Introduction to Statistics and Data Analysis / Edition 4

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Overview

Chris Olsen has teamed up with Roxy Peck and Jay Devore to create INTRODUCTION TO STATISTICS AND DATA ANALYSIS. Based on STATISTICS: THE EXPLORATION OF ANALYSIS AND DATA, Fourth Edition, this new book integrates the graphing calculator and includes expanded coverage of probability. This innovative book focuses on the analysis of real data to motivate the study of statistics. Traditional in structure yet modern in approach, this book places an increased emphasis on data collection and exploratory analysis.
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Editorial Reviews

Booknews
Emphasizing data collection and exploratory analysis, this book covers different methods for describing data, probability, estimation, linear regression and correlation, and variance. The authors (two from California Polytechnic State U, one from George Washington High School) focus on graphical analysis throughout the text and integrate the graphing calculator into the problems. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780840054906
  • Publisher: Cengage Learning
  • Publication date: 1/1/2011
  • Series: Available Titles Aplia Series
  • Edition number: 4
  • Pages: 944
  • Sales rank: 192,201
  • Product dimensions: 8.50 (w) x 10.90 (h) x 1.40 (d)

Meet the Author


Dr. Roxy Peck has been a professor in the Department of Statistics at California Polytechnic State University at San Luis Obispo since 1979, serving for six years as Chair and currently in her fourth year as Associate Dean of the College of Science and Mathematics. Dr. Peck has been very active in the field of statistics education. She is the co-author, with Dr. Jay Devore, of the fourth edition of STATISTICS: THE EXPLORATION AND ANALYSIS OF DATA, a widely used and highly regarded textbook for introductory statistics. She is also co-author of INTRODUCTION TO STATISTICS AND DATA ANALYSIS and co-editor of STATISTICAL CASE STUDIES: A COLLABORATION BETWEEN ACADEME AND INDUSTRY. She currently serves as the chair of the ASA's Section on Statistical Education and is a member of the joint ASA/NCTM Committee on Curriculum in Statistics and Probability for Grades K-12. Additionally, Roxy was the editor of the new edition of STATISTICS: A GUIDE TO THE UNKNOWN. This book contains 25 essays that showcase the use of statistics in a wide variety of disciplines.

Chris Olsen taught statistics at George Washington High School in Cedar Rapids, Iowa, for over 25 years as well as at Cornell College and 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 11 years. As a long-time consultant to the College Board, Chris 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. He was a regional winner of the IBM Computer Teacher of the Year award in 1988, and received the Siemens Award for Advanced Placement in mathematics in 1999. Chris is a frequent contributor to the AP Statistics listserv, and has reviewed materials for "The Mathematics Teacher," the AP Central Web Site, The "American Statistician," and the "Journal of the American Statistical Association." He currently writes a column for "Stats" magazine and is a member of the editorial board of the journal, Teaching Statistics. Chris graduated from Iowa State University with a major in mathematics, and while acquiring graduate degrees at the University of Iowa, concentrated on statistics, computer programming, psychometrics, and test development. In his spare time he enjoys reading and hiking. He and his wife have a daughter, Anna, a Cal Tech graduate in Civil Engineering now working on a Post Doc at the University of Colorado, Boulder.

Jay Devore 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. He 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, Jay served as Chair of the Statistics Department at California Polytechnic State University, San Luis Obispo, which has an international reputation for activities in statistics education. In addition to this book, Jay has written several widely used engineering statistics texts and a book in applied mathematical statistics. He is currently collaborating on a business statistics text, and also serves as an Associate Editor for Reviews for several statistics journals. He is the recipient of a distinguished teaching award from Cal Poly and is a Fellow 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, the executive director of a nonprofit organization in New York City, and his daughter Teresa, an ESL teacher in New York City.

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Table of Contents

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.

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