Efnisyfirlit

• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• Acknowledgments
• Chapter 1. Introduction
  • 1.1 Introduction to Statistical Methodology
  • 1.2 Descriptive Statistics and Inferential Statistics
  • 1.3 The Role of Computers and Software in Statistics
  • 1.4 Chapter Summary
  • Exercises
• Chapter 2. Sampling and Measurement
  • 2.1 Variables and Their Measurement
  • 2.2 Randomization
  • 2.3 Sampling Variability and Potential Bias
  • 2.4 Other Probability Sampling Methods*
  • 2.5 Chapter Summary
  • Exercises
• Chapter 3. Descriptive Statistics
  • 3.1 Describing Data with Tables and Graphs
  • 3.2 Describing the Center of the Data
  • 3.3 Describing Variability of the Data
  • 3.4 Measures of Position
  • 3.5 Bivariate Descriptive Statistics
  • 3.6 Sample Statistics and Population Parameters
  • 3.7 Chapter Summary
  • Exercises
• Chapter 4. Probability Distributions
  • 4.1 Introduction to Probability
  • 4.2 Probability Distributions for Discrete and Continuous Variables
  • 4.3 The Normal Probability Distribution
  • 4.4 Sampling Distributions Describe How Statistics Vary
  • 4.5 Sampling Distributions of Sample Means
  • 4.6 Review: Population, Sample Data, and Sampling Distributions
  • 4.7 Chapter Summary
  • Exercises
• Chapter 5. Statistical Inference: Estimation
  • 5.1 Point and Interval Estimation
  • 5.2 Confidence Interval for a Proportion
  • 5.3 Confidence Interval for a Mean
  • 5.4 Choice of Sample Size
  • 5.5 Estimation Methods: Maximum Likelihood and the Bootstrap*
  • 5.6 Chapter Summary
  • Exercises
• Chapter 6. Statistical Inference: Significance Tests
  • 6.1 The Five Parts of a Significance Test
  • 6.2 Significance Test for a Mean
  • 6.3 Significance Test for a Proportion
  • 6.4 Decisions and Types of Errors in Tests
  • 6.5 Limitations of Significance Tests
  • 6.6 Finding P(Type II Error)
  • 6.7 Small-Sample Test for a Proportion—The Binomial Distribution*
  • 6.8 Chapter Summary
  • Exercises
• Chapter 7. Comparison of Two Groups
  • 7.1 Preliminaries for Comparing Groups
  • 7.2 Categorical Data: Comparing Two Proportions
  • 7.3 Quantitative Data: Comparing Two Means
  • 7.4 Comparing Means with Dependent Samples
  • 7.5 Other Methods for Comparing Means*
  • 7.6 Other Methods for Comparing Proportions*
  • 7.7 Nonparametric Statistics for Comparing Groups*
  • 7.8 Chapter Summary
  • Exercises
• Chapter 8. Analyzing Association Between Categorical Variables
  • 8.1 Contingency Tables
  • 8.2 Chi-Squared Test of Independence
  • 8.3 Residuals: Detecting the Pattern of Association
  • 8.4 Measuring Association in Contingency Tables
  • 8.5 Association Between Ordinal Variables*
  • 8.6 Chapter Summary
  • Exercises
• Chapter 9. Linear Regression and Correlation
  • 9.1 Linear Relationships
  • 9.2 Least Squares Prediction Equation
  • 9.3 The Linear Regression Model
  • 9.4 Measuring Linear Association: The Correlation
  • 9.5 Inferences for the Slope and Correlation
  • 9.6 Model Assumptions and Violations
  • 9.7 Chapter Summary
  • Exercises
• Chapter 10. Introduction to Multivariate Relationships
  • 10.1 Association and Causality
  • 10.2 Controlling for Other Variables
  • 10.3 Types of Multivariate Relationships
  • 10.4 Inferential Issues in Statistical Control
  • 10.5 Chapter Summary
  • Exercises
• Chapter 11. Multiple Regression and Correlation
  • 11.1 The Multiple Regression Model
  • 11.2 Multiple Correlation and R2
  • 11.3 Inferences for Multiple Regression Coefficients
  • 11.4 Modeling Interaction Effects
  • 11.5 Comparing Regression Models
  • 11.6 Partial Correlation*
  • 11.7 Standardized Regression Coefficients*
  • 11.8 Chapter Summary
  • Exercises
• Chapter 12. Regression with Categorical Predictors: Analysis of Variance Methods
  • 12.1 Regression Modeling with Dummy Variables for Categories
  • 12.2 Multiple Comparisons of Means
  • 12.3 Comparing Several Means: Analysis of Variance
  • 12.4 Two-Way ANOVA and Regression Modeling
  • 12.5 Repeated-Measures Analysis of Variance*
  • 12.6 Two-Way ANOVA with Repeated Measures on a Factor*
  • 12.7 Chapter Summary
  • Exercises
• Chapter 13. Multiple Regression with Quantitative and Categorical Predictors
  • 13.1 Models with Quantitative and Categorical Explanatory Variables
  • 13.2 Inference for Regression with Quantitative and Categorical Predictors
  • 13.3 Case Studies: Using Multiple Regression in Research
  • 13.4 Adjusted Means*
  • 13.5 The Linear Mixed Model*
  • 13.6 Chapter Summary
  • Exercises
• Chapter 14. Model Building with Multiple Regression
  • 14.1 Model Selection Procedures
  • 14.2 Regression Diagnostics
  • 14.3 Effects of Multicollinearity
  • 14.4 Generalized Linear Models
  • 14.5 Nonlinear Relationships: Polynomial Regression
  • 14.6 Exponential Regression and Log Transforms*
  • 14.7 Robust Variances and Nonparametric Regression*
  • 14.8 Chapter Summary
  • Exercises
• Chapter 15. Logistic Regression: Modeling Categorical Responses
  • 15.1 Logistic Regression
  • 15.2 Multiple Logistic Regression
  • 15.3 Inference for Logistic Regression Models
  • 15.4 Logistic Regression Models for Ordinal Variables*
  • 15.5 Logistic Models for Nominal Responses*
  • 15.6 Loglinear Models for Categorical Variables*
  • 15.7 Model Goodness-of-Fit Tests for Contingency Tables*
  • 15.8 Chapter Summary
  • Exercises
• Chapter 16. An Introduction to Advanced Methodology
  • 16.1 Missing Data: Adjustment Using Multiple Imputation*
  • 16.2 Multilevel (Hierarchical) Models*
  • 16.3 Event History Models*
  • 16.4 Path Analysis*
  • 16.5 Factor Analysis*
  • 16.6 Structural Equation Models*
  • 16.7 Markov Chains*
  • 16.8 The Bayesian Approach to Statistical Inference*
  • Exercises
• Appendix: R, Stata, SPSS, and SAS for Statistical Analyses
• Bibliography
• Credits
• Index
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • Y
  • Z
• Key Formulas for Statistical Methods
• Table A: Normal curve tail probabilities. Standard normal probability in right-hand tail (for negati
• Table B: t Distribution Critical Values
• A Guide to Choosing a Statistical Method

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