Probability and Statistics for Engineering and the Sciences

ISBN-10: 0495382175

ISBN-13: 9780495382171

Edition: 7th 2008

Authors: Jay L. DeVore

List price: $298.95
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This comprehensive introduction to probability and statistics will give you the solid grounding you need no matter what your engineering specialty. Through the use of lively and realistic examples, the author helps you go beyond simply learning about statistics to actually putting the statistical methods to use. Rather than focus on rigorous mathematical development and potentially overwhelming derivations, the book emphasizes concepts, models, methodology, and applications that facilitate your understanding.
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Book details

List price: $298.95
Edition: 7th
Copyright year: 2008
Publisher: Brooks/Cole
Publication date: 1/26/2007
Binding: Hardcover
Pages: 736
Size: 8.00" wide x 10.00" long x 1.25" tall
Weight: 3.234
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
Sample Spaces and Events
Axioms, Interpretations, and Properties of Probability
Counting Techniques
Conditional Probability
Discrete Random Variables and Probability Distributions
Random Variables
Probability Distributions for Discrete Random Variables
Expected Values of Discrete Random Variables
The Binomial Probability Distribution
Hypergeometric and Negative Binomial Distributions
The Poisson Probability Distribution
Continuous Random Variables and Probability Distributions
Continuous Random Variables and Probability Density Functions
Cumulative Distribution Functions and Expected Values
The Normal Distribution
The Exponential and Gamma Distribution
Other Continuous Distributions
Probability Plots
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
Point Estimation
Some General Concepts of Point Estimation
Methods of Point Estimation
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
Tests of Hypotheses Based on a Single Sample
Hypothesis and Test Procedures
Tests About a Population Mean
Tests Concerning a Population Proportion
Some Comments on Selecting a Test
Inferences Based on Two Samples
Z 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
The Analysis of Variance
Single-Factor ANOVA
Multiple Comparisons in ANOVA
More on Single-Factor ANOVA
Multifactor Analysis of Variance
Two-Factor ANOVA with Kij = 1
Two-Factor ANOVA with Kij > 1
Three-Factor ANOVA
2p Factorial Experiments
Simple Linear Regression and Correlation
The Simple Linear Regression Model
Estimating Model Parameters
Inferences About the Slope Parameter G1
Inferences Concerning &Y-x* and the Prediction of Future Y Values
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
Goodness-of-Fit Tests and Categorical Data Analysis
Goodness-of-Fit Tests When Category Probabilities are Completely Specified
Goodness of Fit for Composite Hypotheses
Two-Way Contingency Tables
Distribution-Free Procedures
The Wilcoxon Signed-Rank Test
The Wilcoxon Rank-Sum Test
Distribution-Free Confidence Intervals
Distribution-Free ANOVA
Quality Control Methods
General Comments on Control Charts
Control Charts fort Process Location
Control Charts for Process Variation
Control Charts for Attributes
CUSUM Procedures
Acceptance Sampling
Appendix TABLES
Cumulative Binomial Probabilities
Cumulative Poisson Probabilities
Standard Normal Curve Areas
The Incomplete Gamma Function
Critical Values for t Distributions
Tolerance Critical Values for Normal Population Distributions
Critical Values for Chi-Squared Distributions
T Curve Tail Areas
Critical Values for F Distributions
Critical Values for Studentized Range Distributions
Chi-Squared Curve Tail Areas
Critical Values for the Ryan-Joiner Test of Normality
Critical Values for the Wilcoxon Signed-Rank Test
Critical Values for the Wilcoxon Rank-Sum Test
Critical Values for the Wilcoxon
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