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• Introductory Statistics for the Behavioral Sciences
• Copyright Page
• Table of Contents
• Dedication
• Preface
• Acknowledgments
• Glossary of Symbols
• PART I: INTRODUCTION
• Chapter 1. Introduction
• Descriptive and Inferential Statistics
• Populations, Samples, Parameters, and Statistics
• Summation Notation
• Summary
• PART II: DESCRIPTIVE STATISTICS
• Chapter 2. Frequency distributions and graphs
• The Purpose of Descriptive Statistics
• Regular Frequency Distributions
• Cumulative Frequency Distributions
• Grouped Frequency Distributions
• Graphic Representations
• Shapes of Frequency Distributions
• Summary
• Chapter 3. Transformed scores I: Percentiles
• Definition of Percentiles
• Deciles, Quartiles, and the Median
• Summary
• Chapter 4. Measures of central tendency
• The Mean
• The Median
• The Mode
• Summary
• Chapter 5. Measures of variability
• The Concept of Variability
• The Range
• The Standard Deviation and Variance
• Summary
• Chapter 6. Transformed scores II : Z and T scores
• Rules for Changing X and σ
• Standard Scores (Z Scores)
• T Scores
• SAT Scores
• Summary
• Appendix to Chapter 6 : Proofs of Rules for Changing X and σ
• PART III: INFERENTIAL STATISTICS
• Chapter 7. The general strategy of inferential statistics
• The Goals of Inferential Statistics
• The Strategy of Inferential Statistics
• Statistical Models
• Summary
• Chapter 8. The normal curve model
• Score Distributions
• Characteristics of the Normal Curve
• Illustrative Examples
• Summary
• Chapter 9. Inferences about the mean of a single population
• The Standard Error of the Mean
• Hypothesis Testing
• The Statistical Test for the Mean of a Single Population when σ Is Known
• The Statistical Test for the Mean of a Single Population when σ Is Not Known : The t Distributions
• Interval Estimation
• The Standard Error of a Proportion
• One-Tailed Tests of Significance
• Summary
• Chapter 10. Testing hypotheses about the difference between the means of two populations
• The Standard Error of the Difference
• Estimating the Standard Error of the Difference
• The t Test for Two Sample Means
• Measures of the Strength of the Relationship between the Two Variables
• Confidence Intervals for μ1 — μ2
• Using the t Test for Two Sample Means: Some General Considerations
• The t Test for Matched Samples
• Summary
• Chapter 11. Linear correlation and prediction
• Describing the Linear Relationship between Two Variables
• Testing the Significance of the Correlation Coefficient
• Prediction and Linear Regression
• Measuring Prediction Error: The Standard Error of Estimate
• Summary
• Appendix to Chapter 11 : Equivalence of the Various Formulas for r
• Chapter 12. Other correlational techniques
• The Relationship between Ranked Variables: The Spearman Rank-Order Correlation Coefficient
• The Relationship between One Dichotomous and One Continuous Variable
• The Relationship between Two Dichotomous Variables
• Summary
• Chapter 13. Introduction to power analysis
• Concepts of Power Analysis
• The Test of the Mean of a Single Population
• The Significance Test of the Proportion of a Single Population
• The Significance Test of a Pearson r
• Testing the Significance of the Difference between Independent Means
• Summary
• Chapter 14. One-way analysis of variance
• The General Logic of ANOVA
• Computational Procedures
• One-Way ANOVA with Unequal Sample Sizes
• Some Comments on the Use of ANOVA
• Summary
• Appendix to Chapter 14 : Proof That Total Variance is Equal to the Sum of between-Group and within-G
• Chapter 15. Introduction to factorial design: Two-way analysis of variance
• Computational Procedures
• The Meaning of Interaction
• Summary
• Chapter 16. Chi square
• Chi Square and Goodness of Fit: One-Variable Problems
• Chi Square as a Test of Independence: Two-Variable Problems
• Measures of Strength of Association in Two-Variable Tables
• Summary
• Appendix
• Index