Higher Education

Author(s): Jay L. Devore

ISBN: 9789353506247

Edition: 9^{th}

© Year : 2016

Binding: Paperback

Pages: 768

Trim Size : 254 x 203 mm

Put statistical theories into practice with PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9E, METRIC EDITION. Always a market favorite, 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, stresses lively examples and engineering activities to drive home the numbers without exhaustive mathematical development and derivations. Many examples, practice problems, sample tests, and simulations based on real data and issues help you build a more intuitive connection to the material. A proven and accurate text, PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9E, METRIC EDITION also includes graphics and screen shots from SAS®, MINITAB®, and Java™ Applets to give you a solid perspective of statistics in action.

- Computer Focus: An abundance of computer output from SAS® and MINITAB® supports student understanding of ANOVA and regression, while Java™ Applets specifically designed for this calculus-based text demonstrate statistics visually.
- Helpful Study Aids: Sample exams and a glossary of symbols and acronyms give students the experience and confidence they need to master concepts and ace exams.
- Key Focal Points: The author emphasizes the critical role that variation plays in statistics, including the nature of variation in the slope estimate in simple linear regression, and includes a detailed description of pooled t procedures for analysis.
- Sampling Simulations: "Simulation Experiments" in the text help students understand sampling distributions and the insights to gain from them, particularly when derivations are too complex to carry out.
- Authentic Data: Multiple new examples and exercises based on real data or actual problems give students practice with statistics for the modern era.
- Clearer Narratives: Polished for even better clarity, chapters focus on delivering a deep, intuitive understanding of the concepts, instead of the purely theoretical approach favored by other texts.
- Practical Probability: Examples and exercises in chapters 2 through 5 are now based on information from published sources, helping to link the concepts to contemporary issues in the workplace and at actual companies.
- Updated Hypothesis Testing: Hypothesis testing based on P-values now replaces the former rejection region approach throughout the text, focusing on the most current approach in practice today.

1. 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. The Binomial Probability Distribution. Hypergeometric and Negative Binomial Distributions. The Poisson Probability Distribution.

4. 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.

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. 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.

8. TESTS OF HYPOTHESIS BASED ON A SINGLE SAMPLE.

Hypotheses and Test Procedures. z Tests for Hypotheses About a Population Mean.

The One-Sample t Test. Tests Concerning a Population Proportion. Further Aspects of Hypothesis Testing.

9. 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.

10. THE ANALYSIS OF VARIANCE.

Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA.

11. MULTIFACTOR ANALYSIS OF VARIANCE.

Two-Factor ANOVA with Kij = 1. Two-Factor ANOVA with Kij > 1. Three-Factor ANOVA. 2p Factorial Experiments.

12. SIMPLE LINEAR REGRESSION AND CORRELATION.

The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter β1. 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.

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

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.