# Statistics: Learning from Data, 1st Edition

• includes JMP Printed Access Card
• Roxy Peck
• ISBN-10: 0495553263
• ISBN-13: 9780495553267
• 878 Pages | Hardcover
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# Overview

STATISTICS: LEARNING FROM DATA, by respected and successful author Roxy Peck, resolves common problems faced by both students and instructors with an innovative approach to elementary statistics. Instead of assuming that students will "pick it up along the way," Peck tackles the areas students struggle with most--probability, hypothesis testing, and selecting an appropriate method of analysis--unlike any text on the market. Probability coverage is based on current research that shows how students best learn the subject. Two unique chapters, one on statistical inference and another on learning from experiment data, address two common areas of student confusion: choosing a particular inference method and using inference methods with experimental data. Supported by learning objectives, real-data examples and exercises, and technology notes, this brand new text guides students in gaining conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice.

## Features

• A New Approach to Probability: Research has demonstrated how students develop an understanding of probability and chance. Using natural frequencies to reason about probability, especially conditional probability, is much easier for students to understand. The treatment of probability in this text is complete, including conditional probability and Bayes' Rule type probability calculations. However, it's done in a way that eliminates the need for the symbolism and formulas, which are a roadblock for many students.

• 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, these considerations are "hidden" in the discussion that occurs when a new method is introduced. Discussing these considerations up front in the form of four key questions that need to be answered before choosing an inference method 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 (Chapter 14): Many statistical studies involve collecting data via experimentation. The same inference procedures used to estimate or test hypotheses about population parameters also are 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 the expected student outcomes, and are presented in three categories: Conceptual Understanding, Mastery of Mechanics, and Putting It into Practice.

• Preview--Motivation for Learning: Each chapter opens with a Preview and 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, 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 Out-of-Class Experience: End-of-section 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.

• Are You Ready to Move On?--Students Test Their Understanding: Prior to moving to the next chapter, "Are You Ready to Move On?" questions allow students to confirm that they have achieved the chapter 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 end-of-chapter review.

• Exploring the Big Ideas--Real-Data Algorithmic Sampling Exercises: Most chapters contain extended sampling-based, real-data exercises at the end of the chapter. Each student goes to the companion website *(http://www.xxx.com)* and gets a different random sample of data from a population. The student then uses that sample to answer the questions. These unique exercises are designed to teach about sampling variability and provide a vehicle for rich classroom discussions of this important statistical concept.

• Simple Design: Recent research shows that many of the "features" in current textbooks are not really helpful to students. In fact, cartoons, sidebars, historical notes, fake post-it notes in the margins, etc. actually distract students and interfere with learning. STATISTICS: LEARNING FROM DATA has a simple, clean design that minimizes clutter and maximizes student understanding.

• Data Analysis Software: JMP™ data analysis software is included with each new textbook at no additional charge.

• Technology Notes: 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, TI-83/84, and TI-Nspire. They include display screens to help students visualize and better understand the steps. More complete technology manuals are also available on the text's website.

• Chapter Activities--Engaging Students in Hands-On Activities: There is a growing body of evidence indicating that students learn best when they are actively engaged. Chapter activities guide students' thinking about important ideas and concepts.

Roxy Peck

Learning from Data. Statistics--It's All About Variability. The Data Analysis Process. Goals for Student Learning. The Structure of the Chapters that Follow.
Section I: COLLECTING DATA.
1. Collecting Data in Reasonable Ways.
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?
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.
3. Numerical Methods for Describing Data Distributions.
Selecting Appropriate Numerical Summaries. Describing Center and Spread for Data Distributions that are Approximately Symmetric. Describing Center and Spread for Data Distributions that are Skewed or Have Outliers. Summarizing a Data Set: Boxplots. Measures of Relative Standing: z-scores and Percentiles.
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. 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. Probability as a Basis for Making Decisions. Estimating Probabilities Empirically and Using Simulation (Optional).
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. The Normal Distribution. Checking for Normality. The Binomial and Geometric Distributions (Optional). Using the Normal Distribution to Approximate a Discrete Distribution (Optional). Counting Rules, The Poisson Distribution (Online).
Section IV: LEARNING FROM SAMPLE DATA.
7. An Overview of Statistical Inference--Learning from Data.
Statistical Inference--What We 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.
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.
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.
Estimating the Difference between Two Population Proportions. Testing Hypotheses about the Difference between Two Population Proportions.
Sampling Distribution of the Sample Mean. A Confidence Interval for a Population Mean. Testing Hypotheses about a Population Mean.
Testing Hypotheses about the Difference between Two Population Means Using Independent Samples. Testing Hypotheses about the Difference between Two Population Means Using Paired Samples. Estimating the Difference between Two Population Means.
Section V: ADDITIONAL OPPORTUNITIES TO LEARN FROM DATA.
14. Learning from Experiment Data.
Variability and Random Assignment. Testing Hypotheses about Differences in Treatment Effects. Estimating a Difference in Treatment Effects.
15. Learning from Categorical Data.
Chi-Square Tests for Univariate Categorical Data. Tests for Homogeneity and Independence in a Two-Way Table.
16. Understanding Relationships--Numerical Data Part 2 (Online).
The Simple Linear Regression Model. Inferences Concerning the Slope of the Population Regression Line. Checking Model Adequacy.
The Analysis of Variance--Single-Factor ANOVA and the F Test. Multiple Comparisons.
Appendix: ANOVA Computations.

