Statistics: The Exploration and Analysis of Data / Edition 6

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Overview

This book introduces you to the study of statistics and data analysis by using real data and attention-grabbing examples. The authors guide you through an intuition-based learning process that stresses interpretation and communication of statistical information. They help you grasp concepts and cement your comprehension by using simple notation-frequently substituting words for symbols.

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Editorial Reviews

Booknews
This textbook for an introductory statistics course covers collecting data and descriptive methods, probability, basic one- and two-sample inferential techniques, and more advanced inferential methodology. The fourth edition adds a chapter on sampling and experimental design. The CD-ROM contains data. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780495390879
  • Publisher: Cengage Learning
  • Publication date: 7/18/2007
  • Series: Available 2010 Titles Enhanced Web Assign Series
  • Edition description: Older Edition
  • Edition number: 6
  • Pages: 768
  • Product dimensions: 8.60 (w) x 10.10 (h) x 1.20 (d)

Meet the Author

JAY DEVORE received a B.S. in Engineering Science from UC Berkeley and a Ph.D. in Statistics from Stanford University. He previously taught at the University of Florida and Oberlin College, and has had visiting positions at Stanford, Harvard, the University of Washington, and New York University. He has been at California Polytechnic State University, San Luis Obispo since 1977, where he is currently Professor and Chair of the Department of Statistics. He is a Fellow of the American Statistical Association, an Associate Editor for the Journal of the American Statistical Association, and received the Distinguished Teaching Award from Cal Poly in 1991. His recreational interests include reading, playing tennis, traveling, and cooking and eating good food.

Roxy Peck is Associate Dean of the College of Science and Mathematics and Professor of Statistics at California Polytechnic State University, San Luis Obispo. Roxy has been on the faculty at Cal Poly since 1979, serving for six years as Chair of the Statistics Department prior to becoming Associate Dean. 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 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 has recently completed five years as the Chief Reader for the AP Statistics Exam, and currently chairs the American Statistical Association's Joint Committee with theNational Council of Teachers of Mathematics on Curriculum in Statistics and Probability for Grades K-12. In addition to being co-editor of STATISTICAL CASE STUDIES: A COLLABORATION BETWEEN ACADEME AND INDUSTRY, Dr. Peck is the co-author of STATISTICS: THE EXPLORATION AND ANALYSIS OF DATA, Fifth Edition and INTRODUCTION TO STATISTICS AND DATA ANALYSIS, Second Edition. Outside the classroom and the office, Dr. Peck likes to travel and spends her spare time reading mystery novels. She also collects Navajo rugs, and heads to New Mexico whenever she can find the time.

