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# Introductory Statistics

## A Problem Solving ApproachSecond Edition| ©2015 Stephen Kokoska

*Introductory Statistics: A Problem-Solving Approach*is now supported by Sapling Intro Stats, a powerful online homework system in which every problem is an opportunity for a student to learn through meaningful hints, targeted feedback, and detailed solutions. Sapling Intro Stats provides stud...

*Introductory Statistics: A Problem-Solving Approach*is now supported by Sapling Intro Stats, a powerful online homework system in which every problem is an opportunity for a student to learn through meaningful hints, targeted feedback, and detailed solutions. Sapling Intro Stats provides students with support to navigate their homework and ultimately understand statistical concepts and calculations. Read more at

#### Institutional Prices

Hardcover + Sapling Homework-Only for Statistics (Single-Term Online)
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Hardcover + Sapling Homework-Only for Statistics (Multi-Term Online)
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Loose-Leaf + LaunchPad
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Loose-Leaf + Sapling Homework-Only for Statistics (Single-Term Online)
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C$112.02

Loose-Leaf + Sapling Homework-Only for Statistics (Multi-Term Online)
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## Stephen Kokoska blends solid mathematics with a distinctive problem-solving approach.

*Introductory Statistics: A Problem-Solving Approach* is now supported by Sapling Intro Stats, a powerful online homework system in which every problem is an opportunity for a student to learn through meaningful hints, targeted feedback, and detailed solutions. Sapling Intro Stats provides students with support to navigate their homework and ultimately understand statistical concepts and calculations. Read more at

Features

**Focus on Statistical Inference.** The main theme of this text is statistical inference and decision making through the interpretation of numerical results. The process of statistical inference is introduced in a variety of applications and statistical settings all using a similar, carefully delineated, four-step approach: Claim, Experiment, Likelihood, and Conclusion.

**Step-by-Step Solutions.** The solutions to selected examples are presented in logical, systematic steps. This approach helps guide the reader through the necessary calculations in order to find a solution and interpret results.

**Solution Explanations.** Each part in a solution is explained, or justified, so that the reader can easily follow each step in a solution.

**A Closer Look.** The details provided in these sections offer straightforward explanations of various definitions and concepts. The itemized specifics, including hints, tips, and reminders, make it easier for the reader to interpret, comprehend, and learn important statistical ideas.

**How To Boxes.** This feature provides clear steps for constructing basic graphs or performing essential calculations. The title in a How To box is color coded and easy to locate with each chapter.

**Definition/Formula Boxes.** Definitions and formulas are clearly marked and outlined with clean, crisp color-coded lines.

**Data Sets.** All data sets presented in the examples and exercises are available on the text’s website (www.macmillanlearning.com/introstats3e) in various formats. This makes it easy for students and instructors to use the most familiar and comfortable technology.

**Grouped Exercises.** There is a wide variety of interesting, engaging exercises on relevant topics, based on current data, at the end of each section. These problems provide plenty of opportunity for practice, review, and application of concepts. Exercises are grouped according to: **Concept Check:** True/False, Fill in the Blank, and Short Answer exercises designed to reinforce the basic concepts presented in the section.**Practice:** Basic, introductory problems to familiarize students with the relevant applications and solution methods.**Applications:** Realistic, appealing exercises to build confidence and promote routine understanding.

Extended Applications: Applied problems that require extra creativity and thought.**Challenge:** Additional exercises and technology projects that allow students to

discover more advanced concepts and connections.

At the end of each chapter, **Chapter Exercises** help to test the student's overall understanding of the chapter’s concepts and to practice for assessments. Answers to odd-numbered section and chapter exercises are given at the back of the book.

**Chapter Summary.** A table at the end of each chapter provides a short summary and page reference for each key concept, relevant notation, and formula.

**Theory Symbols.** In only a few places in the text, more advanced material is offset with blue, half-moon symbols. This material can be skipped by the typical reader, but provides more complete explanations of various topics.