# Supplements

All supplements have been updated in coordination with the Main title.
Please see Main title page for new to this edition information.

## Instructor Supplements

Preliminary Edition of Statistics: Learning from Data  (ISBN-10: 1285049365 | ISBN-13: 9781285049366)

STATISTICS: LEARNING FROM DATA, by respected and successful author Roxy Peck, resolves common problems faced by both students and instructors with an innovative approach to elementary statistics. Instead of assuming that students will "pick it up along the way," Peck tackles the areas students struggle with most--probability, hypothesis testing, and selecting an appropriate method of analysis--unlike any text on the market. Probability coverage is based on current research that shows how students best learn the subject. Two unique chapters, one on statistical inference and another on learning from experiment data, address two common areas of student confusion: choosing a particular inference method and using inference methods with experimental data. Supported by learning objectives, real-data examples and exercises, and technology notes, this brand new text guides students in gaining conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice.

Solutions Builder  (ISBN-10: 1285165721 | ISBN-13: 9781285165721)

This online instructor database offers complete worked solutions to all exercises in the text, allowing you to create customized, secure solutions printouts (in PDF format) matched exactly to the problems you assign in class. www.cengage.com/solutionbuilder.

Cengage Testing, powered by Cognero® Instant Access  (ISBN-10: 1285980085 | ISBN-13: 9781285980089)
Student Solutions Manual for Peck's Statistics  (ISBN-10: 1285089839 | ISBN-13: 9781285089836)

Contains fully worked-out solutions to selected exercises in the text, giving students a way to check their answers and ensure that they took the correct steps to arrive at an answer.

CourseMate, 1 term (6 months) Instant Access  (ISBN-10: 1285085728 | ISBN-13: 9781285085722)

Complement your text and course content with study and practice materials. Cengage Learning's Statistics CourseMate brings course concepts to life with interactive learning, study, and exam preparation tools that support the printed textbook. Watch student comprehension soar as your class works with the printed textbook and the textbook-specific website. Statistics CourseMate goes beyond the book to deliver what you need!

MindTap Mathematics, 1 term (6 months) Instant Access  (ISBN-10: 1285840674 | ISBN-13: 9781285840673)

MindTap Mathematics for Peck's Statistics: Learning From Data is the digital learning solution that helps instructors engage and transform today's students into critical thinkers. Through paths of dynamic assignments and applications that you can personalize, real-time course analytics, and an accessible reader, MindTap helps you turn cookie cutter into cutting edge, apathy into engagement, and memorizers into higher-level thinkers.

MindTap Mathematics, 1 term (6 months) Printed Access Card  (ISBN-10: 1285840682 | ISBN-13: 9781285840680)

MindTap Mathematics for Peck's Statistics: Learning From Data is the digital learning solution that helps instructors engage and transform today's students into critical thinkers. Through paths of dynamic assignments and applications that you can personalize, real-time course analytics, and an accessible reader, MindTap helps you turn cookie cutter into cutting edge, apathy into engagement, and memorizers into higher-level thinkers.

Statistics, Loose-leaf Version  (ISBN-10: 1305259688 | ISBN-13: 9781305259683)

STATISTICS: LEARNING FROM DATA, by respected and successful author Roxy Peck, resolves common problems faced by both students and instructors with an innovative approach to elementary statistics. Peck tackles the areas students struggle with most--probability, hypothesis testing, and selecting an appropriate method of analysis--unlike any text on the market. Probability coverage is based on current research that shows how students best learn the subject. Two unique chapters, one on statistical inference and another on learning from experiment data, address two common areas of student confusion: choosing a particular inference method and using inference methods with experimental data. Supported by learning objectives, real-data examples and exercises, and technology notes, this brand new text guides students in gaining conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice.

