Statistics,
3rd Edition

Section I: COLLECTING DATA.
1. Collecting Data in Reasonable Ways.
Statistics: It’s All About Variability. Statistical Studies: Observation and Experimentation. Collecting Data: Planning an Observational Study. Collecting Data: Planning an Experiment. The Importance of Random Selection and Random Assignment: What Types of Conclusions Are Reasonable? Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
Section II: DESCRIBING DATA DISTRIBUTIONS.
2. Graphical Methods for Describing Data Distributions.
Selecting an Appropriate Graphical Display. Displaying Categorical Data: Bar Charts and Comparative Bar Charts. Displaying Numerical Data: Dotplots, Stem-and-Leaf Displays, and Histograms. Displaying Bivariate Numerical Data: Scatterplots and Time-Series Plots. Graphical Displays in the Media. Bivariate and Multivariable Graphical Displays. Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
3. Numerical Methods for Describing Data Distributions.
Selecting Appropriate Numerical Summaries. Describing Center and Variability for Data Distributions that are Approximately Symmetric. Describing Center and Variability for Data Distributions that are Skewed or Have Outliers. Summarizing a Data Set: Boxplots. Measures of Relative Standing: z-scores and Percentiles. Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
4. Describing Bivariate Numerical Data.
Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Describing Linear Relationships and Making Predictions--Putting it all Together. Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking. Bonus Material on Logistic Regression (Online).
Section III: A FOUNDATION FOR INFERENCE: REASONING ABOUT PROBABILITY.
5. Probability.
Interpreting Probabilities. Computing Probabilities. Probabilities of More Complex Events: Unions, Intersections and Complements. Conditional Probability. Calculating Probabilities -- A More Formal Approach. Probability as a Basis for Making Decisions. Estimating Probabilities Empirically and Using Simulation (Optional). Chapter Activities.
6. Random Variables and Probability Distributions.
Random Variables. Probability Distributions for Discrete Random Variables. Probability Distributions for Continuous Random Variables. The Mean and Standard Deviation of a Random Variable. Normal Distribution. Checking for Normality. Binomial and Geometric Distributions (Optional). Using the Normal Distribution to Approximate a Discrete Distribution (Optional). Chapter Activities. Bonus Material on Counting Rules, The Poisson Distribution (Online).
Section IV: LEARNING FROM SAMPLE DATA.
7. An Overview of Statistical Inference -- Learning from Data.
Statistical Inference -- What You Can Learn from Data. Selecting an Appropriate Method -- Four Key Questions. A Five-Step Process for Statistical Inference.
8. Sampling Variability and Sampling Distributions.
Statistics and Sampling Variability. The Sampling Distribution of a Sample Proportion. How Sampling Distributions Support Learning from Data. Chapter Activities.
9. Estimating a Population Proportion.
Selecting an Estimator. Estimating a Population Proportion -- Margin of Error. A Large Sample Confidence Interval for a Population Proportion. Choosing a Sample Size to Achieve a Desired Margin of Error. Bootstrap Confidence Intervals for a Population Proportion (Optional). Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
10. Asking and Answering Questions about a Population Proportion.
Hypotheses and Possible Conclusions. Potential Errors in Hypothesis Testing. The Logic of Hypothesis Testing -- An Informal Example. A Procedure for Carrying Out a Hypothesis Test. Large-Sample Hypothesis Tests for a Population Proportion. Randomization Tests and Exact Binomial Tests for One Proportion (Optional). Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
11. Asking and Answering Questions about the Difference between Two Population Proportions.
Estimating the Difference between Two Population Proportions. Testing Hypotheses about the Difference between Two Population Proportions. Inference for Two Proportions Using Data from an Experiment. Simulation-Based Inference for Two Proportions (Optional). Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
12. Asking and Answering Questions about a Population Mean.
The Sampling Distribution of the Sample Mean. A Confidence Interval for a Population Mean. Testing Hypotheses about a Population Mean. Simulation-Based Inference for One Mean (Optional). Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
13. Asking and Answering Questions about the Difference between Two Population Means.
Two Samples: Paired versus Independent Samples. Learning About a Difference in Population Means Using Paired Samples. Learning About a Difference in Population Means Using Independent Samples. Inference for Two Means Using Data from an Experiment. Simulation-Based Inference for Two Means (Optional). Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
Section V: ADDITIONAL OPPORTUNITIES TO LEARN FROM DATA.
14. Learning from Categorical Data.
Chi-Square Tests for Univariate Categorical Data. Tests for Homogeneity and Independence in a Two-Way Table. Avoid These Common Mistakes. Chapter Activities.
15. Understanding Relationships--Numerical Data Part 2 (Online).
The Simple Linear Regression Model. Inferences Concerning the Slope of the Population Regression Line. Checking Model Adequacy.
16. Asking and Answering Questions about More Than Two Means (Online).
The Analysis of Variance -- Single-Factor ANOVA and the F Test. Multiple Comparisons.
Appendix: ANOVA Computations.

