Probability and Statistics for Engineering and the Sciences
This market-leading text provides a comprehensive introduction to probability and statistics for engineering students in all specialties. Proven, accurate, and lauded for its excellent examples, PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 8e, International Edition evidences Jay Devore’s reputation as an outstanding author and leader in the academic community. Devore emphasizes concepts, models, methodology, and applications as opposed to rigorous mathematical development and derivations. Aided by his lively and realistic examples, students go beyond simply learning about statistics—they also learn how to put statistical methods to use.
Features
• Sample exams help students build confidence and master concepts prior to taking class exams; the glossary of symbols/acronyms, which includes text page references, is another useful study aid.
• Exclusively from Cengage Learning, Enhanced WebAssign® allows you to assign exercises from this text and ensure that your students receive multimedia tutorial support and immediate feedback as they complete their automatically graded assignments.
• Several exercises refer to material covered in earlier sections and chapters, allowing students to more easily see the connections between concepts.
• Virtually every example and exercise has a real world context. Real data in exercises and examples stimulate students'' interest and enhance their comprehension of concepts.
• Notable content includes a strong emphasis on the role that variation plays in statistics, emphasis on the nature of variation in the slope estimate in simple linear regression, and inclusion of a detailed description of pooled t procedures to provide a balance between unpooled and pooled analyses.
• "Simulation Experiments" help students gain an understanding of sampling distributions and the insight they provide, especially when a derivation is too complex to carry out.
• Strong computer coverage, especially with ANOVA and regression, is supported by an abundance of computer output from SAS® and MINITAB® and coverage of computer methods. Inclusion of Java™ Applets from Gary McClelland''s SEEING STATISTICS, specifically designed for this calculus-based text, allows students to experience statistics firsthand.
Table of Contents
1. OVERVIEW AND DESCRIPTIVE STATISTICS.
Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability. Supplementary Exercises .
2. PROBABILITY.
Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability. Counting Techniques. Conditional Probability. Independence. Supplementary Exercises.
3. 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.
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. Supplementary Exercises.
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. Supplementary Exercises.
6. POINT ESTIMATION.
Some General Concepts of Point Estimation. Methods of Point Estimation. Supplementary Exercises.
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.
Supplementary Exercises.
8. TESTS OF HYPOTHESES BASED ON A SINGLE SAMPLE.
Hypotheses and Test Procedures. Tests About a Population Mean. Tests Concerning a Population Proportion. P-Values. Some Comments on Selecting a Test. Supplementary Exercises.
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. Supplementary Exercises.
10. THE ANALYSIS OF VARIANCE.
Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA. Supplementary Exercises.
11. MULTIFACTOR ANALYSIS OF VARIANCE.
Two-Factor ANOVA with Kij = 1. Two-Factor ANOVA with Kij = 1. Three-Factor ANOVA. 2p Factorial Experiments. Supplementary Exercises.
12. SIMPLE LINEAR REGRESSION AND CORRELATION.
The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter â1. Inferences Concerning µY-x* and the Prediction of Future Y Values. Correlation. Supplementary Exercises.
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. Supplementary Exercises.
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.
Supplementary Exercises.
15. DISTRIBUTION-FREE PROCEDURES.
The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA. Supplementary Exercises.
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. Supplementary Exercises.
