## Description

## Elementary Statistics 3rd Edition

This book is designed for an introductory course in statistics. The mathematical prerequisite is basic algebra. In addition to presenting the mechanics of the subject, we have endeavored to explain the concepts behind them, in a writing style as straightforward, clear, and engaging as we could make it. As practicing statisticians, we have done everything possible to ensure that the material presented is accurate and correct. We believe that this book will enable instructors to explore statistical concepts in-depth yet remain easy for students to read and understand.

To achieve this goal, we have incorporated a number of useful pedagogical features:

### Features:

• Check Your Understanding Exercises: After each concept is explained, one or more exercises are immediately provided for students to be sure they are following the material. These exercises provide students with confidence that they are ready to go on or alert them to the need to review the material just covered.

• Explain It Again: Many important concepts are reinforced with additional explanations in these marginal notes.

• Real Data: Statistics instructors universally agree that the use of real data engages students and convinces them of the usefulness of the subject. A great many of the examples and exercises use real data. Some data sets explore topics in health or social sciences, while others are based on popular cultures such as movies, contemporary music, or video games.

• Integration of Technology: Many examples contain screenshots from the TI-84 Plus calculator, MINITAB, and Excel. Each section contains detailed, step-by-step instructions, where applicable, explaining how to use these forms of technology to carry out the procedures explained in the text.

• Interpreting Technology: Many exercises present output from technology and require the student to interpret the results.

• Write About It: These exercises, found at the end of each chapter, require students to explain statistical concepts in their own words.

• Case Studies: Each chapter begins with a discussion of a real problem. At the end of the chapter, a case study demonstrates applications of chapter concepts to the problem.

### Flexibility

We have endeavored to make our book flexible enough to work effectively with a wide variety of instructor styles and preferences. We cover both the P-value and critical value approaches to hypothesis testing, so instructors can choose to cover either or both of these methods. The material on two-sample inference is divided into two chapters— Chapter 10 on two-sample confidence intervals, and Chapter 11 on two-sample hypothesis tests. This

gives instructors the option of covering all the material on confidence intervals before starting hypothesis testing, by covering Chapter 10 immediately after Chapter 8.

We have placed the material on descriptive statistics for bivariate data immediately following descriptive statistics for univariate data. Those who wish to cover bivariate description and inference together may postpone Chapter 4 until sometime before covering Chapter 13.

Instructors differ widely in their preferences regarding the depth of coverage of probability. Light treatment of the subject may be obtained by covering Section 5.1 and skipping the rest of the chapter. More depth can be obtained by covering Sections 5.2 and 5.3. Section 5.4 on counting can be included for an even more comprehensive treatment.

### Supplements

Supplements, including online homework, videos, guided student notes, and PowerPoint presentations, play an increasingly important role in the educational process. As authors, we have adopted a hands-on approach to the development of our supplements, to make sure that they are consistent with the style of the text and that they work effectively with a variety of instructor preferences. In particular, our online homework package offers instructors the flexibility to choose whether the solutions that students view are based on tables or technology, where applicable.

## New in This Edition

The third edition of the book is intended to extend the strengths of the second. Some of the changes are:

• A new objective on the weighted mean has been added.

• A large number of new exercises have been included, many of which involve real data from recent sources.

• Many new conceptual exercises have been added, for example, about detecting confounding in public health studies, drawing inferences from the shape of a histogram, interpreting technology output to detect outliers, reinforcing the concept that the Central Limit Theorem applies to the sample mean rather than individual sample items, and understanding the effect of sample size on the margin of error of a confidence interval.

• A new supplement to accompany the text has been developed that focuses on prerequisite skills.

• Several of the case studies have been updated.

• The exposition has been improved in a number of places.

## Brief Contents:

C H A P T E R 1 Basic Ideas

C H A P T E R 2 Graphical Summaries of Data

C H A P T E R 3 Numerical Summaries of Data

C H A P T E R 4 Summarizing Bivariate Data

C H A P T E R 5 Probability

C H A P T E R 6 Discrete Probability Distributions

C H A P T E R 7 The Normal Distribution

C H A P T E R 8 Confidence Intervals

C H A P T E R 9 Hypothesis Testing

C H A P T E R 10 Two-Sample Confidence Intervals

C H A P T E R 11 Two-Sample Hypothesis Tests

C H A P T E R 12 Tests with Qualitative Data

C H A P T E R 13 Inference in Linear Models

C H A P T E R 14 Analysis of Variance

C H A P T E R 15 Nonparametric Statistics

## Contents

Preface

Acknowledgments

Index of Applications

**CHAPTER 1 Basic Ideas**

1.1 Sampling

1.2 Types of Data

1.3 Design of Experiments

1.4 Bias in Studies

Chapter 1 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 2 Graphical Summaries of Data**

