Statistics

• NEW - Updates content and citations throughout to reflect current literature.

• NEW - Offers new, expanded, or revised discussions of:

  • Multivariate analysis of variance and logistic regression.

  • Log-linear mode.

• Presents a broad collection of data-analysis procedures and techniques used across a wide variety of biological disciplines—e.g., physiology, genetics, ecology, behavior, morphology.

• Offers the most comprehensive treatment available, including coverage of unique topics such as diversity, polynomial regression, multidimensional contingency tables, stepwise regression, nonparametric multiple comparisons, higher order factorial analyses of variance, circular distributions, and power and sample size determinations.

• Contains an orderly organization and presentation of topics, with cross-referencing as appropriate.

• Adopts an approach that is both readable and accessible, assuming no previous statistics background or mathematical expertise beyond simple algebra.

• Encourages students to think about the value of each statistical technique, as opposed to merely plugging numbers into formulae.

• Considers factorial analysis of variance and multiple regression in terms of the interpretation of typical computer output.

• Integrates a wealth of graphs and other figures to visually support concepts under discussion.

• Features a uniquely comprehensive set of statistical tables—over 40 in all—that facilitates statistical analyses without having to consult a separate book.

  • Includes tables that are unique to this volume.

• Guides readers step-by-step through numerous worked examples, demonstrating each of the concepts and procedures under discussion.

  • Provides worked examples for all major procedures.

• Presents additional exercises, with answers, at the end of each chapter.

• Offers an extensive bibliography to direct readers toward further literature.

• Updates content and citations throughout to reflect current literature.

• Offers new, expanded, or revised discussions of:

  • Multivariate analysis of variance and logistic regression.

  • Log-linear mode.

 1. Introduction.


 2. Populations and Samples.


 3. Measures of Central Tendency.


 4. Measures of Dispersion and Variability.


 5. Probabilities.


 6. The Normal Distribution.


 7. One-Sample Hypotheses.


 8. Two-Sample Hypotheses.


 9. Paired-Sample Hypotheses.


10. Multisample Hypotheses: The Analysis of Variance.


11. Multiple Comparisons.


12. Two-Factor Analysis of Variance


13. Data Transformations.


14. Multiway Factorial Analysis of Variance.


15. Nested (Hierarchical) Analysis of Variance.


16. Multivariate Analysis of Variance.


17. Simple Linear Regression.


18. Comparing Simple Linear Regression Equations.


19. Simple Linear Correlation.


20. Multiple Regression and Correlation.


21. Polynomial Regression.


22. Testing for Goodness of Fit.


23. Contingency Tables.


24. More on Dichotomous Variables.


25. Testing for Randomness.


26. Circular Distributions: Descriptive Statistics.


27. Circular Distributions: Hypothesis Testing.


Appendix A: Analysis of Variance Hypothesis Testing.


Appendix B: Statistical Tables and Graphs.


Answers to Exercises.


Literature Cited.


Index.

Biostatistics  (Statistics)

• 
  Biostatistical Analysis, 5/E
  Zar
  ©2010 | Pearson | Cloth; 960 pp | Instock
  ISBN-10: 0131008463 | ISBN-13: 9780131008465
  Suggested retail price: $138.67  Buy from myPearsonStoreNet price: $104.00  (What's this?)Brief Description

  

  Zar’s Biostatistical Analysis, Fifth Edition, is the ideal textbook for graduate and undergraduate students seeking practical coverage of statistical analysis methods used by researchers to collect, summarize, analyze, and draw conclusions from biological research. The latest edition of this best-selling textbook is both comprehensive and easy to read. It is suitable as an introduction for beginning students and as a comprehensive procedural reference for today’s practitioners.

   

  This book is appropriate for a one- or two-semester, junior or graduate-level course in biostatistics, biometry, quantitative biology, or statistics, and assumes a prerequisite of algebra.

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