Biometry 3rd Edition by Robert R. Sokal & F. James Rohlf (PDF)

Biometry: The Principles and Practice of Statistics in Biological Research 3rd Edition

Author: Robert R. Sokal &apm; F. James Rohlf
Release at: 1995
Pages: 899
Edition: 3rd Edition (The Principles and Practice of Statistics in Biological Research)
File Size: 12 MB
File Type: PDF
Language: English



Description of Biometry: The Principles and Practice of Statistics in Biological Research 3rd Edition (PDF)

Biometry: The Principles and Practice of Statistics in Biological Research 3rd Edition by Robert R. Sokal & F. James Rohlf is a great book available in (eBook) PDF download. The success of the first two editions of Biometry among teachers, students, and others who use biological statistics has encouraged us to prepare this extensively revised 3rd Edition. We wrote and have revised this book because we feel that there is a need for an up-to-date text aimed primarily at the academic biologist—a text that develops the subject from an elementary introduction up to the advanced methods necessary nowadays for biological research and for an understanding of the published literature. Many available texts represent the outlook and interests of agricultural experiment stations. This is quite proper in view of the great application of statistics in this field; in fact, modem statistics originated at such institutions.

However, personal inclination and the nature of the institution at which we teach cause us to address ourselves to general biologists, ecologists, geneticists, physiologists, and other biologists working largely on nonapplied subjects in universities, research institutes, and museums. Considerable overlap exists between the needs of these two somewhat artificially contrasted groups and, of necessity, some of our presentation will deal with agricultural experimentation. More broadly, the statistical methods treated here are useful in many applied fields, including medicine and allied health sciences. Since it is a well-known pedagogical dictum that people learn best by familiar examples, we have endeavored to make our examples as pertinent as possible for our readers.

Much agricultural and biological work is by its very nature experimental. This book, while furnishing ample directions for the analysis of experimental work, also stresses descriptive and analytical statistical study of biological phenomena. These powerful methods are often overlooked and sometimes, by implication, the validity of nonexperimental biological work is put in doubt. We think that descriptive, analytical, and experimental approaches are all of value, and we have tried to strike a balance among them. This approach will be appreciated by workers in applied fields such as the health sciences where ethical, financial, or other considerations may prevent free use of direct experiment.




Content of Biometry: The Principles and Practice of Statistics in Biological Research 3rd Edition (PDF)


Chapter 1: INTRODUCTION

  • Some Definitions
  • The Development of Biometry
  • The Statistical Frame of Mind
  • Chapter 2: DATA IN BIOLOGY

  • Samples and Populations
  • Variables in Biology
  • Accuracy and Precision of Data
  • Derived Variables
  • Frequency Distributions
  • Chapter 3: THE HANDLING OF DATA

  • Computers
  • Software
  • Efficiency and Economy in Data Processing
  • Chapter 4: DESCRIPTIVE STATISTICS

  • The Arithmetic Mean
  • Other Means
  • The Median
  • The Mode
  • The Range
  • The Standard Deviation
  • Sample Statistics and Parameters
  • Coding Data Before Computation
  • Computing Means and Standard Deviations
  • The Coefficient of Variation
  • Chapter 5: INTRODUCTION TO PROBABILITY DISTRIBUTIONS: BINOMIAL AND POISSON

  • Probability, Random Sampling, and Hypothesis Testing
  • The Binomial Distribution
  • The Poisson Distribution
  • Other Discrete Probability Distributions
  • Chapter 6: THE NORMAL PROBABILITY DISTRIBUTION

  • Frequency Distributions of Continuous Variables
  • Properties of the Normal Distribution
  • A Model for the Normal Distribution
  • Applications of the Normal Distribution
  • Fitting a Normal Distribution to Observed Data
  • Skewness and Kurtosis
  • Graphic Methods
  • Other Continuous Distributions
  • Chapter 7: ESTIMATION AND HYPOTHESIS TESTING

  • Distribution and Variance of Means
  • Distribution and Variance of Other Statistics
  • Introduction to Confidence Limits
  • The /-Distribution
  • Confidence Limits Based on Sample Statistics
  • The Chi-Square Distribution
  • Confidence Limits for Variances
  • Introduction to Hypothesis Testing
  • Tests of Simple Hypotheses Using the Normal and Distributions
  • Testing the Hypothesis H0:

