Elementary Statistics, 9th edition

• (NOTE: Each chapter concludes with Chapter in Review, Review Problems, Focusing on Data Analysis, Case Study Discussion, and Biography.)

• Technology Resources
• Data Sources

I: INTRODUCTION

1. The Nature of Statistics
   • 1.1 Statistics Basics
   • 1.2 Simple Random Sampling
   • 1.3 Other Sampling Designs∗
   • 1.4 Experimental Designs∗

II: DESCRIPTIVE STATISTICS

2. Organizing Data
   • Case Study: World's Richest People
   • 2.1 Variables and Data
   • 2.2 Organizing Qualitative Data
   • 2.3 Organizing Quantitative Data
   • 2.4 Distribution Shapes
   • 2.5 Misleading Graphs∗
3. Descriptive Measures
   • Case Study: The Beatles' Song Length
   • 3.1 Measures of Center
   • 3.2 Measures of Variation
   • 3.3 Chebyshev's Rule and the Empirical Rule∗
   • 3.4 The Five-Number Summary; Boxplots
   • 3.5 Descriptive Measures for Populations; Use of Samples
4. Descriptive Methods in Regression and Correlation
   • Case Study: Healthcare: Spending and Outcomes
   • 4.1 Linear Equations with One Independent Variable
   • 4.2 The Regression Equation
   • 4.3 The Coefficient of Determination
   • 4.4 Linear Correlation

III: PROBABILITY, RANDOM VARIABLES, AND SAMPLING DISTRIBUTIONS

5. Probability and Random Variables
   • Case Study: Texas Hold ‘em
   • 5.1 Probability Basics
   • 5.2 Events
   • 5.3 Some Rules of Probability
   • 5.4 Discrete Random Variables and Probability Distributions∗
   • 5.5 The Mean and Standard Deviation of a Discrete Random Variable∗
   • 5.6 The Binomial Distribution∗
6. The Normal Distribution
   • Case Study: Chest Sizes of Scottish Militiamen
   • 6.1 Introducing Normally Distributed Variables
   • 6.2 Areas under the Standard Normal Curve
   • 6.3 Working with Normally Distributed Variables
   • 6.4 Assessing Normality; Normal Probability Plots
7. The Sampling Distribution of the Sample Mean
   • Case Study: The Chesapeake and Ohio Freight Study
   • 7.1 Sampling Error; the Need for Sampling Distributions
   • 7.2 The Mean and Standard Deviation of the Sample Mean
   • 7.3 The Sampling Distribution of the Sample Mean

IV: INFERENTIAL STATISTICS

8. Confidence Intervals for One Population Mean
   • Case Study: Bank Robberies: A Statistical Analysis
   • 8.1 Estimating a Population Mean
   • 8.2 Confidence Intervals for One Population Mean When σ Is Known
   • 8.3 Confidence Intervals for One Population Mean When σ Is Unknown
9. Hypothesis Tests for One Population Mean
   • Case Study: Gender and Sense of Direction
   • 9.1 The Nature of Hypothesis Testing
   • 9.2 Critical-Value Approach to Hypothesis Testing
   • 9.3 P-Value Approach to Hypothesis Testing
   • 9.4 Hypothesis Tests for One Population Mean When σ Is Known
   • 9.5 Hypothesis Tests for One Population Mean When σ Is Unknown 
10. Inferences for Two Population Means
    • Case Study: Dexamethasone Therapy and IQ
    • 10.1 The Sampling Distribution of the Difference between Two Sample Means for Independent Samples
    • 10.2 Inferences for Two Population Means, Using Independent Samples: Standard Deviations Assumed Equal
    • 10.3 Inferences for Two Population Means, Using Independent Samples: Standard Deviations Not Assumed Equal
    • 10.4 Inferences for Two Population Means, Using Paired Samples
11. Inferences for Population Proportions
    • Case Study: Arrested Youths
    • 11.1 Confidence Intervals for One Population Proportion
    • 11.2 Hypothesis Tests for One Population Proportion
    • 11.3 Inferences for Two Population Proportions
12. Chi-Square Procedures
    • Case Study: Eye and Hair Color
    • 12.1 The Chi-Square Distribution
    • 12.2 Chi-Square Goodness-of-Fit Test
    • 12.3 Contingency Tables; Association
    • 12.4 Chi-Square Independence Test
    • 12.5 Chi-Square Homogeneity Test
13. Analysis of Variance (ANOVA)
    • Case Study: Self-Perception and Physical Activity
    • 13.1 The F-Distribution
    • 13.2 One-Way ANOVA: The Logic
    • 13.3 One-Way ANOVA: The Procedure
14. Inferential Methods in Regression and Correlation
    • Case Study: Shoe Size and Height
    • 14.1 The Regression Model; Analysis of Residuals
    • 14.2 Inferences for the Slope of the Population Regression Line
    • 14.3 Estimation and Prediction
    • 14.4 Inferences in Correlation

• *Indicates optional material.

APPENDICES

1. Statistical Tables
2. Answers to Selected Exercises

Index

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