Applying and Interpreting Statistics

1. Front Matter

   Pages i-xxviii

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2. The Importance of Statistics in an Information-Based World

   • Glen McPherson

   Pages 1-34

3. Data: The Factual Information

   • Glen McPherson

   Pages 35-45

4. Statistical Models: The Experimenter’s View

   • Glen McPherson

   Pages 46-58

5. Comparing Model and Data

   • Glen McPherson

   Pages 59-71

6. Probability: A Fundamental Tool of Statistics

   • Glen McPherson

   Pages 72-95

7. Some Widely Used Statistical Models

   • Glen McPherson

   Pages 96-115

8. Some Important Statistics and Their Sampling Distributions

   • Glen McPherson

   Pages 116-144

9. Statistical Analysis: The Statistician’s View

   • Glen McPherson

   Pages 145-173

10. Examining Proportions and Success Rates

    • Glen McPherson

    Pages 174-190

11. Model and Data Checking

    • Glen McPherson

    Pages 191-217

12. Questions About the “Average” Value

    • Glen McPherson

    Pages 218-232

13. Comparing Two Groups, Treatments, or Processes

    • Glen McPherson

    Pages 233-281

14. Comparative Studies, Surveys, and Designed Experiments

    • Glen McPherson

    Pages 282-325

15. Comparing More Than Two Treatments or Groups

    • Glen McPherson

    Pages 326-358

16. Comparing Mean Response When There Are Three or More Treatments

    • Glen McPherson

    Pages 359-423

17. Comparing Patterns of Response: Frequency Tables

    • Glen McPherson

    Pages 424-448

18. Studying Relations Between Variables

    • Glen McPherson

    Pages 449-511

19. Prediction and Estimation: The Role of Explanatory Variables

    • Glen McPherson

    Pages 512-565

20. Questions About Variability

    • Glen McPherson

    Pages 566-585

In the period since the first edition was published, I have appreciated the corre­ spondence from all parts of the world expressing thanks for the presentation of statistics from a user's perspective. It has been particularIy pleasing to have been invited to contribute to course restructuring and development based on the ap­ proach to learning and applying statistics that underlies this book. In addition, I have taken account of suggestions and criticisms, and I hope that this new edition will address all major concerns. The range of readily accessible statistical methods has greatly expanded over the past decade, particularly with the growing accessibility of comprehensive statisti­ cal computing packages. The approach adopted in this book has anticipated the changes by its emphasis on building understanding and skills in method selection and interpretation of findings. There has been a reduction in computational for­ mulas to reflect the fact that basic statistical analyses are now almost universally undertaken on computers. This has allowed the inclusion of a more general cover­ age of unifying methodology, particularly Generalized linear methodology, which permits users to more accurately match their requirements to statistical models and methods. A major addition is a chapter on the commonly used multivariate methods.

• Analysis
• Chi-squared distribution
• Excel
• Normal distribution
• Probability distribution
• SAS
• SPSS
• Sage
• Statistical Analysis
• Statistical Computing
• Statistical Models
• sets

From the reviews of the second edition:

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

"The author covers a wide variety of applications. Each method is presented in sufficient depth to allow the reader to understand when the method should be used…I also appreciate the author’s tendency to refer reader’s to earlier sections or chapters for more foundational materials. In conclusion, I feel that this book would be a useful resource and a good text to use in training graduate-level statistical consultants on how to think about the application of statistics from a user’s perspective."

STATISTICAL METHODS IN MEDICAL RESEARCH

"The layout of the book is quite good…In all things pertaining to format, the author has been consistent…At the end of each example in the book, the report name and name of files holding data and statistical computing programs are provided. As such, even a student of statistics can benefit very much from this book. Although the book is not that big, it contains lots of valuable information. The author has really accomplished showing that statistics should be seen as an integral component of investigation and as a tool for conducting a well-informed post-mortem of data! The author has produces a masterpiece. His book can be used as a reference and as an undergraduate text for a course in statistics for those taking statistics as a service course as well as majors. As a reference manual, it is useful because it contains statistical methods from a wide spectrum starting with univariate statistical methods to multivariate statistical methods. As a student text, it is useful because the design is user friendly and calculus has been played down to some level without loss of direction…To crown it all, the author has achieved what he purposed in his mind. His immense experience in statistical consulting is something to go by. The book itself is a masterpiece and a ‘must have’ for every practicingstatistician."

"Statistical practitioners will find this book useful in many ways: as the basis of a short course … on introductory statistical methods … as a reference for the practitioner’s own use; and as a teaching tool to explain statistical theory and methods to our customers. … As a textbook, this book will be especially useful as an introduction for undergraduate students who will be taking additional upper-level courses in the experimental sciences or for graduate students with varied foundations in mathematical statistics." (Jay H. Goodman, Technometrics, Vol. 44 (3), 2002)

• School of Mathematics and Physics, The University of Tasmania, Hobart, Australia

  Glen McPherson

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