New to This Edition

**LaunchPad**

W.H. Freeman’s breakthrough online course space offers:

**Pre-built Units for each chapter**, curated by experienced educators, with media for each chapter organized and ready to assign or customize to suit your course.**All online resources for the text in one location**, including an interactive e-book, LearningCurve adaptive quizzing, Try It Now exercises, StatTutor, video tech manuals, statistical applets, CrunchIt! statistical software, EESEE case studies, lecture videos, and optional sections.**Intuitive and useful analytics**, with a Gradebook that lets you see how your class is doing individually and as a whole.**A streamlined interface**that lets instructors build an entire course in minutes.

**Kokoska’s unique Solution Trail framework in the text margin serves as a unique guide for approaching and solving the problems. To allow students to put this guidance to use, exercise sets now feature questions that ask students to create their own Solution Trails.**

Revised Solution Trail Exercises

Revised Solution Trail Exercises

Approximately 40% New and Updated Exercises and Examples

Approximately 40% New and Updated Exercises and Examples

**Each exercise set opens with true/false, fill-in-the-blank, and short-answer questions that help students solidify their understanding of essential statistical concepts.**

New Concept Check Exercises

New Concept Check Exercises

**This introductory chapter eases students into the course and Kokoska’s approach. It includes about a dozen exercises that teachers can assign for first day of class, helping students settle into the course more easily.**

New Chapter 0

New Chapter 0

**"Looking Back" recaps key concepts learned in prior chapters.**

Revised Chapter Openers to include "Looking Forward/Looking Back"

Revised Chapter Openers to include "Looking Forward/Looking Back"

**"Looking Forward"**lists the key learning objectives covered in the chapter.

**New "Last Step" Exercises Based on the Chapter Opening Question**

The chapter-opening question is presented again as an exercise at the end of the chapter, to close the concept and application loop, as a last step.

**Most examples include a reference to a related exercise in the end-of-chapter set. With this, students can test their understanding of the examples’ concepts and techniques immediately.**

Try It Now References

Try It Now References

**In addition to Excel, Minitab, and TI-84C, the new edition incorporates output screens from Freeman’s CrunchIt!, as well as from JMP, in the narrative and examples (both software packages are available free of charge in LaunchPad).**

More Statistical Technology Integration

More Statistical Technology Integration

**
Introductory Statistics**

Second Edition| ©2015

Stephen Kokoska

# Digital Options

## LaunchPad

Get the e-book, do assignments, take quizzes, prepare for exams and more, to help you achieve success in class.

Learn About LaunchPad Schedule LaunchPad Demo Go to LaunchPad

## Sapling Learning

Do your homework online, with immediate feedback for optimum learning.