• Roxy Peck

  Roxy Peck is Associate Dean Emerita of the College of Science and Mathematics, and Professor of Statistics Emerita at California Polytechnic State University, San Luis Obispo. A faculty member at Cal Poly from 1979 until 2009, Roxy served for six years as Chair of the Statistics Department before becoming Associate Dean, a position she held for 13 years. She received an M.S. in Mathematics and a Ph.D. in Applied Statistics from the University of California, Riverside. Roxy is nationally known in the area of statistics education, and she was presented with the Lifetime Achievement Award in Statistics Education at the U.S. Conference on Teaching Statistics in 2009. In 2003, she received the American Statistical Association’s Founder’s Award, recognizing her contributions to K–12 and undergraduate statistics education. She is a Fellow of the American Statistical Association and an elected member of the International Statistics Institute. Roxy served for five years as the Chief Reader for the Advanced Placement (AP) Statistics Exam and has chaired the American Statistical Association’s Joint Committee with the National Council of Teachers of Mathematics on Curriculum in Statistics and Probability for Grades K–12 and the Section on Statistics Education. In addition to her texts in introductory statistics, Roxy is also co-editor of “Statistical Case Studies: A Collaboration Between Academe and Industry” and a member of the editorial board for “Statistics: A Guide to the Unknown, 4th Edition.” Outside the classroom, Roxy likes to travel and spends her spare time reading mystery novels. She also collects Navajo rugs and heads to Arizona and New Mexico whenever she can find the time.

• Catherine Case

  Catherine Case is a senior lecturer in the statistics department at the University of Georgia. She has experience teaching classes of different sizes (from 6 to 240). She has also taught at levels ranging from high school to graduate school and has taught for many different audiences. She has received multiple awards as an instructor. Dr. Case serves as the editor of Statistics Education Web (STEW), a free online resource that provides peer-reviewed lesson plans for teachers at both the K-12 and the college level. She regularly presents at conferences and workshops and has published several articles about students’ understanding of inference. Dr. Case was also a contributor to the NSF Levels of Conceptual Understanding in Statistics (LOCUS) assessment project and a writer for the ASA Statistical Education of Teachers (SET) document. She enjoys cooking, playing the mandolin and drinking way too much coffee on her screened porch. Her favorite days are the ones she spends with her husband Adam, her son Isaac and her dog Gracie.

• NEW! "STOP AND THINK" QUESTIONS CHALLENGE STUDENTS TO STRATEGICALLY CONSIDER SOLUTIONS. Providing opportunities for active learning and statistical thinking, "Stop and Think" questions encourage students to pause and make their own predictions before reading full explanations in the text.

• NEW! "CHECK YOUR UNDERSTANDING" QUESTIONS ENSURE STUDENTS COMPREHEND CONCEPTS. These end-of-section questions confirm students’ understanding of important concepts before advancing to the next section.

