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Probability and Statistics for Engineering and Sciences (7TH 08  Old Edition)by Jay L. Devore
Synopses & ReviewsPlease note that used books may not include additional media (study guides, CDs, DVDs, solutions manuals, etc.) as described in the publisher comments.
Publisher Comments: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.
Synopsis:This bestseller continues to provide engineering students with a comprehensive introduction to probability and statistics through its use of concepts, models, methodology, and lively and realistic examples and applications.
About the AuthorJay 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 highlevel positions in nonprofit organizations in Boston and New York City, and his daughter Teresa, an ESL teacher in New York City.
Table of Contents1. OVERVIEW AND DESCRIPTIVE STATISTICS. Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability. 2. PROBABILITY. Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability. Counting Techniques. Conditional Probability. Independence. 3. 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. 4. 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. 5. 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. 6. POINT ESTIMATION. Some General Concepts of Point Estimation. Methods of Point Estimation. 7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE. Basic Properties of Confidence Intervals. LargeSample 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. 8. TESTS OF HYPOTHESES BASED ON A SINGLE SAMPLE. Hypothesis and Test Procedures. Tests About a Population Mean. Tests Concerning a Population Proportion. PValues. Some Comments on Selecting a Test. 9. INFERENCES BASED ON TWO SAMPLES. z Tests and Confidence Intervals for a Difference Between Two Population Means. The TwoSample t Test and Confidence Interval. Analysis of Paired Data. Inferences Concerning a Difference Between Population Proportions. Inferences Concerning Two Population Variances. 10. THE ANALYSIS OF VARIANCE. SingleFactor ANOVA. Multiple Comparisons in ANOVA. More on SingleFactor ANOVA. 11. MULTIFACTOR ANALYSIS OF VARIANCE. TwoFactor ANOVA with Kij = 1. TwoFactor ANOVA with Kij > 1. ThreeFactor ANOVA. 2p Factorial Experiments. 12. SIMPLE LINEAR REGRESSION AND CORRELATION. The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter â1. Inferences Concerning µYx* and the Prediction of Future Y Values. Correlation. 13. 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. 14. GOODNESSOFFIT TESTS AND CATEGORICAL DATA ANALYSIS. GoodnessofFit Tests When Category Probabilities are Completely Specified. Goodness of Fit for Composite Hypotheses. TwoWay Contingency Tables. 15. DISTRIBUTIONFREE PROCEDURES. The Wilcoxon SignedRank Test. The Wilcoxon RankSum Test. DistributionFree Confidence Intervals. DistributionFree ANOVA. 16. 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 ChiSquared Distributions. t Curve Tail Areas. Critical Values for F Distributions. Critical Values for Studentized Range Distributions. ChiSquared Curve Tail Areas. Critical Values for the RyanJoiner Test of Normality. Critical Values for the Wilcoxon SignedRank Test. Critical Values for the Wilcoxon RankSum Test. Critical Values for the Wilcoxon SignedRank Interval. Critical Values for the Wilcoxon RankSum Interval. â Curves for t Tests. Answers to OddNumbered Exercises. Index.
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