2.1 Graphical Summaries for Qualitative Data

2.2 Frequency Distributions and Their Graphs

2.3 More Graphs for Quantitative Data

2.4 Graphs Can Be Misleading

Chapter 2 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 3 Numerical Summaries of Data**

3.1 Measures of Center

3.2 Measures of Spread

3.3 Measures of Position

Chapter 3 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 4 Summarizing Bivariate Data**

4.1 Correlation

4.2 The Least-Squares Regression Line

4.3 Features and Limitations of the Least-Squares Regression Line

Chapter 4 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 5 Probability**

5.1 Basic Concepts of Probability

5.2 The Addition Rule and the Rule of Complements

5.3 Conditional Probability and the Multiplication Rule

5.4 Counting

Chapter 5 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 6 Discrete Probability Distributions**

6.1 Random Variables

6.2 The Binomial Distribution

6.3 The Poisson Distribution

Chapter 6 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 7 The Normal Distribution**

7.1 The Standard Normal Curve

7.2 Applications of the Normal Distribution

7.3 Sampling Distributions and the Central Limit Theorem

7.4 The Central Limit Theorem for Proportions

7.5 The Normal Approximation to the Binomial Distribution

7.6 Assessing Normality

Chapter 7 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 8 Confidence Intervals**

8.1 Confidence Intervals for a Population Mean, Standard Deviation Known

8.2 Confidence Intervals for a Population Mean, Standard Deviation Unknown

8.3 Confidence Intervals for a Population Proportion

8.4 Confidence Intervals for a Standard Deviation

8.5 Determining Which Method to Use

Chapter 8 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

CHAPTER 9 Hypothesis Testing

9.1 Basic Principles of Hypothesis Testing

9.2 Hypothesis Tests for a Population Mean, Standard Deviation Known

9.3 Hypothesis Tests for a Population Mean, Standard Deviation Unknown

9.4 Hypothesis Tests for Proportions

9.5 Hypothesis Tests for a Standard Deviation

9.6 Determining Which Method to Use

9.7 Power

Chapter 9 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 10 Two-Sample Confidence Intervals**

10.1 Confidence Intervals for the Difference Between Two Means: Independent Samples

10.2 Confidence Intervals for the Difference Between Two Proportions

10.3 Confidence Intervals for the Difference Between Two Means: Paired Samples

Chapter 10 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 11 Two-Sample Hypothesis Tests**

11.1 Hypothesis Tests for the Difference Between Two Means: Independent Samples

11.2 Hypothesis Tests for the Difference Between Two Proportions

11.3 Hypothesis Tests for the Difference Between Two Means: Paired

Samples

11.4 Hypothesis Tests for Two Population Standard Deviations

11.5 The Multiple Testing Problem

Chapter 11 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 12 Tests with Qualitative Data**

12.1 Testing Goodness of Fit

12.2 Tests for Independence and Homogeneity

Chapter 12 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 13 Inference in Linear Models**

13.1 Inference on the Slope of the Regression Line

13.2 Inference About the Response

13.3 Multiple Regression

Chapter 13 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 14 Analysis of Variance**

14.1 One-Way Analysis of Variance

14.2 Two-Way Analysis of Variance

Chapter 14 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

**CHAPTER 15 Nonparametric Statistics**

15.1 The Sign Test

15.2 The Rank-Sum Test

15.3 The Signed-Rank Test

Chapter 15 Summary

Vocabulary and Notation

Chapter Quiz

Review Exercises

Appendix A Tables A-1

Appendix B TI-84 PLUS Stat Wizards B-1

Answers to Odd-Numbered Exercises (Student edition only) SA-1

Answers to Selected Exercises (Instructor’s edition only) IA-1

Index

## About the Authors:

William Navidi is a professor of Applied Mathematics and Statistics at the Colorado School of Mines in Golden, Colorado. He received a Bachelor’s degree in Mathematics from New College, a Master’s degree in Mathematics from Michigan State University, and a Ph.D. in Statistics from the University of California at Berkeley. Bill began his teaching career at the County College of Morris, a two-year college in Dover, New Jersey. He has taught mathematics and statistics at all levels, from developmental through the graduate level. Bill has written two Engineering Statistics textbooks for McGraw-Hill. In his spare time, he likes to play racquetball.

Barry Monk is a Professor of Mathematics at Middle Georgia State University in Macon, Georgia. Barry received a Bachelor of Science in Mathematical Statistics, a Master of Arts in Mathematics specializing in Optimization and Statistics, and a Ph.D. in Applied Mathematics, all from the University of Alabama. Barry has been teaching Introductory Statistics since 1992 in the classroom and online environments. Barry has a minor in Creative Writing and is a skilled jazz pianist.

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