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Cover type: Hardback

Edition: 5TH 01

Copyright: 2001

Publisher: Duxbury Press

Published: 2001

International: No

Edition: 5TH 01

Copyright: 2001

Publisher: Duxbury Press

Published: 2001

International: No

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This market-leading book provides a comprehensive introduction to probability models and statistical methods in engineering and the physical and natural sciences. It is a proven accurate book with great examples from an outstanding author, Jay Devore. Through the use of lively and realistic examples, readers go beyond simply learning about statistics--they actually experience its potential. Probability and Statistics for Engineering and the Sciences, Fifth Edition emphasizes models, methodology, and applications rather than rigorous mathematical development and theory.

**Devore, Jay L. : California Polytechnic State University, San Luis Obispo **

**1. OVERVIEW AND DESCRIPTIVE STATISTICS. **

Introduction. Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability. Supplementary Exercises. Bibliography.

**2. PROBABILITY. **

Introduction. Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability. Counting Techniques. Conditional Probability. Independence. Supplementary Exercises. Bibliography.

**3. DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. **

Introduction. 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. Supplementary Exercises. Bibliography.

**4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTION. **

Introduction. Continuous Random Variables and Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Gamma Distribution and Its Relatives. Other Continuous Distributions. Probability Plots. Supplementary Exercises. Bibliography.

**5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES. **

Introduction. 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. Bibliography.

**6. POINT ESTIMATION. **

Introduction. Some General Concepts of Point Estimation. Methods of Point Estimation. Supplementary Exercises. Bibliography.

**7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE. **

Introduction. 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. Bibliography.

**8. TESTS OF HYPOTHESES BASED ON A SINGLE SAMPLE. **

Introduction. Hypothesis and Test Procedures. Tests About a Population Mean. Tests Concerning a Population Proportion. P-Values. Some Comments on Selecting a Test Procedure. Supplementary Exercises. Bibliography.

**9. INFERENCE BASED ON TWO SAMPLES. **

Introduction. 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. Supplementary Exercises. Bibliography.

**10. THE ANALYSIS OF VARIANCE.**

Introduction. Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA. Supplementary Exercises. Bibliography.

**11. MULTIFACTOR ANALYSIS OF VARIANCE. **

Introduction. Two-Factor ANOVA with K[sub ij] = 1. Two-Factor ANOVA with K[sub ij] > 1. Three-Factor ANOVA. 2^p Factorial Experiments. Supplementary Exercises. Bibliography.

**12. SIMPLE LINEAR REGRESSION AND CORRELATION. **

Introduction. The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter B[sub 1]. Inferences Concerning µ[sub gamma o x] and the Prediction of Future Y Values. Correlation. Supplementary Exercises. Bibliography.

**13. NONLINEAR AND MULTIPLE REGRESSION. **

Introduction. Aptness of the Model and Model Checking. Regression with Transformed Values. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression. Supplementary Exercises. Bibliography.

**14. THE ANALYSIS OF CATEGORICAL DATA. **

Introduction. Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness of Fit for Composite Hypotheses. Two-Way Contingency Tables. Supplementary Exercises. Bibliography.

**15. DISTRIBUTION-FREE PROCEDURES. **

Introduction. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA. Supplementary Exercises. Bibliography.

**16. QUALITY CONTROL METHODS. **

Introduction. 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. Bibliography.

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 Signed-Rank Interval. Critical Values for the Wilcoxon Rank-Sum Interval. B Curves for t Tests.

ANSWERS TO ODD-NUMBERED EXERCISES.

INDEX.

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Summary

This market-leading book provides a comprehensive introduction to probability models and statistical methods in engineering and the physical and natural sciences. It is a proven accurate book with great examples from an outstanding author, Jay Devore. Through the use of lively and realistic examples, readers go beyond simply learning about statistics--they actually experience its potential. Probability and Statistics for Engineering and the Sciences, Fifth Edition emphasizes models, methodology, and applications rather than rigorous mathematical development and theory.

Author Bio

**Devore, Jay L. : California Polytechnic State University, San Luis Obispo **

Table of Contents

**1. OVERVIEW AND DESCRIPTIVE STATISTICS. **

Introduction. Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability. Supplementary Exercises. Bibliography.

**2. PROBABILITY. **

Introduction. Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability. Counting Techniques. Conditional Probability. Independence. Supplementary Exercises. Bibliography.

**3. DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. **

Introduction. 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. Supplementary Exercises. Bibliography.

**4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTION. **

Introduction. Continuous Random Variables and Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Gamma Distribution and Its Relatives. Other Continuous Distributions. Probability Plots. Supplementary Exercises. Bibliography.

**5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES. **

Introduction. 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. Bibliography.

**6. POINT ESTIMATION. **

Introduction. Some General Concepts of Point Estimation. Methods of Point Estimation. Supplementary Exercises. Bibliography.

**7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE. **

Introduction. 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. Bibliography.

