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Introduction to Statistics and Data Analysis 4th Edition

Roxy Peck, Chris Olsen, Jay L. Devore

  • Published
  • Previous Editions 2008, 2005, 2005
  • 944 Pages
Starting At 90.00 See pricing and ISBN options

Overview

Roxy Peck, Chris Olsen, and Jay Devore's new edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including the frequent substitution of words for symbols--helps students grasp concepts and cement their comprehension. Hands-on activities and interactive applets allow students to practice statistics firsthand. INTRODUCTION TO STATISTICS AND DATA ANALYSIS, 4th Edition, includes updated coverage of the graphing calculator as well as expanded coverage of probability.

Roxy Peck, California Polytechnic State University, San Luis Obispo

Roxy Peck is Emerita Associate Dean 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.

Chris Olsen, Grinnell College

Chris Olsen taught statistics at George Washington High School in Cedar Rapids, Iowa, for over 25 years and currently teaches at Grinnell College. Chris is a past member (twice) of the AP Statistics Test Development Committee and has been a table leader at the AP Statistics reading for 12 years. He is a long-time consultant to the College Board and has led workshops and institutes for AP Statistics teachers in the United States and internationally. Chris was the Iowa recipient of the Presidential Award for Excellence in Science and Mathematics Teaching in 1986, a regional awardee of the IBM Computer Teacher of the Year in 1988, and received the Siemens Award for Advanced Placement in mathematics in 1999. Chris is a frequent contributor to and is moderator of the AP Teacher Community online. He is currently a member of the editorial board of “Teaching Statistics.” Chris graduated from Iowa State University with a major in mathematics and philosophy. While acquiring graduate degrees at the University of Iowa, he concentrated on statistics, computer programming, and psychometrics. In his spare time he enjoys reading and hiking. He and his wife have a daughter, Anna, a Caltech graduate in Civil Engineering. Her field of expertise is quantification of uncertainty in seismic risk.

