The new edition of Anthony Hayter's book continues in the same student-oriented vein that has made previous editions successful. Because Tony Hayter teaches and conducts research at a premier engineering school, he is in touch with engineers daily and understands their vocabulary. This leads to a clear and more readable writing style that students understand and appreciate. Additionally, because of his intimacy with the professional community, Hayter includes many high-interest examples and datasets that keep students' attention throughout the term. PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS employs a flexible approach with regard to the use of computer tools. Because the book is not tied to a particular software package, instructors may choose the program that best suits their needs. However, the book does provide substantial computer output (using MINITAB and other programs) to give students the necessary practice in interpreting output. "Computer Note" sections offer tips for using various software packages to perform analysis of the datasets, which can be downloaded from the website. Through the use of extensive examples and datasets, the book illustrates the importance of statistical data collection and analysis for students in the fields of aerospace, biochemical, civil, electrical, environmental, industrial, mechanical, and textile engineering, as well as for students in physics, chemistry, computing, biology, management, and mathematics.

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1. PROBABILITY THEORY. Probabilities. Events. Combinations of Events. Conditional Probability. Probabilities of Event Intersections. Posterior Probabilities. Counting Techniques. 2. RANDOM VARIABLES. Discrete Random Variables. Continuous Random Variables. The Expectation of a Random Variable. The Variance of a Random Variable. Jointly Distributed Random Variables. Combinations and Functions of Random Variables. 3. DISCRETE PROBABILITY DISTRIBUTIONS. The Binomial Distribution. The Geometric and Negative Binomial Distributions. The Hypergeometric Distribution. The Poisson Distribution. The Multinomial Distribution. 4. CONTINUOUS PROBABILITY DISTRIBUTIONS. The Uniform Distribution. The Exponential Distribution. The Gamma Distribution. The Weibull Distribution. The Beta Distribution. 5. THE NORMAL DISTRIBUTION. Probability Calculations Using the Normal Distribution. Linear Combinations of Normal Random Variables. Approximating Distributions with the Normal Distribution. Distributions Related to the Normal Distribution. 6. DESCRIPTIVE STATISTICS. Experimentation. Data Presentation. Sample Statistics. Examples. 7. STATISTICAL ESTIMATION AND SAMPLING DISTRIBUTIONS. Point Estimates. Properties of Point Estimates. Sampling Distributions. Constructing Parameter Estimates. 8. INFERENCES ON A POPULATION MEAN. Confidence Intervals. Hypothesis Testing. Summary. 9. COMPARING TWO POPULATION MEANS. Introduction. Analysis of Paired Samples. Analysis of Independent Samples. Summary. 10. DISCRETE DATA ANALYSIS. Inferences on a population Proportion. Comparing Two Population Proportions. Goodness-of-Fit Tests for One-Way Contingency Tables. Testing for Independence in Two-Way Contingency Tables. 11. THE ANALYSIS OF VARIANCE. One Factor Analysis of Variance. Randomized Block Designs. 12. SIMPLE LINEAR REGRESSION AND CORRELATION. The Simple Linear Regression Model. Fitting the Regression Line. Inferences on the Slope Parameter ss1. Inferences on the Regression Line. Prediction Intervals for Future Response Values. The Analysis of Variance Tables. Residual Analysis. Variable Transformation. Correlation Analysis. 13. MULTIPLE LINEAR REGRESSION AND NONLINEAR REGRESSION. Introduction to Multiple Linear Regression. Examples of Multiple Linear Regression. matrix Algebra Formulation of Multi-le Linear Regression. Evaluating Model Adequacy. Nonlinear Regression. 14. MULTIFACTOR EXPERIMENTAL DESIGN AND ANALYSIS. Experiments with Two Factors. Experiments with Three or More Factors. 15. NONPARAMETRIC STATISTICAL ANALYSIS. The analysis of a single Population. Comparing Two Populations. Comparing Three or More Populations. 16. QUALITY CONTROL METHODS. Introduction. Statistical Process control. Variable Control Charts. Attribute Control Charts. Acceptance Sampling. 17. RELIABILITY ANALYSIS AND LIFE TESTING. System Reliability. Modeling Failure Rates. Life Testing. Tables. Answers to Odd-Numbered Problems. Index.

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