• NEW! STATISTICAL ANALYSIS AND LEARNING TOOL (SALT) PROVIDES AN EASY-TO-USE DATA ANALYSIS RESOURCE. SALT enables introductory statistics students to engage in data manipulation, analysis, and interpretation without getting bogged down in complex computations. Accompanying exercises in WebAssign utilize SALT

• NEW! UPDATED EXAMPLES AND EXERCISES EMPHASIZE RELEVANT TOPICS. Updated examples and exercises include data drawn from recent journal articles, newspaper headlines and online sources.

• NEW SECTION (CH. 2) DISCUSSES BIVARIATE AND MULTIVARIATE GRAPHICAL DISPLAYS. This new coverage helps students learn how to translate graphs that are more complex than traditional histograms, boxplots or bar charts, including those with information on more than one or two variables.

• ORGANIZATION REFLECTS THE DATA ANALYSIS PROCESS. Students first learn how data analysis is a process that begins with careful planning. They then learn about data collection, data description using graphical and numerical summaries, data analysis and interpretation of results.

• STUDENTS WORK WITH REAL, ENGAGING DATA. The exercises and examples involve real data from a wide range of areas. Chapter activities offer mini-projects in which students explore important ideas and concepts in more depth through authentic applications of the statistical process.

• PROVIDES AN ACCESSIBLE APPROACH TO PROBABILITY. The approach uses natural frequencies to reason about probability which research shows is much easier for students to understand – especially with conditional probability. An optional section introduces probability rules for those who also want more traditional coverage.

• BRIEF CHAPTER (CH. 7) PROVIDES AN OVERVIEW OF STATISTICAL INFERENCE. This short chapter focuses on what students need to consider when selecting an appropriate method of analysis.

• DISCUSSES INFERENCE FOR PROPORTIONS BEFORE INFERENCE FOR MEANS. The authors develop the concept of a sampling distribution via simulation. Inferential procedures for proportions are based on the normal distribution, so students can focus on new concepts of estimation and hypothesis testing without grappling with the introduction of the t distribution.

• HANDS-ON CHAPTER ACTIVITIES ENGAGE STUDENTS. A growing body of evidence indicates that students learn best when they are actively engaged. Chapter activities offer mini-projects that allow students to explore important ideas and concepts in more depth through authentic applications of the statistical process.

• BRIEF CHAPTER (CH. 7) PROVIDES AN OVERVIEW OF STATISTICAL INFERENCE. This short chapter focuses on what students need to consider in order to select an appropriate method of analysis. In most texts, this is often hidden in the discussion that occurs when a new method is introduced. In contrast, this text clearly discusses four key questions that must be answered before choosing an inference method. Students develop a general framework for inference, making it easier to make correct choices.

• DISCUSSES INFERENCE FOR PROPORTIONS BEFORE INFERENCE FOR MEANS. This title develops the concept of a sampling distribution via simulation. Simulation is simpler in the context of proportions, where it is easy to construct a hypothetical population (versus the more complicated context of means, which requires assumptions about shape and spread). In addition, inferential procedures for proportions are based on the normal distribution, so students can focus on new concepts of estimation and hypothesis testing without having to grapple with the introduction of the t distribution.

• ARE YOU READY TO MOVE ON?" FEATURES HELP STUDENTS TEST THEIR UNDERSTANDING. These "Are You Ready to Move On?" questions at the end of each chapter help students gauge whether they have mastered that chapter's learning objectives before advancing to the next chapter. Like the problem sets for each section, this collection of exercises is developmental -- assessing all of the chapter learning outcomes and serving as a comprehensive end-of-chapter review.

• MOST CHAPTERS CONTAIN EXTENDED REAL-DATA ALGORITHMIC SAMPLING EXERCISES. Students visit this edition's companion website to obtain a random sample of data from a population. Students then use their samples to answer the questions in the text. You can use the varying results as the basis for class discussion. These unique exercises are designed to teach about sampling variability early in the course and to prompt meaningful classroom discussions about this important statistical concept.

• AVAILABLE IN WEBASSIGN WITH COREQUISITE SUPPORT. This edition is also available in WebAssign with Corequisite Support that provides the flexibility to match any corequisite implementation model and delivers extra remediation to students.

Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.

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