STATISTICS: LEARNING FROM DATA, Second Edition, helps you learn to think like a statistician. It pays particular attention to areas that students often struggle with  probability, hypothesis testing, and selecting an appropriate method of analysis. Supported by learning objectives, realdata examples and exercises, and technology notes, this book helps you to develop conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice.
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, StemandLeaf Displays, and Histograms. Displaying Bivariate Numerical Data: Scatterplots and TimeSeries Plots. Graphical Displays in the Media. 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: zscores 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 PredictionsPutting 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 FiveStep Process for Statistical Inference. Chapter Activities.
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. LargeSample 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. SimulationBased 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. SimulationBased 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. SimulationBased 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.
ChiSquare Tests for Univariate Categorical Data. Tests for Homogeneity and Independence in a TwoWay Table. Avoid These Common Mistakes. Chapter Activities.
15. Understanding RelationshipsNumerical 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  SingleFactor 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 coeditor 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.

Tom Short
The late Tom Short was an Associate Professor in the Statistics Program within the Department of Mathematics at West Chester University of Pennsylvania. He also previously held faculty positions at Villanova University, Indiana University of Pennsylvania and John Carroll University. He was a Fellow of the American Statistical Association and received the 2005 Mu Sigma Rho Statistics Education Award. Tom served on the leadership team for readings of the Advanced Placement (AP) Statistics Exam, and on the AP Statistics Development Committee. He also served on the Board of Directors of the American Statistical Association. Tom treasured the time he shared with his four children and the many adventures experienced with his wife, Darlene.

NOTE: This title is also available in WebAssign with Corequisite Support that provides the flexibility to match any corequisite implementation model and empowers you to deliver high quality content at the right time for your students at an affordable price.

RESTRUCTURED CHAPTERS ON STATISTICAL INFERENCE. The chapters on statistical inference have been restructured to integrate methods for learning from experiments with methods for learning from samples. The coverage of inference based on data from statistical experiments (Ch. 14 in the first edition) has been integrated into earlier chapters, but the important distinction between inferences based on data from experiments and inferences based on data from sampling is maintained.

EXPANDED TREATMENT OF PROBABILITY. The second edition contains a new section titled “Calculating Probabilities  A More Formal Approach” for instructors who want to also provide a more traditional coverage of probability. For those who prefer the “hypothetical 1,000” approach from the first edition, the newly added traditional section is optional and can be omitted without compromising any of the probability student learning objectives.

UPDATED EXAMPLES AND EXERCISES. In a continuing effort to keep things interesting and relevant, the second edition contains many updated examples and exercises that use data from recent journal articles, newspapers, and web posts, on topics of interest to students.

NEW for Fall 2020  Turn your students into statistical thinkers with the Statistical Analysis and Learning Tool (SALT). SALT is an easytouse data analysis tool created with the introlevel student in mind. It contains dynamic graphics and allows students to manipulate data sets in order to visualize statistics and gain a deeper conceptual understanding about the meaning behind data. SALT is built by Cengage, comes integrated in Cengage WebAssign Statistics courses and available to use standalone.

NEW SECTIONS ON RANDOMIZATIONBASED INFERENCE METHODS. Research indicates that randomizationbased instruction in statistical inference may help learners to better understand the concepts of confidence and significance. New optional sections on randomizationbased inference methods provide an alternative method of analysis that can be used when the conditions required for normal distributionbased inference are not met.

A NEW APPROACH TO PROBABILITY. Research has shown that using natural frequencies to reason about probability  especially conditional probability  is much easier for students to understand. The text's treatment of probability is complete, including conditional probability and Bayes' Rule probability calculations, but it's done in a way that eliminates the need for the symbolism and formulas, which are roadblocks for many students. A new, optional section that introduces probability rules accommodates those who also want to provide students with a more traditional coverage.

ARE YOU READY TO MOVE ON?  STUDENTS TEST THEIR UNDERSTANDING. Prior to moving to subsequent chapters, "Are You Ready to Move On?" questions at the end of each chapter let students gauge whether they have achieved that chapter's learning objectives. Like the problem sets for each section, this collection of exercises is developmental  assessing all of the chapter learning objectives and serving as a comprehensive endofchapter review.