## Student Supplements

Preliminary Edition of Statistics: Learning from Data  (ISBN-10: 1285049365 | ISBN-13: 9781285049366)

STATISTICS: LEARNING FROM DATA resolves common problems faced by students with an innovative approach that makes it easier for you to learn elementary statistics. Instead of assuming that you will "pick it up along the way," author Roxy Peck tackles the areas that statistics students struggle with most--probability, hypothesis testing, and selecting an appropriate method of analysis. Probability coverage is based on current research that shows how students best learn the subject. Real-data examples and exercises provide a meaningful context for introducing and developing new concepts. With this text as your guide, you'll gain conceptual understanding, mechanical proficiency, and the ability to put your new knowledge into practice. JMP is a statistics software for Windows and Macintosh computers from SAS, the market leader in analytics software and services for industry. JMP Student Edition is a streamlined, easy-to-use version that provides all the statistical analysis and graphics covered in this textbook. JMP provides an interface to explore data visually and interactively, which will help you develop a healthy relationship with data, work more efficiently with data, and tackle difficult statistical problems more easily. Because its output provides both statistics and graphs together, you will better see and understand the application of concepts covered in this book as well. Access to this software is available for free with new copies of the book and available for purchase standalone use at www.jmp.com/getse

Student Solutions Manual for Peck's Statistics  (ISBN-10: 1285089839 | ISBN-13: 9781285089836)

Go beyond the answers--see what it takes to get there and improve your grade! This manual provides worked-out, step-by-step solutions to selected problems in the text. This gives you the information you need to truly understand how these problems are solved.

CourseMate, 1 term (6 months) Instant Access  (ISBN-10: 1285085728 | ISBN-13: 9781285085722)

The more you study, the better the results. Make the most of your study time by accessing everything you need to succeed in one place. Read your textbook, take notes, review flashcards, watch videos, and take practice quizzes--online with CourseMate.

MindTap Mathematics, 1 term (6 months) Instant Access  (ISBN-10: 1285840674 | ISBN-13: 9781285840673)

MindTap Mathematics for Peck's Statistics: Learning From Data provides you with the tools you need to better manage your limited time -- you can complete assignments whenever and wherever you are ready to learn with course material specially customized for you by your instructor and streamlined in one proven, easy-to-use interface. With an array of tools and apps -- from note taking to flashcards -- you'll get a true understanding of course concepts, helping you to achieve better grades and setting the groundwork for your future courses.

MindTap Mathematics, 1 term (6 months) Printed Access Card  (ISBN-10: 1285840682 | ISBN-13: 9781285840680)

MindTap Mathematics for Peck's Statistics: Learning From Data provides you with the tools you need to better manage your limited time -- you can complete assignments whenever and wherever you are ready to learn with course material specially customized for you by your instructor and streamlined in one proven, easy-to-use interface. With an array of tools and apps -- from note taking to flashcards -- you'll get a true understanding of course concepts, helping you to achieve better grades and setting the groundwork for your future courses.

WebAssign, Single-Term Instant Access  (ISBN-10: 1337707163 | ISBN-13: 9781337707169)

WebAssign for Peck's Statistics: Learning from Data, 1st Edition, helps you prepare for class with confidence. Its online learning platform for your math, statistics and science courses helps you practice and absorb what you learn. Videos and tutorials walk you through concepts when you're stuck, and instant feedback and grading let you know where you stand--so you can focus your study time and perform better on in-class assignments. Study smarter with WebAssign!

Statistics, Loose-leaf Version  (ISBN-10: 1305259688 | ISBN-13: 9781305259683)

STATISTICS: LEARNING FROM DATA, by respected and successful author Roxy Peck, resolves common problems faced by both students and instructors with an innovative approach to elementary statistics. Peck tackles the areas students struggle with most--probability, hypothesis testing, and selecting an appropriate method of analysis--unlike any text on the market. Probability coverage is based on current research that shows how students best learn the subject. Two unique chapters, one on statistical inference and another on learning from experiment data, address two common areas of student confusion: choosing a particular inference method and using inference methods with experimental data. Supported by learning objectives, real-data examples and exercises, and technology notes, this brand new text guides students in gaining conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice.