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:
- 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
- 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.