REALDATA ALGORITHMIC SAMPLING EXERCISES. Most chapters contain extended samplingbased, realdata exercises. Students visit the companion website and each student gets a different random sample of data from a population. Students then use their samples to answer the questions posed, and their varying results can be used as the basis for class discussion. These unique exercises are designed to teach about sampling variability starting early in the course and to provide a vehicle for rich classroom discussions of this important statistical concept.

DATA ANALYSIS SOFTWARE. JMP™ data analysis software can be included with each new textbook at no additional cost when requested by the instructor. Minitab 18® can be included with any textbook or bundle for a small fee.

TECHNOLOGY NOTES at the end of most chapters give students helpful hints and guidance on completing tasks associated with a particular chapter. The following technologies are included in the notes: JMP™, MINITAB®, SPSS®, Microsoft® Excel® 2007, TI83/84, and TINspire. They include display screens to help students visualize and to better understand the steps. More complete technology manuals are also available on the text's website.

HANDSON CHAPTER ACTIVITIES ENGAGE STUDENTS. A growing body of evidence indicates that students learn best when they are actively engaged. Chapter activities guide students' thinking about important ideas and concepts.

CHAPTER ON OVERVIEW OF STATISTICAL INFERENCE (CHAPTER 7). This short chapter focuses on the things students need to think about in order to select an appropriate method of analysis. In most texts, this is "hidden" in the discussion that occurs when a new method is introduced. Considering this up front and structured around four key questions that need to be answered before choosing an inference method helps students to develop a general framework for inference and makes it easier for students to make correct choices.

AN ORGANIZATION THAT REFLECTS THE DATA ANALYSIS PROCESS. Students are introduced early to the idea that data analysis is a process that begins with careful planning, followed by data collection, data description using graphical and numerical summaries, data analysis, and, finally, interpretation of results. The ordering of topics in the textbook mirrors this process: data collection, then data description, then statistical inference.

INFERENCE FOR PROPORTIONS BEFORE INFERENCE FOR MEANS. The book makes it possible to develop 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, allowing students to focus on the new concepts of estimation and hypothesis testing without having to grapple with the introduction of the t distribution.

SEPARATE TREATMENT OF INFERENCE BASED ON EXPERIMENT DATA. Many statistical studies involve collecting data from a statistical experiment. The same inference procedures used to estimate or test hypotheses about population parameters are also used to estimate or test hypotheses about treatment effects. However, the necessary assumptions are slightly different (for example, random assignment replaces the assumption of random selection), as is the wording of conclusions. Treating both cases together tends to confuse students; this text makes the distinction clear.

CHAPTER LEARNING OBJECTIVES  KEEPING STUDENTS INFORMED ABOUT EXPECTATIONS. The learning objectives explicitly state expected student outcomes, and are presented in three categories: Conceptual Understanding, Mastery of Mechanics, and Putting It into Practice.

CHAPTER PREVIEW  MOTIVATION FOR LEARNING. Each chapter opens with a Preview and a Preview Example that provide motivation for studying the concepts and methods introduced in the chapter. They address why the material is worth learning, provide the conceptual foundation for the methods covered in the chapter, and connect to what the student already knows. These relevant and current examples provide a context in which one or more questions are proposed for further investigation. The context is revisited in the chapter once students have the necessary understanding to more fully address the questions posed.

REAL DATA THAT MOTIVATES AND ENGAGES. Examples and exercises with overly simple settings don't allow students to practice interpreting results in real situations. The exercises and examples are a particular strength of this text. Most involve data extracted from journal articles, newspapers, web posts, and other published sources. They cover a wide range of disciplines and subject areas of interest to today's students, including, among others, health and fitness, consumer research, psychology and aging, environmental research, law and criminal justice, and entertainment.

EXERCISES ORGANIZED INTO DEVELOPMENTAL SETS TO STRUCTURE THE OUTOFCLASS EXPERIENCE. Endofsection exercises are presented in two developmental sets. The exercises in each set work together to assess all of the learning objectives for the section. Additional section exercises are included for those who want more practice.
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