Synopses & Reviews
Jay Devore and Roxy Peck's new book features analysis of data, often using real data, as the prime motivation for the study of statistics. Traditional in structure yet modern in spirit, the book incorporates material that reflects an increased emphasis on issues involved in data collection. Focusing on concepts rather than calculation, the authors place the emphasis in probability on intuition and simulation (rather than mathematical formalism and offer more examples and exercises in order to integrate description and exploration from the early chapters.
About 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 the National 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.
Table of Contents
1.THE ROLE OF STATISTICS. Three Reasons to Study Statistics. Statistics and Data Analysis. The Nature and Role of Variability. 2.THE DATA ANALYSIS PROCESS AND COLLECTING DATA SENSIBLY. Types of Data. The Data Analysis Process. Collecting Data Sensibly: Observation and Experiment. Sampling. Simple Comparative Experiments. 3. GRAPHICAL METHODS FOR DESCRIBING DATA. Displaying Categorical Data: Frequency Distributions, Bar Charts and Pie Charts. Displaying Numerical Data: Dotplots and Stem-and-Leaf Displays. Displaying Numerical Data: Frequency Distributions and Histograms. Interpreting 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 Spread: Chebyshevs Rule, The Empirical Rule, and z-Scores. Interpreting the Results of Statistical Analyses. 5. SUMMARIZING BIVARIATE DATA. Scatter Plots. Correlation. Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Nonlinear Relationships and Transformations. Interpreting the Results of Statistical Analyses. 6. PROBABILITY. Interpreting Probabilities and Basic Probability Rules. Probability as a Basis for Making Decisions. Estimating Probabilities. 7. POPULATION 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 DISTRIBUTIONS. 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. A Large Sample Confidence Interval for a Population Proportion. A Confidence Interval for a Population Mean. Interpreting the Results of Statistical Analyses. 10. HYPOTHESIS TESTING USING A SINGLE SAMPLE. Hypotheses and Test Procedures. Errors in Hypothesis Testing. Large-Sample Hypothesis Tests for a Population Proportion. Hypothesis Tests for a Population Mean. Power and Probability of Type II Error (Optional). Interpreting the Results of Statistical Analyses. 11. COMPARING TWO POPULATIONS OR TREATMENTS. Inferences Concerning the Difference Between Two Population Means or Treatments Using Independent Samples. Inferences Concerning the Difference Between Two Population Means Using Paired Samples. Large-Sample Inferences Concerning a Difference Between Two Population Proportions. Distribution-Free Procedures for Inferences Concerning a Difference Between Two Population Means Using Independent Samples (Optional). Interpreting the Results of Statistical Analyses. 12. THE ANALYSIS OF CATEGORICAL DATA AND GOODNESS-OF-FIT TESTS. Chi-squared Tests for Univariate Categorical Data. Tests for Homogeneity and Independence in a Two-Way Table. Interpreting the Results of Statistical Analyses. 13. SIMPLE LINEAR REGRESSION AND CORRELATION INFERENTIAL METHODS. The Simple Linear Regression Model. Inferences Concerning the Slope of Population Regression Line. Checking Model Adequacy. Inferences Based on the Estimated Regression Line (Optional). Inferences About the Population Correlation Coefficient (Optional). Interpreting 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. Other Issues in Multiple Regression. 15. THE ANALYSIS OF VARIANCE. Single Factor ANOVA and the F Test. Multiple Comparisons. The F Test for a Randomized Block Experiment. Two-Factor ANOVA. Interpreting the Results of Statistical Analyses. Appendix: Anova Computations. Statistical Tables. The Binomial Distribution. Answers To Selected Odd-Numbered Exercises. Index.