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This best-selling text is written for the introductory statistics course and students majoring in any field. Although the use of algebra is minimal, students should have completed at least an elementary algebra course. In many cases, underlying theory is included, but this book does not stress the mathematical rigor more suitable for mathematics majors. Elementary Statistics is appropriate for students pursuing careers in a variety of disciplines. The text emphasizes interpretating data.

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• 1 Introduction to Statistics
• Overview
• The Nature of Data
• Uses and Abuses of Statistics
• Design of Experiments
• 2 Describing, Exploring, and Comparing Data
• Overview
• Summarizing Data with Frequency Tables
• Pictures of Data
• Measures of Center
• Measures of Variation
• Measures of Position
• Exploratory Data Analysis (EDA)
• 3 Probability
• Overview
• Fundamentals
• Addition Rule
• Multiplication Rule: Basics
• Multiplication Rule: Complements and Conditional Probability
• Probabilities Through Simulations
• Counting
• 4 Probability Distributions
• Overview
• Random Variables
• Binomial Probability Distributions
• Mean, Variance, and Standard Deviation for the Binomial Distribution
• The Poisson Distribution
• 5 Normal Probability Distributions
• Overview
• The Standard Normal Distribution
• Nonstandard Normal Distributions: Finding Probabilities
• Nonstandard Normal Distributions: Finding Values
• The Central Limit Theorem
• Normal Distribution as Approximation to Binomial Distribution
• Determining Normality
• 6 Estimates and Sample Sizes
• Overview
• Estimating a Population Mean: Large Samples
• Estimating a Population Mean: Small Samples
• Determining Sample Size
• Estimating a Population Proportion
• Estimating a Population Variance
• 7 Hypothesis Testing
• Overview
• Fundamentals of Hypothesis Testing
• Testing a Claim about a Mean: Large Samples
• Testing a Claim about a Mean: Small Samples
• Testing a Claim about a Proportion
• Testing a Claim about a Standard Deviation or Variance
• 8 Inferences from Two Samples
• Overview
• Inferences about Two Means: Independent and Large Samples
• Inferences about Two Means: Matched Pairs
• Inferences about Two Proportions
• Comparing Variation in Two Samples
• Inferences about Two Means: Independent and Small Samples
• 9 Correlation and Regression
• Overview
• Correlation
• Regression
• Variation and Prediction Intervals
• Multiple Regression
• Modeling
• 10 Multinomial Experiments and Contingency Tables
• Overview
• Multinomial Experiments: Goodness-0f-Fit
• Contingency Tables: Independence and Homogeneity
• 11 Analysis of Variance
• Overview
• One-Way ANOVA
• Two-Way ANOVA
• 12 Nonparametric Statistics
• Overview
• Sign Test
• Wilcoxon Signed-Ranks Test for Matched Pairs
• Wilcoxon Rank-Sum Test for Two Independent Samples
• Kruskal-Wallis Test
• Rank Correlation
• Runs Test for Randomness
• 13 Statistical Process Control
• Overview
• Control Charts for Variation and Mean
• Control Charts for Attributes
• 14 Projects, Procedures, Perspectives
• Projects
• Procedure
• Perspective
• Appendices
• Appendix A Tables
• Appendix B Data Sets
• Appendix C TI-83 Plus Reference
• Appendix D Glossary
• Appendix E Bibliography
• Appendix F Answers to Odd-Numbered Exercises (and All Review Exercises and All Cumulative Review Exercises)
• Credits