    Chapter 8: INTRODUCTION TO THE ANALYSIS OF VARIANCE

  • Variances of Samples and Their Means
  • The F-Distribution
  • The Hypothesis H0:
  • Heterogeneity Among Sample Means Partitioning the Total Sum of Squares and Degrees of Freedom
  • Model 1 Anova
  • Model 11 Anova
  • Chapter 9: SINGLE-CLASSIFICATION ANALYSIS OF VARIANCE

  • Computational Formulas
  • General Case: Unequal n
  • Special Case: Equal n
  • Special Case: Two Groups
  • Special Case: A Single Specimen Compared With a Sample
  • Comparisons Among Means: Planned Comparisons
  • Comparisons Among Means: Unplanned Comparisons
  • Finding the Sample Size Required for a Test
  • Chapter 10: NESTED ANALYSIS OF VARIANCE

  • Nested Anova: Design
  • Nested Anova: Computation
  • Nested Anovas With Unequal Sample Sizes
  • The Optimal Allocation of Resources
  • Chapter 11: TWO-WAY ANALYSIS OF VARIANCE

  • Two-Way Anova: Design
  • Two-Way Anova With Equal Replication: Computation
  • Two-Way Anova: Significance Testing
  • Two-Way Anova Without Replication
  • Paired Comparisons
  • Unequal Subclass Sizes
  • Missing Values in a Randomized-Blocks Design
  • Chapter 12: MULTIWAY ANALYSIS OF VARIANCE

  • The Factorial Design
  • A Three-Way Factorial Anova
  • Higher-Order Factorial Anovas
  • Other Designs
  • Anovas by Computer
  • Chapter 13: ASSUMPTIONS OF ANALYSIS OF VARIANCE

  • A Fundamental Assumption
  • Independence
  • Homogeneity of Variance
  • Normality
  • Additivity
  • Transformations
  • The Logarithmic Transformation
  • The Square-Root Transformation
  • The Box-Cox Transformation
  • The Arcsine Transformation
  • Nonparametric Methods in Lieu of Single-Classification Anovas
  • Nonparametric Methods in Lieu of Two-Way Anova
  • Chapter 14: LINEAR REGRESSION

  • Introduction to Regression
  • Models in Regression
  • The Linear Regression Equation
  • Tests of Significance in Regression
  • More Than One Value of Y for Each Value of X
  • The Uses of Regression
  • Estimating X from Y
  • Comparing Regression Lines
  • Analysis of Covariance
  • Linear Comparisons in Anovas
  • Examining Residuals and Transformations in Regression
  • Nonparametric Tests for Regression
  • Model II Regression
  • Chapter 15: CORRELATION

  • Correlation and Regression
  • The Product-Moment Correlation Coefficient
  • The Variance of Sums and Differences
  • Computing the Product-Moment Correlation Coefficient
  • Significance Tests in Correlation
  • Applications of Correlation
  • Principal Axes and Confidence Regions
  • Nonparametric Tests for Association
  • Chapter 16: MULTIPLE AND CURVILINEAR REGRESSION

  • Multiple Regression: Computation
  • Multiple Regression: Significance Tests
  • Path Analysis
  • Partial and Multiple Correlation
  • Choosing Predictor Variables
  • Curvilinear Regression
  • Advanced Topics in Regression and Correlation
  • Chapter 17: ANALYSIS OF FREQUENCIES

  • Introduction to Tests for Goodness of Fit
  • Single-Classification Tests for Goodness of Fit
  • Replicated Tests of Goodness of Fit
  • Tests of Independence: Two-Way Tables
  • Analysis of Three-Way and Multiway Tables
  • Analysis of Proportions
  • Randomized Blocks for Frequency Data
  • Chapter 18: MISCELLANEOUS METHODS

  • Combining Probabilities From Tests of Significance
  • Tests for Randomness of Nominal Data: Runs Tests
  • Randomization Tests
  • The Jackknife and the Bootstrap
  • The Future of Biometry: Data Analysis
  • APPENDIX: MATHEMATICAL PROOFS

    BIBLIOGRAPHY

    INDEX

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