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Statistics in a Nutshell
Read an excerpt:
Interview with author Sarah Boslaugh
Excerpt provided courtesy of O'Reilly Media. Copyright © O'Reilly Media, Inc. Written permission from the publisher is required for any use of this material.
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Need to learn statistics as part of your job, or want some help passing a statistics
course? Statistics in a Nutshell is a clear and concise introduction and reference
that's perfect for anyone with no previous background in the subject. This book
gives you a solid understanding of statistics without being too simple, yet
without the numbing complexity of most college texts.
You get a firm grasp of the fundamentals and a hands-on understanding of how
to apply them before moving on to the more advanced material that follows. Each
chapter presents you with easy-to-follow descriptions illustrated by graphics,
formulas, and plenty of solved examples. Before you know it, you'll learn to
apply statistical reasoning and statistical techniques, from basic concepts
of probability and hypothesis testing to multivariate analysis.
Organized into four distinct sections, Statistics in a Nutshell offers you:
• Introductory material: Different ways to think about statistics
• Basic concepts of measurement and probability theory
• Data management for statistical analysis
• Research design and experimental design
• How to critique statistics presented by others
• Basic inferential statistics: Basic concepts of inferential statistics
• The concept of correlation, when it is and is not an appropriate measure of
association
• Dichotomous and categorical data
• The distinction between parametric and nonparametric statistics
• Advanced inferential techniques: The General Linear Model
• Analysis of Variance (ANOVA) and MANOVA
• Multiple linear regression
• Specialized techniques: Business and quality improvement statistics
• Medical and public health statistics
• Educational and psychological statistics
Unlike many introductory books on the subject, Statistics in a Nutshell doesn't
omit important material in an effort to dumb it down. And this book is far more
practical than most college texts, which tend to over-emphasize calculation
without teaching you when and how to apply different statistical tests.
With Statistics in a Nutshell, you learn how to perform most common statistical
analyses, and understand statistical techniques presented in research articles.
If you need to know how to use a wide range of statistical techniques without
getting in over your head, this is the book you want.
About the Authors
Sarah Boslaugh holds a PhD in Research and Evaluation from the City
University of New York and have been working as a statistical analyst for 15
years, in a variety of professional settings, including the New York City Board
of Education, the Institutional Research Office of the City University of New
York, Montefiore Medical Center, the Virginia Department of Social Services,
Magellan Health Services, Washington University School of Medicine, and BJC
HealthCare. She has taught statistics in several different contexts and currently
teaches Intermediate Statistics at Washington University Medical School. She
has published two previous books: An Intermediate Guide to SPSS Programming:
Using Syntax for Data Management (SAGE Publications, 2004) and Secondary Data
Sources for Public Health (forthcoming from Cambridge U. Press, 2007) and am
currently editing the Encyclopedia of Epidemiology for SAGE Publications (forthcoming,
2007).
Paul A. Watters PhD CITP, is Head of Data Services at the Medical Research
Council's National Survey of Health and Development, which is the oldest of
the British birth cohort studies. He is also an honorary senior research fellow
at University College London. Dr. Watters is the project manager for the MRC's
Data Access Project, and is presently investigating methods for securing investigator
access to public health data in large-scale distributed systems in a challenging
ethical and legal environment. He has an active research interest in the use
of orthogonal and non-orthogonal methods for feature extraction in pattern recognition,
especially in biometric applications.
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