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Table of Contents

1. THE ROLE OF STATISTICS AND THE DATA ANALYSIS PROCESS. 1.1 Three Reasons to Study Statistics. 1.2 The Nature and Role of Variability. 1.3 Statistics and the Data Analysis Process. 1.4 Types of Data and Some Simple Graphical Displays. 2. COLLECTING DATA SENSIBLY. 2.1 Statistical Studies: Observation and Experimentation. 2.2 Sampling. 2.3 Simple Comparative Experiments. 2.4 More Experimental Design. 2.5 More on Observational Studies: Designing Surveys. 2.6 Interpreting and Communicating the Results of Statistical Analyses. 3. GRAPHICAL METHODS FOR DESCRIBING DATA. 3.1 Displaying Categorical Data: Comparative Bar Charts and Pie Charts. 3.2 Displaying Numerical Data: Stem-and-Leaf Displays. 3.3 Displaying Numerical Data: Frequency Distributions and Histograms. 3.4 Displaying Bivariate Numerical Data. 3.5 Interpreting and Communicating the Results of Statistical Analyses. 4. NUMERICAL METHODS FOR DESCRIBING DATA. 4.1 Describing the Center of a Data Set. 4.2 Describing the Variability in a Data Set. 4.3 Summarizing a Data Set: Boxplots. 4.4 Interpreting Center and Variability: Chebyshev's Rule, the Empirical Rule, and z Scores. 4.5 Interpreting and Communicating the Results of Statistical Analyses. 5. SUMMARIZING BIVARIATE DATA. 5.1 Correlation. 5.2 Linear Regression: Fitting a Line to Bivariate Data. 5.3 Assessing the Fit of a Line. 5.4 Nonlinear Relationship and Transformations. 5.5 Logistic Regression. 5.6 Interpreting and Communicating the Results of Statistical Analyses. 6. PROBABILITY. 6.1 Interpreting Probabilities and Basic Probability Rules. 6.2 Probability as a Basis for Making Decisions. 6.3 Estimating Probabilities Empirically and by Using Simulation. 7. POPULATION DISTRIBUTIONS. 7.1 Describing the Distribution of Values in a Population. 7.2 Population Models for Continuous Numerical Variables. 7.3 Normal Distributions. 7.4 Checking for Normality and Normalizing Transformations. 8. SAMPLING VARIABILITY AND SAMPLING DISTRIBUTIONS. 8.1 Statistics and Sampling Variability. 8.2 The Sampling Distribution of a Sample Mean. 8.3 The Sampling Distribution of a Sample Proportion. 9. ESTIMATION USING A SINGLE SAMPLE. 9.1 Point Estimation. 9.2 Large-Sample Confidence Interval for a Population Proportion. 9.3 Confidence Interval for a Population Mean. 9.4 Interpreting and Communicating the Results of Statistical Analyses. 10. HYPOTHESIS TESTING USING A SINGLE SAMPLE. 10.1 Hypotheses and Test Procedures. 10.2 Errors in Hypothesis Testing. 10.3 Large-Sample Hypothesis Tests for a Population Proportion. 10.4 Hypothesis Test for a Population Mean. 10.5 Power and Probability of Type II Error. 10.6 Interpreting and Communicating the Results of Statistical Analyses. 11. COMPARING TWO POPULATIONS OR TREATMENTS. 11.1 Inferences Concerning the Difference Between Two Population or Treatment Means Using Independent Samples. 11.2 Inferences Concerning the Difference Between Two Population or Treatment Means Using Paired Samples. 11.3 Large Sample Inferences Concerning a Difference Between Two Population or Treatment Proportions. 11.4 Interpreting and Communicating the Results of Statistical Analyses. 12. THE ANALYSIS OF CATEGORICAL DATA AND DOOGNESS-OF-FIT TESTS. 12.1 Chi-Square Tests for Univariate Data. 12.2 Tests for Homogeneity and Independence in a Two-way Table. 12.3 Interpreting and Communicating the Results of Statistical Analyses. 13. SIMPLE LINEAR REGRESSION AND CORRELATION INFERENTIAL METHODS. 13.1 Simple Linear Regression Model. 13.2 Inferences About the Slope of the Population Regression Line. 13.3 Checking Model Adequacy. 13.4 Inferences Based on the Estimated Regression Line. 13.5 Inferences About the Population Correlation Coefficient. 13.6 Interpreting and Communicating the Results of Statistical Analyses. 14. MULTIPLE REGRESSION ANALYSIS. 14.1 Multiple Regression Models. 14.2 Fitting a Model and Assessing Its Utility. 14.3 Inferences Based on an Estimated Model. 14.4 Other Issues in Multiple Regression. 14.5 Interpreting and Communicating the Results of Statistical Analyses. 15. ANALYSIS OF VARIANCE. 15.1 Single-factor ANOVA and the F Test. 15.2 Multiple Comparisons. 15.3 The F Test for a Randomized Block Experiment. 15.4 Two-Factor ANOVA. 15.5 Interpreting and Communicating the Results of Statistical Analyses. 16. NONPARAMETRIC (DISTRIBUTION FREE) STATISTICAL METHODS. 16.1 Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Independent Samples. 16.2 Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Paired Samples. 16.3 Distribution-Free ANOVA.

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