**Introductory Statistics**

Second Edition| 2015

Stephen Kokoska

## Table of Contents

**0. Why Study Statistics**

The Science of Intuition

The Statistical Inference Procedure

Problem Solving

With a Little Help From Technology

**1. An Introduction to Statistics and Statistical Inference**

1.1 Statistics Today

1.2 Populations, Samples, Probability, and Statistics

1.3 Experiments and Random Samples

**2. Tables and Graphs for Summarizing Data**

2.1 Types of Data

2.2 Bar Charts and Pie Charts

2.3 Stem-and-Leaf Plots

2.4 Frequency Distributions and Histograms

**3. Numerical Summary Measures**

3.1 Measures of Central Tendency

3.2 Measures of Variability

3.3 The Empirical Rule and Measures of Relative Standing

3.4 Five-Number Summary and Box Plots

**4 Probability**

4.1 Experiments, Sample Spaces, and Events

4.2 An Introduction to Probability

4.3 Counting Techniques

4.4 Conditional Probability

4.5 Independence

**5. Random Variables and Discrete Probability Distributions**

5.1 Random Variables

5.2 Probability Distributions for Discrete Random Variables

5.3 Mean, Variance, and Standard Deviation for a Discrete Random Variable

5.4 The Binomial Distribution

5.5 Other Discrete Distributions

**6. Continuous Probability Distributions**

6.1 Probability Distributions for Continuous Random Variables

6.2 The Normal Distribution

6.3 Checking the Normality Assumption

6.4 The Exponential Distribution

**7. Sampling Distributions**

7.1 Statistics, Parameters, and Sampling Distributions

7.2 The Sampling Distribution of the Sample Mean and the Central

Limit Theorem

7.3 The Distribution of the Sample Proportion

**8. Confidence Intervals Based on a Single Sample**

8.1 Point Estimation

8.2 A Confidence Interval for a Population Mean when s Is Known

8.3 A Confidence Interval for a Population Mean when s Is Unknown

8.4 A Large-Sample Confidence Interval for a Population Proportion

8.5 A Confidence Interval for a Population Variance

**9. Hypothesis Tests Based on a Single Sample**

9.1 The Parts of a Hypothesis Test and Choosing the Alternative Hypothesis

9.2 Hypothesis Test Errors

9.3 Hypothesis Tests Concerning a Population Mean when s Is Known

9.4 p Values

9.5 Hypothesis Tests Concerning a Population Mean when s Is Unknown

9.6 Large-Sample Hypothesis Tests Concerning a Population Proportion

9.7 Hypothesis Tests Concerning a Population Variance or Standard Deviation

**10. Confidence Intervals and Hypothesis Tests Based on Two Samples or Treatments**

10.1 Comparing Two Population Means Using Independent Samples when Population Variances Are Known

10.2 Comparing Two Population Means Using Independent Samples from Normal Populations

10.3 Paired Data

10.4 Comparing Two Population Proportions Using Large Samples

10.5 Comparing Two Population Variances or Standard Deviations

**11. The Analysis of Variance**

11.1 One-Way ANOVA

11.2 Isolating Differences

11.3 Two-Way ANOVA

**12. Correlation and Linear Regression**

12.1 Simple Linear Regression

12.2 Hypothesis Tests and Correlation

12.3 Inferences Concerning the Mean Value and an Observed Value of Y for x 5 x*

12.4 Regression Diagnostics

12.5 Multiple Linear Regression

**13. Categorical Data and Frequency Tables**

13.1 Univariate Categorical Data, Goodness-of-Fit Tests

13.2 Bivariate Categorical Data, Tests for Homogeneity and Independence

**14. Nonparametric Statistics**

14.1 The Sign Test

14.2 The Signed-Rank Test

14.3 The Rank-Sum Test

14.4 The Kruskal–Wallis Test

14.5 The Runs Test

14.6 Spearman’s Rank Correlation

Notes and Data Sources

Tables Appendix

Answers to Odd-Numbered Exercises

Index

Optional Online Sections

Section 6.5 The Normal Approximation to the Binomial Distribution

Section 12.6 The Polynomial and Qualitative Predictor Models

Section 12.7 Model Selection Procedures

## Authors

### Stephen Kokoska

**Steve Kokoska** received his undergraduate degree from Boston College, and his M.S and Ph.D. from the University of New Hampshire. His initial research interests included the statistical analysis of cancer chemoprevention experiments. He has published a number of research papers in mathematics journals, including: Biometrics, Anticancer Research, and Computer Methods and Programs in Biomedicine. He has also presented results at national conferences, written several books, and been awarded grants from the National Science Foundation, the Center for Rural Pennsylvania, and the Ben Franklin Program.

Steve is a long-time consultant for the College Board and conducted workshops in Brazil, the Dominican Republic, and China. He was the AP Calculus Chief Reader for four years, and has been involved with calculus reform and the use of technology in the classroom. He has been teaching at Bloomsburg University for 25years and recently served as Director of the Honors Program.

Steve has been teaching introductory statistics classes throughout his academic career, and there is no doubt that this is his favorite course. This class (and text) provides students with basic, life-long, quantitative skills that they will use in almost any job and teaches them how to think and reason logically. Steve believes very strongly in data-driven decisions and conceptual understanding through problem solving.

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**Introductory Statistics**

Second Edition| 2015

Stephen Kokoska

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