Elementary Statistics, 9th edition
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Hallmark features of this title
- Real-world examples illustrate every concept. You Try It! exercises follow most worked examples, helping students immediately check their understanding.
- Interpretation boxes explain the meaning and significance of statistical results in everyday life and the importance of interpreting results.
- What Does It Mean? boxes explain definitions, formulas and key facts in plain English, and summarize relevant discussions.
- Procedure boxes include the “why, when, and how” of statistical methods.
- Parallel Critical-Value/P-Value Approaches enable instructors to focus on one approach or to compare both approaches.
Published by Pearson (June 15th 2022) - Copyright © 2023
Subject: Introductory Statistics
Category: Elementary Statistics
Table of contents
(NOTE: Each chapter concludes with Chapter in Review, Review Problems, Focusing on Data Analysis, Case Study Discussion, and Biography.)
- 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
- 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∗
- 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
- 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
- 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∗
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
Appendix A: Statistical Tables
Appendix B: Answers to Selected Exercises
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