Jay L. Devore, California Polytechnic State University, San Luis Obispo

Jay Devore is Professor Emeritus of Statistics at California Polytechnic State University. He earned his undergraduate degree in Engineering Science from the University of California at Berkeley, spent a year at the University of Sheffield in England, and finished his Ph.D. in statistics at Stanford University. Jay previously taught at the University of Florida and at Oberlin College and has had visiting appointments at Stanford, Harvard, the University of Washington, New York University, and Columbia University. From 1998 to 2006, he served as Chair of the Cal Poly Statistics Department. In addition to this book, Jay has written several other widely used statistics texts for engineers and scientists and a book in applied mathematical statistics. He recently coauthored a text in probability and stochastic processes. He is the recipient of a distinguished teaching award from Cal Poly, is a Fellow of the American Statistical Association, and has served several terms as an Associate Editor of the “Journal of the American Statistical Association.” In his spare time, he enjoys reading, cooking and eating good food, tennis, and travel to faraway places. He is especially proud of his wife, Carol, a retired elementary school teacher, his daughter Allison, who has held several high-level positions in nonprofit organizations in Boston and New York City, and his daughter Teresa, a high school teacher in Brooklyn.
  • New Cumulative Review Exercises appear after selected chapter sets, allowing you to test students' comprehension of topics spanning multiple chapters.
  • More than 50 new examples and more than 270 new exercises, which use data from current newspapers and journals, help students understand statistical concepts in a realistic context. In addition, more of the exercises specifically ask students to write (for example, by requiring them to explain their reasoning, interpret results, and comment on important features of an analysis).
  • Examples and exercises using data sets that can be accessed online from the text website (Statistics CourseMate) are designated by an icon, as are examples that are further illustrated in technology manuals for MINITAB®, SPSS®, etc.--which are also available at the website.
  • Exercises have been added to the "Interpreting and Communicating the Results of Statistical Analyses" sections, giving students the chance to practice these important skills. All of these sections now have assignable end-of-section questions associated with them.
  • Approximately 90 exercises have video solutions, presented by Brian Kotz of Montgomery College, which can be viewed online or downloaded for viewing later. An icon designates these exercises in the text.
  • A greater number of end-of-chapter activities have been added to the book, ideal for use as a chapter capstone or integrated at appropriate places as the chapter material is covered in class.
  • The "Graphing Calculator Explorations" have been significantly revised and updated.
  • Optional material on logistic regression in Chapter 5, "Summarizing Bivariate Data," assures complete coverage of this important topic.
  • A chapter on Nonparametric Methods is available online, providing you with the option of covering this topic if you wish.
  • "Interpreting and Communicating the Results of Statistical Analysis" sections, which emphasize the importance of being able to interpret statistical output and communicate its meaning to non-statisticians, now have assignable end-of-section questions associated with them.
  • Real data gives students authentic scenarios that help them understand statistical concepts in relevant, interesting contexts.
  • The book features broad coverage of sampling, survey design and experimental design coverage of transformations and nonlinear regression, and an emphasis on graphical display as a necessary component of data analysis.
  • The book highlights the role of the computer in contemporary statistics through numerous printouts and exercises that can be solved by computer.
  • Several Java™ applets, used in conjunction with activities that appear at the end of the chapter, provide visual insight into statistical concepts.
  • A digital image bank and Microsoft® PowerPoint® slides in the Instructor's Resource Binder make lecture and class preparation quick and easy.
  • The text is enhanced by a variety of online teaching and learning resources. These include the book's website, Statistics CourseMate; Aplia™, an online interactive learning solution that improves comprehension of key statistical concepts; and Cengage Learning's Enhanced WebAssign®, which allows you to assign problems from the text online and ensure that students receive multimedia tutorial support as they complete their assignments.
  • Most chapters contain "Graphing Calculator Explorations." Found at the end of the chapter, these explorations promote statistical thinking and allow students to actively experience technology.
1. THE ROLE OF STATISTICS AND THE DATA ANALYSIS PROCESS.
Why Study Statistics. The Nature and Role of Variability. Statistics and the Data Analysis Process. Types of Data and Some Simple Graphical Displays.
2. COLLECTING DATA SENSIBLY.
Statistical Studies: Observation and Experimentation. Sampling. Simple Comparative Experiments. More on Experimental Design. More on Observational Studies: Designing Surveys (Optional). Interpreting and Communicating the Results of Statistical Analyses.
3. GRAPHICAL METHODS FOR DESCRIBING DATA.
Displaying Categorical Data: Comparative Bar Charts and Pie Charts. Displaying Numerical Data: Stem-and-Leaf Displays. Displaying Numerical Data: Frequency Distributions and Histograms. Displaying Bivariate Numerical Data. Interpreting and Communicating the Results of Statistical Analyses.
4. NUMERICAL METHODS FOR DESCRIBING DATA.
Describing the Center of a Data Set. Describing Variability in a Data Set. Summarizing a Data Set: Boxplots. Interpreting Center and Variability: Chebyshev''s Rule, the Empirical Rule, and z Scores. Interpreting and Communicating the Results of Statistical Analyses.
5. SUMMARIZING BIVARIATE DATA.
Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Nonlinear Relationships and Transformations. Logistic Regression (Optional). Interpreting and Communicating the Results of Statistical Analyses.
6. PROBABILITY.
Chance Experiments and Events. Definition of Probability. Basic Properties of Probability. Conditional Probability. Independence. Some General Probability Rules. Estimating Probabilities Empirically Using Simulation.
7. RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS.
Random Variables. Probability Distributions for Discrete Random Variables. Probability Distributions for Continuous Random Variables. Mean and Standard Deviation of a Random Variable. Binomial and Geometric Distributions. Normal Distributions. Checking for Normality and Normalizing Transformations. Using the Normal Distribution to Approximate a Discrete Distribution.
8. SAMPLING VARIABILITY AND SAMPLING DISTRIBUTION.
Statistics and Sampling Variability. The Sampling Distribution of a Sample Mean. The Sampling Distribution of a Sample Proportion.
9. ESTIMATION USING A SINGLE SAMPLE.
Point Estimation. Large-Sample Confidence Interval for a Population Proportion. Confidence Interval for a Population Mean. Interpreting and Communicating the Results of Statistical Analyses.
10. HYPOTHESIS TESTING USING A SINGLE SAMPLE.
Hypotheses and Test Procedures. Errors in Hypotheses Testing. Large-Sample Hypothesis Tests for a Population Proportion. Hypotheses Tests for a Population Mean. Power and Probability of Type II Error. Interpreting and Communicating the Results of Statistical Analyses.
11. COMPARING TWO POPULATIONS OR TREATMENTS.
Inferences Concerning the Difference Between Two Population or Treatment Means Using Independent Samples. Inferences Concerning the Difference Between Two Population or Treatment Means Using Paired Samples. Large Sample Inferences Concerning a Difference Between Two Population or Treatment Proportions. Interpreting and Communicating the Results of Statistical Analyses.
12. THE ANALYSIS OF CATEGORICAL DATA AND GOODNESS-OF-FIT TESTS.
Chi-Square Tests for Univariate Data. Tests for Homogeneity and Independence in a Two-way Table. Interpreting and Communicating the Results of Statistical Analyses.
13. SIMPLE LINEAR REGRESSION AND CORRELATION: INFERENTIAL METHODS.
Simple Linear Regression Model. Inferences About the Slope of the Population Regression Line. Checking Model Adequacy. Inferences Based on the Estimated Regression Line (Optional). Inferences About the Population Correlation Coefficient (Optional). Interpreting and Communicating the Results of Statistical Analyses.
14. MULTIPLE REGRESSION ANALYSIS.
Multiple Regression Models. Fitting a Model and Assessing Its Utility. Inferences Based on an Estimated Model (online). Other Issues in Multiple Regression (online). Interpreting and Communicating the Results of Statistical Analyses (online).
Activity 14.1: Exploring the Relationship Between Number of Predictors and Sample Size.
15. ANALYSIS OF VARIANCE.
Single-Factor ANOVA and the F Test. Multiple Comparisons. The F Test for a Randomized Block Experiment (online). Two-Factor ANOVA (online). Interpreting and Communicating the Results of Statistical Analyses (online).
16. NONPARAMETRIC (DISTRIBUTION-FREE STATISTICAL METHODS (ONLINE).
Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Independent Samples (Optional). Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Paired Samples. Distribution-Free ANOVA.
WebAssign
Each WebAssign online product offers the full textbook combined with a flexible and fully customizable online instructional assignments and tools.

