Statistics: The Exploration & Analysis of Data, 7th Edition

Roxy Peck, Jay L. Devore

Copyright 2012
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Statistics: The Exploration & Analysis of Data 7th Edition by Roxy Peck/Jay L. Devore

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Overview
Meet the Authors
What's New
Features
Table of Contents
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Overview

Roxy Peck and Jay Devore's STATISTICS: THE EXPLORATION AND ANALYSIS OF DATA, 7th 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.

Meet the Authors

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Roxy Peck

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. As a faculty member at Cal Poly from 1979 until 2009, Dr. Peck 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. Dr. Peck 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. Dr. Peck 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, Dr. Peck is co-editor of STATISTICAL CASE STUDIES: A COLLABORATION BETWEEN ACADEME AND INDUSTRY and is a member of the editorial board for STATISTICS: A GUIDE TO THE UNKNOWN, 4TH EDITION. Outside of the classroom, she likes to travel and spends her spare time reading mystery novels. Dr. Peck also collects Navajo rugs and travels to Arizona and New Mexico whenever she can find the time.

Jay L. Devore

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.

What's New

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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 students 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.
More than 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 text is enhanced by a variety of online teaching and learning resources. These include the book's website, Statistics CourseMate, and Cengage's WebAssign, which allows you to assign problems from the text online and ensure that students receive multimedia tutorial support as they complete their assignments.

Features

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An optional section on logistic regression is included in Chapter 5, "Summarizing Bivariate Data," for those who would like more complete coverage of data analysis techniques for categorical data.
Advanced topics that are often omitted in a one-quarter or one-semester course, such as inference and variable selection methods in multiple regression and analysis of variance for randomized block and two-factor designs, are available online at the book's website, Statistics CourseMate.
"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.

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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.
Interpreting Probabilities and Basic Probability Rules. Probability as a Basis for Making Decisions. Estimating Probabilities Empirically and by Using Simulation.
7. RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS.
Describing the Distribution of Values in a Population. Population Models for Continuous Numerical Variables. Normal Distributions. Checking for Normality and Normalizing Transformations.
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).
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.