For junior/senior undergraduates taking probability and statistics as it applied to engineering, science or computer science.
With its unique balance of theory and methodology, this classic text provides a rigorous introduction to basic probability theory and statistical inference that is motivated by interesting, relevant applications. Extensively updated coverage, new problem sets, and chapter-ending material extend the text's relevance to a new generation of engineers and scientists.
Features
Broad coverage of all types of statistical studies-Exposes students to designed experiments, the observational study, and the retrospective study, using examples of each type and outlining their advantages and disadvantages.
Flexible organization-Makes text appropriate for one- or two-term courses.
Accurate, compelling exercise sets-Use significant real data from actual studies.
Detailed case studies-Give students real-life applications of statistical concepts.
New To This Edition
Updated problem sets and applications-Includes nearly 20% new problem sets that demonstrate updated applications to engineering as well as biological, physical, and computer science.
New end-of-chapter review material-Emphasizes key ideas as well as the risks and hazards associated with practical application of the material.
New self-contained chapter on Bayesian statistics-Takes a practical approach by introducing applications in many fields. Introduces the concept of subjective probability, and treats point and interval estimation from a Bayesian point of view using practical examples.
Extensive updates throughout-Includes new material on topics such as: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; "dummy" variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and lognormal distributions; central limit theorem, and data plotting.
New exercises on random variables and probability distributions-Use compelling topics such as particle size distribution for missile fuel; measurement errors in scientific systems; studies of time to failure for washing machines; assembly-line production of electron tubes; product shelf life; airport passenger congestion; chemical impurities; failure in systems of electronic components working in parallel, and many others.
Restructured coverage of hypothesis testing-Helps students develop a clear understanding of what is and is not being accomplished in hypothesis testing.
Real-life applications of the Poisson, binomial, and hypergeometric distributions-Generate student interest using topics such as flaws in manufactured copper wire, highway potholes, hospital patient traffic, airport luggage screening, and homeland security.
Expanded treatment of R2, the coefficient of determination-Centers around the need to compromise between achieving a "good fit" and the inevitable loss in error degrees of freedom when one "overfits."
Expanded discussion of Tukey's test on multiple comparisons-Presents additional detail on the notion of error rate and ?-values in the context of simultaneous confidence intervals.
New section on data transformation in analysis of variance-Addresses the robustness of analysis of variance to the assumption of homogeneous variance, connecting the discussion to previous sections on diagnostic plots to detect violations in assumptions.
Interaction plots-Provides examples of scientific interpretations and new exercises using graphics.
New examples requiring the use of one-sided intervals-Includes confidence intervals, prediction intervals, and tolerance intervals.
Two level designs as screening experiments--Highlights their role as part of a sequential plan in which the scientist or engineer attempts to learn about the process, assess the role of the candidate factors, and give insight to determine the best region of experimentation.
1. Introduction to Statistics and Data Analysis
2. Probability
3. Random Variables and Probability Distributions
4. Mathematical Expectations
5. Some Discrete Probability Distributions
6. Some Continuous Probability Distributions
7. Functions of Random Variables (optional)
8. Fundamental Distributions and Data Description
9. One and Two Sample Estimation Problems
10. One and Two Sided Tests of Hypotheses
11. Simple Linear Regression
12. Multiple Linear Regression
13. One Factor Experiments: General
14. Factorial Experiments (Two or More Factors)
15. 2k Factorial Experiments and Fractions
16. Nonparametric Statistics
17. Statistical Quality Control
18. Bayesian Statistics
Other Editions for Probability and Statistics for Engineers and Scientists
For junior/senior undergraduates taking probability and statistics as it applied to engineering, science or computer science.
With its unique balance of theory and methodology, this classic text provides a rigorous introduction to basic probability theory and statistical inference that is motivated by interesting, relevant applications. Extensively updated coverage, new problem sets, and chapter-ending material extend the text's relevance to a new generation of engineers and scientists.
Features
Broad coverage of all types of statistical studies-Exposes students to designed experiments, the observational study, and the retrospective study, using examples of each type and outlining their advantages and disadvantages.
Flexible organization-Makes text appropriate for one- or two-term courses.
Accurate, compelling exercise sets-Use significant real data from actual studies.
Detailed case studies-Give students real-life applications of statistical concepts.
New To This Edition
Updated problem sets and applications-Includes nearly 20% new problem sets that demonstrate updated applications to engineering as well as biological, physical, and computer science.
New end-of-chapter review material-Emphasizes key ideas as well as the risks and hazards associated with practical application of the material.
New self-contained chapter on Bayesian statistics-Takes a practical approach by introducing applications in many fields. Introduces the concept of subjective probability, and treats point and interval estimation from a Bayesian point of view using practical examples.
Extensive updates throughout-Includes new material on topics such as: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; "dummy" variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and lognormal distributions; central limit theorem, and data plotting.
New exercises on random variables and probability distributions-Use compelling topics such as particle size distribution for missile fuel; measurement errors in scientific systems; studies of time to failure for washing machines; assembly-line production of electron tubes; product shelf life; airport passenger congestion; chemical impurities; failure in systems of electronic components working in parallel, and many others.
Restructured coverage of hypothesis testing-Helps students develop a clear understanding of what is and is not being accomplished in hypothesis testing.
Real-life applications of the Poisson, binomial, and hypergeometric distributions-Generate student interest using topics such as flaws in manufactured copper wire, highway potholes, hospital patient traffic, airport luggage screening, and homeland security.
Expanded treatment of R2, the coefficient of determination-Centers around the need to compromise between achieving a "good fit" and the inevitable loss in error degrees of freedom when one "overfits."
Expanded discussion of Tukey's test on multiple comparisons-Presents additional detail on the notion of error rate and ?-values in the context of simultaneous confidence intervals.
New section on data transformation in analysis of variance-Addresses the robustness of analysis of variance to the assumption of homogeneous variance, connecting the discussion to previous sections on diagnostic plots to detect violations in assumptions.
Interaction plots-Provides examples of scientific interpretations and new exercises using graphics.
New examples requiring the use of one-sided intervals-Includes confidence intervals, prediction intervals, and tolerance intervals.
Two level designs as screening experiments--Highlights their role as part of a sequential plan in which the scientist or engineer attempts to learn about the process, assess the role of the candidate factors, and give insight to determine the best region of experimentation.