Multivariate Statistics for Wildlife and Ecology Research

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With its focus on the practical application of the techniques of multivariate statistics, this book shapes the powerful tools of statistics for the specific needs of ecologists and makes statistics more applicable to their course of study. It gives readers a solid conceptual understanding of the role of multivariate statistics in ecological applications and the relationships among various techniques, while avoiding detailed mathematics and the underlying theory. More importantly, the reader will gain insight into the type of research questions best handled by each technique and the important considerations in applying them. Whether used as a textbook for specialised courses or as a supplement to general statistics texts, the book emphasises those techniques that students of ecology and natural resources most need to understand and employ in their research. While targeted for upper-division and graduate students in wildlife biology, forestry, and ecology, and for professional wildlife scientists and natural resource managers, this book will also be valuable to researchers in any of the biological sciences..


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Applied Multivariate Statistics with R (Statistics for Biology and Health)

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Applied Multivariate Statistics with R (Statistics for Biology and Health) Info

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This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the Behavior Risk Factor Surveillance System, discussing both the shortcomings of the data as well as useful analyses. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary.

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Elsevier's Integrated Pharmacology: With STUDENT CONSULT Online Access, 1e

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Elsevier's Integrated Pharmacology: With STUDENT CONSULT Online Access, 1e La revue

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Each title in the new Integrated series focuses on the core knowledge in a specific basic science discipline, while linking that information to related concepts from other disciplines. Case-based questions at the end of each chapter enable you to gauge your mastery of the material, and a color-coded format allows you to quickly find the specific guidance you need. Bonus STUDENT CONSULT access - included with the text - allows you to conveniently access the book's content online · clip content to your handheld device · link to content in other STUDENT CONSULT titles · and more! These concise and user-friendly references provide crucial guidance for the early years of medical training, as well as for exam preparation.

• Includes case-based questions at the end of each chapter
• Features a colour-coded format to facilitate quick reference and promote effective retention
• Offers access to STUDENT CONSULT! At www.studentconsult.com, you'll find the complete text and illustrations of the book online, fully searchable · "Integration Links" to bonus content in other STUDENT CONSULT titles · content clipping for handheld devices · an interactive community center with a wealth of additional resources · and much more!

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Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition

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Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition Info

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Thoroughly revised and updated, the third edition of Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking retains and refines the core perspectives of the previous editions: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes.

With its engaging and conversational tone, this unique book provides a clear introduction to statistics for undergraduate and graduate students in a wide range of fields and also serves as a statistics refresher for working scientists. It is especially useful for those students in health-science related fields who have no background in biostatistics.

CONTENTS

Part A: Introducing Statistics 

 1. Statistics and Probability Are Not Intuitive

 2. The Complexities of Probability

 3. From Sample to Population 

Part B: Confidence Intervals 

 4. Confidence Interval of a Proportion 

 5. Confidence Interval of Survival Data 

 6. Confidence Interval of Counted Data 

Part C: Continuous Variables 

 7. Graphing Continuous Data

 8. Types of Variables 

 9. Quantifying Scatter 

10. The Gaussian Distribution 

11. The Lognormal Distribution and Geometric Mean

12. Confidence Interval of a Mean 

13. The Theory of Confidence Intervals

14. Error Bars 

PART D: P Values and Significance 

15. Introducing P Values 

16. Statistical Significance and Hypothesis Testing

17. Relationship Between Confidence Intervals and Statistical Significance 

18. Interpreting a Result That Is Statistically Significant 

19. Interpreting a Result That Is Not Statistically Significant 

20. Statistical Power

21. Testing for Equivalence or Noninferiority

PART E: Challenges in Statistics 

22. Multiple Comparisons Concepts 

23. The Ubiquity of Multiple Comparison

24. Normality Tests

25. Outliers 

26. Choosing a Sample Size

PART F: Statistical Tests 

27. Comparing Proportions

28. Case-Control Studies

29. Comparing Survival Curves 

30. Comparing Two Means: Unpaired t Test

31. Comparing Two Paired Groups

32. Correlation 

PART G: Fitting Models to Data 

33. Simple Linear Regression

34. Introducing Models 

35. Comparing Models 

36. Nonlinear Regression

37. Multiple Regression 

38. Logistic and Proportional Hazards Regression

PART H The Rest of Statistics 

39. Analysis of Variance 

40. Multiple Comparison Tests After ANOVA 

41. Nonparametric Methods

42. Sensitivity and Specificity and Receiver-Operator Characteristic Curves 

43. Meta-analysis

PART I Putting It All Together 

44. The Key Concepts of Statistics

45. Statistical Traps to Avoid

46. Capstone Example 

47. Review Problems 

48. Answers to Review Problems 

 

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