**8. TESTS OF HYPOTHESES BASED ON A SINGLE SAMPLE. **

Introduction. Hypothesis and Test Procedures. Tests About a Population Mean. Tests Concerning a Population Proportion. P-Values. Some Comments on Selecting a Test Procedure. Supplementary Exercises. Bibliography.

**9. INFERENCE BASED ON TWO SAMPLES. **

Introduction. 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. Supplementary Exercises. Bibliography.

**10. THE ANALYSIS OF VARIANCE.**

Introduction. Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA. Supplementary Exercises. Bibliography.

**11. MULTIFACTOR ANALYSIS OF VARIANCE. **

Introduction. Two-Factor ANOVA with K[sub ij] = 1. Two-Factor ANOVA with K[sub ij] > 1. Three-Factor ANOVA. 2^p Factorial Experiments. Supplementary Exercises. Bibliography.

**12. SIMPLE LINEAR REGRESSION AND CORRELATION. **

Introduction. The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter B[sub 1]. Inferences Concerning µ[sub gamma o x] and the Prediction of Future Y Values. Correlation. Supplementary Exercises. Bibliography.

**13. NONLINEAR AND MULTIPLE REGRESSION. **

Introduction. Aptness of the Model and Model Checking. Regression with Transformed Values. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression. Supplementary Exercises. Bibliography.

**14. THE ANALYSIS OF CATEGORICAL DATA. **

Introduction. Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness of Fit for Composite Hypotheses. Two-Way Contingency Tables. Supplementary Exercises. Bibliography.

**15. DISTRIBUTION-FREE PROCEDURES. **

Introduction. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA. Supplementary Exercises. Bibliography.

**16. QUALITY CONTROL METHODS. **

Introduction. 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. Bibliography.

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 Signed-Rank Interval. Critical Values for the Wilcoxon Rank-Sum Interval. B Curves for t Tests.

ANSWERS TO ODD-NUMBERED EXERCISES.

INDEX.

Publisher Info

Publisher: Duxbury Press

Published: 2001

International: No

Published: 2001

International: No

**Devore, Jay L. : California Polytechnic State University, San Luis Obispo **

**1. OVERVIEW AND DESCRIPTIVE STATISTICS. **

Introduction. Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability. Supplementary Exercises. Bibliography.

**2. PROBABILITY. **

Introduction. Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability. Counting Techniques. Conditional Probability. Independence. Supplementary Exercises. Bibliography.

**3. DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. **

Introduction. 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. Supplementary Exercises. Bibliography.

**4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTION. **

Introduction. Continuous Random Variables and Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Gamma Distribution and Its Relatives. Other Continuous Distributions. Probability Plots. Supplementary Exercises. Bibliography.

**5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES. **

Introduction. 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. Bibliography.

**6. POINT ESTIMATION. **

Introduction. Some General Concepts of Point Estimation. Methods of Point Estimation. Supplementary Exercises. Bibliography.

**7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE. **

Introduction. 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. Bibliography.

**8. TESTS OF HYPOTHESES BASED ON A SINGLE SAMPLE. **

Introduction. Hypothesis and Test Procedures. Tests About a Population Mean. Tests Concerning a Population Proportion. P-Values. Some Comments on Selecting a Test Procedure. Supplementary Exercises. Bibliography.

**9. INFERENCE BASED ON TWO SAMPLES. **

Introduction. 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. Supplementary Exercises. Bibliography.

**10. THE ANALYSIS OF VARIANCE.**

Introduction. Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA. Supplementary Exercises. Bibliography.

**11. MULTIFACTOR ANALYSIS OF VARIANCE. **

Introduction. Two-Factor ANOVA with K[sub ij] = 1. Two-Factor ANOVA with K[sub ij] > 1. Three-Factor ANOVA. 2^p Factorial Experiments. Supplementary Exercises. Bibliography.

**12. SIMPLE LINEAR REGRESSION AND CORRELATION. **

Introduction. The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter B[sub 1]. Inferences Concerning µ[sub gamma o x] and the Prediction of Future Y Values. Correlation. Supplementary Exercises. Bibliography.

**13. NONLINEAR AND MULTIPLE REGRESSION. **

Introduction. Aptness of the Model and Model Checking. Regression with Transformed Values. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression. Supplementary Exercises. Bibliography.

**14. THE ANALYSIS OF CATEGORICAL DATA. **

Introduction. Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness of Fit for Composite Hypotheses. Two-Way Contingency Tables. Supplementary Exercises. Bibliography.

**15. DISTRIBUTION-FREE PROCEDURES. **

Introduction. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA. Supplementary Exercises. Bibliography.

**16. QUALITY CONTROL METHODS. **

Introduction. 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. Bibliography.

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 Signed-Rank Interval. Critical Values for the Wilcoxon Rank-Sum Interval. B Curves for t Tests.

ANSWERS TO ODD-NUMBERED EXERCISES.

INDEX.