Probability and Statistics for Engineering and the Sciences

Auteur

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.

Texte du rabat

Put statistical theories into practice with PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9th Edition. Always a favorite with statistics students, this calculus-based text offers a comprehensive introduction to probability and statistics while demonstrating how professionals apply concepts, models, and methodologies in today's engineering and scientific careers. Jay Devore, an award-winning professor and internationally recognized author and statistician, emphasizes authentic problem scenarios in a multitude of examples and exercises, many of which involve real data, to show how statistics makes sense of the world. Mathematical development and derivations are kept to a minimum. The book also includes output, graphics, and screen shots from various statistical software packages to give you a solid perspective of statistics in action. A Student Solutions Manual, which includes worked-out solutions to almost all the odd-numbered exercises in the book, is available.

Contenu

1. Three-Factor ANOVA 11. 4 2p Factorial Experiments. 12. SIMPLE LINEAR REGRESSION AND CORRELATION. The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter ß1. Inferences Concerning µY•x* and the Prediction of Future Y Values. Correlation. 13. NONLINEAR AND MULTIPLE REGRESSION. Assessing Model Adequacy. Regression with Transformed Variables. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression. 14. GOODNESS-OF-FIT TESTS AND CATEGORICAL DATA ANALYSIS. Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness-of-Fit Tests for Composite Hypotheses. Two-Way Contingency Tables 15. DISTRIBUTION-FREE PROCEDURES. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA. 16. QUALITY CONTROL METHODS. General Comments on Control Charts. Control Charts for Process Location. Control Charts for Process Variation. Control Charts for Attributes. CUSUM Procedures. Acceptance Sampling.