Cengage provides a seamless user experience for Learning Management Systems (LMS) integration. Please contact your Cengage Learning Consultant for ordering information and visit our Learning Management System Integration web page on WebAssign for general information.

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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.

FOR INSTRUCTORS

Online Complete Solutions Manual

ISBN: 9781305265813
Available on the Instructor Companion Site, this manual contains solutions to all exercises from the text, including Chapter Review Exercises and Chapter Tests.

Teacher's Companion Web Site Guide

ISBN: 9781305268913
Everything you need for your course in one place! This collection of book-specific lecture and class tools is available online via www.cengage.com/login. Access and download PowerPoint presentations, images, instructor’s manual, and more.

Cengage Learning Testing, powered by Cognero Instant Access

ISBN: 9781305265899
Cengage Learning Testing Powered by Cognero is a flexible, online system that allows you to author, edit, and manage test bank content, create multiple test versions in an instant, and deliver tests from your LMS, your classroom or wherever you want. This is available online via your SSO account at login.cengage.com.

Student Solutions Manual for Peck/Olsen/Devore’s An Introduction to Statistics and Data Analysis, 5th

ISBN: 9781305265820
Containing fully worked-out solutions to all of the odd-numbered exercises in the text, this manual gives you a way to check your answers and ensure that you have taken the correct steps to arrive at an answer.

FOR STUDENTS

Student Solutions Manual for Peck/Olsen/Devore’s An Introduction to Statistics and Data Analysis, 5th

ISBN: 9781305265820
Containing fully worked-out solutions to all of the odd-numbered exercises in the text, this manual gives you a way to check your answers and ensure that you have taken the correct steps to arrive at an answer.