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Need to learn statistics for your job? Want help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference for anyone new to the subject. Thoroughly revised and expanded, this edition helps you gain a solid understanding of statistics without the numbing complexity of many college texts.

Each chapter presents easy-to-follow descriptions, along with graphics, formulas, solved examples, and hands-on exercises. If you want to perform common statistical analyses and learn a wide range of techniques without getting in over your head, this is your book.

• Learn basic concepts of measurement and probability theory, data management, and research design
• Discover basic statistical procedures, including correlation, the t-test, the chi-square and Fisher’s exact tests, and techniques for analyzing nonparametric data
• Learn advanced techniques based on the general linear model, including ANOVA, ANCOVA, multiple linear regression, and logistic regression
• Use and interpret statistics for business and quality improvement, medical and public health, and education and psychology
• Communicate with statistics and critique statistical information presented by others

1. Preface
2. 1. Basic Concepts of Measurement
1. Measurement
2. Levels of Measurement
3. True and Error Scores
4. Reliability and Validity
5. Measurement Bias
6. Exercises
3. 2. Probability
2. Basic Definitions
3. Defining Probability
1. Expressing the Probability of an Event
2. Conditional Probabilities
3. Calculating the Probability of Multiple Events
4. Bayes’ Theorem
5. Enough Exposition, Let’s Do Some Statistics!
6. Exercises
4. 3. Inferential Statistics
1. Probability Distributions
2. Independent and Dependent Variables
3. Populations and Samples
4. The Central Limit Theorem
5. Hypothesis Testing
6. Confidence Intervals
7. p-values
8. The Z-Statistic
9. Data Transformations
10. Exercises
5. 4. Descriptive Statistics and Graphic Displays
1. Populations and Samples
2. Measures of Central Tendency
3. Measures of Dispersion
4. Outliers
5. Graphic Methods
6. Bar Charts
7. Bivariate Charts
8. Exercises
6. 5. Categorical Data
1. The R×C Table
2. The Chi-Square Distribution
3. The Chi-Square Test
4. Fisher’s Exact Test
5. McNemar’s Test for Matched Pairs
6. Proportions: The Large Sample Case
7. Correlation Statistics for Categorical Data
8. The Likert and Semantic Differential Scales
9. Exercises
7. 6. The t-Test
1. The t Distribution
2. The One-Sample t-Test
3. The Independent Samples t-Test
4. Repeated Measures t-Test
5. Unequal Variance t-Test
6. Exercises
8. 7. The Pearson Correlation Coefficient
1. Association
2. Scatterplots
3. The Pearson Correlation Coefficient
4. The Coefficient of Determination
5. Exercises
9. 8. Introduction to Regression and ANOVA
1. The General Linear Model
2. Linear Regression
3. Analysis of Variance (ANOVA)
4. Calculating Simple Regression by Hand
5. Exercises
10. 9. Factorial ANOVA and ANCOVA
1. Factorial ANOVA
2. ANCOVA
3. Exercises
11. 10. Multiple Linear Regression
1. Multiple Regression Models
1. Dummy Variables
2. Methods for Building Regression Models
2. Exercises
12. 11. Logistic, Multinomial, and Polynomial Regression
1. Logistic Regression
2. Multinomial Logistic Regression
3. Polynomial Regression
4. Overfitting
5. Exercises
13. 12. Factor Analysis, Cluster Analysis, and Discriminant Function Analysis
14. 13. Nonparametric Statistics
1. Between-Subjects Designs
2. Within-Subjects Designs
3. Exercises
15. 14. Business and Quality Improvement Statistics
1. Index Numbers
2. Time Series
3. Decision Analysis
4. Quality Improvement
5. Exercises
16. 15. Medical and Epidemiological Statistics
1. Measures of Disease Frequency
2. Ratio, Proportion, and Rate
3. Prevalence and Incidence
4. Crude, Category-Specific, and Standardized Rates
5. The Risk Ratio
6. The Odds Ratio
7. Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio
8. Power Analysis
9. Sample Size Calculations
10. Exercises
17. 16. Educational and Psychological Statistics
1. Percentiles
2. Standardized Scores
3. Test Construction
4. Classical Test Theory: The True Score Model
5. Reliability of a Composite Test
6. Measures of Internal Consistency
7. Item Analysis
8. Item Response Theory
9. Exercises
18. 17. Data Management
19. 18. Research Design
1. Basic Vocabulary
2. Observational Studies
3. Quasi-Experimental Studies
4. Experimental Studies
5. Gathering Experimental Data
6. Example Experimental Design
20. 19. Communicating with Statistics
1. General Notes
21. 20. Critiquing Statistics Presented by Others
1. Evaluating the Whole Article
2. The Misuse of Statistics
3. Common Problems
4. Quick Checklist
5. Issues in Research Design
6. Descriptive Statistics
7. Inferential Statistics
1. Assumptions of Statistical Tests
22. A. Review of Basic Mathematics
1. Laws of Arithmetic
2. Properties of Real Numbers
3. Exponents and Roots
4. Solving Equations
5. Systems of Equations
6. Graphing Equations
7. Linear Inequalities
8. Fractions
9. Factorials, Permutations, and Combinations
10. Exercises