions and Confidence Limits for Effect Sizesp. 382
Power in an Anovap. 390
Sample Size in an Anovap. 391
Minimum Detectable Differencep. 395
Post Hoc Power Analysisp. 396
Optimal Allocation of Resources in a Nested Designp. 397
Randomized Blocks and Other Two-Way and Multiway Designsp. 406
Assumptions of Analysis of Variancep. 409
A Fundamental Assumptionp. 410
Independencep. 410
Homogeneity of Variancesp. 413
Normalityp. 422
Transformationsp. 426
The Logarithmic Transformationp. 427
The Square Root Transformationp. 433
The Box-Cox Transformationp. 435
The Arcsine Transformationp. 438
Nonparametric Methods in Lieu of Single-Classification Anovap. 440
Nonparametric Methods in Lieu of Two-Way Anovap. 460
Linear Regressionp. 471
Introduction to Regressionp. 472
Models in Regressionp. 475
The Linear Regression Equationp. 477
Hypothesis Testing in Regressionp. 485
More Than One Value of Y for Each Value of Xp. 495
The Uses of Regressionp. 506
Estimating X From Yp. 511
Comparing Two Regression Linesp. 513
Linear Comparisons in Anovasp. 515
Examining Residuals and Transformations in Regressionp. 524
Nonparametric Tests for Regressionp. 532
Model II Regressionp. 535
Effect Size, Power, and Sample Size in Regressionp. 544
Correlationp. 551
Correlation Versus Regressionp. 551
The Product-Moment Correlation Coefficientp. 554
Computing the Product-Moment Correlation Coefficientp. 562
The Variance of Sums and Differencesp. 565
Hypothesis Tests for Correlationsp. 567
Applications of Correlationp. 577
Nonparametric Tests for Associationp. 580
Major Axes and Confidence Regionsp. 588
Effect Size, Power, and Sample Sizep. 592
Multiple and Curvilinear Regressionp. 603
Multiple Regression: Computationp. 604
Multiple Regression: Hypothesis Testsp. 614
Path Analysis and Structural Equation Modelingp. 625
Partial and Multiple Correlationp. 644
Selection of Independent Variablesp. 649
Computation of Multiple Regression by Matrix Methodsp. 656
Solving Anovas as Regression Problems: General Linear Modelsp. 659
Analysis of Covariance (Ancova)p. 665
Curvilinear Regressionp. 671
Effect Size, Power, and Sample Size in Multiple Regressionp. 685
Advanced Topics in Regression and Correlationp. 694
Analysis of Frequenciesp. 703
Introduction to Tests for Goodness of Fitp. 704
Single-Classification Tests for Goodness of Fitp. 714
Replicated Tests of Goodness of Fitp. 730
Tests of Independence: Two-Way Tablesp. 739
Analysis of Three-Way Tablesp. 758
Analysis of Proportionsp. 773
Randomized Blocks for Frequency Datap. 793
Effect Sizes, Power, and Sample Sizesp. 801
Meta-Analysis and Miscellaneous Methodsp. 817
Synthesis of Prior Research Results: Meta-Analysisp. 817
Tests for Randomness of Nominal Data: Runs Testsp. 841
Isotonic Regressionp. 847
Application of Randomization Tests to Unconventional Statisticsp. 850
The Mantel Test of Association Between Two Distance Matricesp. 852
The Future of Biometry: Data Analysisp. 859
Mathematical Proofsp. 869
Introduction to Matricesp. 885
Bibliographyp. 891
Author Indexp. 909
Subject Indexp. 915
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