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This edition of this text has more real data sets, contemporary data applications and real-world examples. It incorporates a modern, exploratory analysis without abandoning traditional organization and good solid coverage. The everyday use of statistics is consistently demonstrated, allowing students to appreciate the relevance of statistics to their concerns. An increased emphasis on conceptual understanding and interpretation encourages students to conceptualize problems rather solving them by rote.

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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