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Probability and Statistics for Engineering and the Sciences

ISBN-10: 0538733527

ISBN-13: 9780538733526

Edition: 8th 2012

Authors: Jay L. (Jay L. Devore) Devore

List price: $263.95
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Book details

List price: $263.95
Edition: 8th
Copyright year: 2012
Publisher: Brooks/Cole
Publication date: 1/1/2011
Binding: Hardcover
Pages: 768
Size: 8.25" wide x 10.25" long x 1.25" tall
Weight: 3.608
Language: English

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 Statistics Department. In addition to this book, Jay has written several widely used engineering statistics texts 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, an ESL teacher in New York City.

Overview And Descriptive Statistics
Populations, Samples, and Processes
Pictorial and Tabular Methods in Descriptive Statistics
Measures of Location
Measures of Variability
Supplementary Exercises
Sample Spaces and Events
Axioms, Interpretations, and Properties of Probability
Counting Techniques
Conditional Probability
Supplementary Exercises
Discrete Random Variables And Probability
Random Variables
Probability Distributions for Discrete Random Variables
Expected Values
The Binomial Probability Distribution
Hypergeometric and Negative Binomial Distributions
The Poisson Probability Distribution
Supplementary Exercises
Continuous Random Variables And Probability Distributions
Probability Density Functions
Cumulative Distribution Functions and Expected Values
The Normal Distribution
The Exponential and Gamma Distributions
Other Continuous Distributions
Probability Plots
Supplementary Exercises
Joint Probability Distributions And Random Samples
Jointly Distributed Random Variables
Expected Values, Covariance, and Correlation
Statistics and Their Distributions
The Distribution of the Sample Mean
The Distribution of a Linear Combination
Supplementary Exercises
Point Estimation
Some General Concepts of Point Estimation
Methods of Point Estimation
Supplementary Exercises
Statistical Intervals Based On A Single Sample
Basic Properties of Confidence Intervals
Large-Sample Confidence Intervals for a Population Mean and Proportion
Intervals Based on a Normal Population Distribution
Confidence Intervals for the Variance and Standard Deviation of a Normal Population
Supplementary Exercises
Tests Of Hypotheses Based On A Single Sample
Hypotheses and Test Procedures
Tests About a Population Mean
Tests Concerning a Population Proportion
Some Comments on Selecting a TeSt. Supplementary Exercises
Inferences Based On Two Samples
Tests and Confidence Intervals for a Difference Between Two Population Means
The Two-Sample t Test and Confidence Interval
Analysis of Paired Data
Inferences Concerning a Difference Between Population Proportions
Inferences Concerning Two Population Variances
Supplementary Exercises
The Analysis Of Variance
Single-Factor ANOVA
Multiple Comparisons in ANOVA
More on Single-Factor ANOVA
Supplementary Exercises
Multifactor Analysis Of Variance
Two-Factor ANOVA with Kij =
Two-Factor ANOVA with Kij =
Three-Factor ANOVA. 2p Factorial Experiments
Supplementary Exercises
Simple Linear Regression And Correlation
The Simple Linear Regression Model
Estimating Model Parameters
Inferences About the Slope Parameter ?1
Inferences Concerning ?Y-x* and the Prediction of Future Y Values
Supplementary Exercises
Nonlinear And Multiple Regression
Aptness of the Model and Model Checking
Regression with Transformed Variables
Polynomial Regression
Multiple Regression Analysis
Other Issues in Multiple Regression
Supplementary Exercises
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
Supplementary Exercises
Distribution-Free Procedures
The Wilcoxon Signed-Rank TeSt
The Wilcoxon Rank-Sum TeSt
Distribution-Free Confidence Intervals
Distribution-Free ANOVA
Supplementary Exercises
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
Supplementary Exercises