Statistics in a Nutshell

ISBN-10: 1449316824

ISBN-13: 9781449316822

Edition: 2nd 2012

Authors: Sarah Boslaugh

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Need to learn statistics as part of your job, or want help passing a statistics course?Statistics in a Nutshellis a clear and concise introduction and reference for anyone who’s new to the subject. This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts.Each chapter in this thoroughly revised and expanded edition presents easy-to-follow descriptions illustrated by graphics, formulas, and lots 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.Statistics in a Nutshellincludes:Introductory material—Learn basic concepts of measurement and probability theory, data management for statistical analysis, research design and experimental design, how to write up your own results, and how to critique statistics presented by othersBasic inferential statistics—Discover the concepts of hypothesis testing, simple correlation, the distinction between parametric and nonparametric statistics, and learn simple methods of analysis appropriate to dichotomous, categorical, and continuous variablesAdvanced inferential techniques—Learn the General Linear Model, including analysis of Variance (ANOVA), multiple linear regression, and logistic and multinomial regressionSpecialized techniques—Use and interpret business and quality improvement statistics, medical and public health statistics, and educational and psychological statisticsIf you need to know how to perform most common statistical analyses, and how to use a wide range of statistical techniques without getting in over your head, this is the book for you.
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Book details

List price: $31.99
Edition: 2nd
Copyright year: 2012
Publisher: O'Reilly Media, Incorporated
Publication date: 11/13/2012
Binding: Paperback
Pages: 594
Size: 6.57" wide x 8.98" long x 1.17" tall
Weight: 1.804
Language: English

Sarah Boslaugh, Ph.D. has more than 20 years of experience working in data management and statistical analysis. She has worked as an SPSS programmer in many different settings in the public and private spheres, including academia, health care, government, and the insurance industry. Dr. Boslaugh received her Ph.D. in research methods and evaluation from the City University of New York and is currently a Senior Statistical Data Analyst at the Washington University School of Medicine in St. Louis. Her research interests include multilevel modeling, geographic information systems, and measurement theory.

Basic Concepts of Measurement
Levels of Measurement
True and Error Scores
Reliability and Validity
Measurement Bias
About Formulas
Basic Definitions
Defining Probability
Bayes' Theorem
Enough Exposition, Let's Do Some Statistics!
Inferential Statistics
Probability Distributions
Independent and Dependent Variables
Populations and Samples
The Central Limit Theorem
Hypothesis Testing
Confidence Intervals
The Z-Statistic
Data Transformations
Descriptive Statistics and Graphic Displays
Populations and Samples
Measures of Central Tendency
Measures of Dispersion
Graphic Methods
Bar Charts
Bivariate Charts
Categorical Data
The R�C Table
The Chi-Square Distribution
The Chi-Square Test
Fisher's Exact Test
McNemar's Test for Matched Pairs
Proportions: The Large Sample Case
Correlation Statistics for Categorical Data
The Likert and Semantic Differential Scales
The t-Test
The t Distribution
The One-Sample t-Test
The Independent Samples t-Test
Repeated Measures t-Test
Unequal Variance t-Test
The Pearson Correlation Coefficient
The Pearson Correlation Coefficient
The Coefficient of Determination
Introduction to Regression and ANOVA
The General Linear Model
Linear Regression
Analysis of Variance (ANOVA)
Calculating Simple Regression by Hand
Factorial ANOVA and ANCOVA
Factorial ANOVA
Multiple Linear Regression
Multiple Regression Models
Logistic, Multinomial, and Polynomial Regression
Logistic Regression
Multinomial Logistic Regression
Polynomial Regression
Factor Analysis, Cluster Analysis, and Discriminant Function Analysis
Factor Analysis
Cluster Analysis
Discriminant Function Analysis
Nonparametric Statistics
Between-Subjects Designs
Within-Subjects Designs
Business and Quality Improvement Statistics
Index Numbers
Time Series
Decision Analysis
Quality Improvement
Medical and Epidemiological Statistic
Measures of Disease Frequency
Ratio, Proportion, and Rate
Prevalence and Incidence
Crude, Category-Specific, and Standardized Rates
The Risk Ratio
The Odds Ratio
Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio
Power Analysis
Sample Size Calculations
Educational and Psychological Statistics
Standardized Scores
Test Construction
Classical Test Theory: The True Score Model
Reliability of a Composite Test
Measures of Internal Consistency
Item Analysis
Item Response Theory
Data Management
An Approach, Not a Set of Recipes
The Chain of Command
The Rectangular Data File
Spreadsheets and Relational Databases
Inspecting a New Data File
String and Numeric Data
Missing Data
Research Design
Basic Vocabulary
Observational Studies
Quasi-Experimental Studies
Experimental Studies
Gathering Experimental Data
Example Experimental Design
Communicating with Statistics
General Notes
Critiquing Statistics Presented by Others
Evaluating the Whole Article
The Misuse of Statistics
Common Problems
Quick Checklist
Issues in Research Design
Descriptive Statistics
Inferential Statistics
Review of Basic Mathematics
Introduction to Statistical Packages
Probability Tables for Common Distributions
Online Resources
Glossary of Statistical Terms
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