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PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS, Fourth Edition, continues the student-oriented approach that has made previous editions successful. As a teacher and researcher at a premier engineering school, author Tony Hayter is in touch with engineers daily--and understands their vocabulary. The result of this familiarity with the professional community is a clear and readable writing style that students understand and appreciate, as well as high-interest, relevant examples and data sets that keep students' attention. A flexible approach to the use of computer tools, including tips for using various software packages, allows instructors to choose the program that best suits their needs. At the same time, substantial computer output (using MINITAB and other programs) gives students the necessary practice in interpreting output. Extensive use of examples and data sets 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.
- A new Guide of Statistical Methodologies can be used to match statistical inference methodologies to data sets and research questions. It includes examples that are typically used for each methodology. The guide helps students address a common challenge--determining which method or technique is best to use with a particular type of problem.
- The Fourth Edition includes more than 200 new and revised problems, with many new data sets and interesting real world problems from every engineering area. Students have raved about the true/false questions in particular, commenting that they provide a needed self-check opportunity.
- A new continuing case study, Internet Marketing, begins before Chapter 1 and runs through Chapter 12. This timely case study, on a topic relevant to students in their daily lives, presents various problems and shows how probability and statistics are used to analyze and solve them. It also helps illustrate the connections among topics within the first 12 chapters.
- Composition of the book allows flexibility in the order in which the material is taught. The material has been divided into four sections based on probability (Chapters 1-5), basic statistics (Chapters 6-10), advanced statistical methodologies (Chapters 11-14), and additional topics (Chapters 15-17). The Preface offers suggested paths that instructors may follow based on topic preference, making the book ideal for departments in which different methods of teaching coexist.
- This book can also serve as a handbook of statistical methodologies for undergraduate and graduate engineering students.
- Answers to all odd-numbered problems from the end-of-chapter sections are provided at the back of the book.
- Worked examples (77) and more than 150 data sets represent the many different areas of engineering; for instance, civil, mechanical, electrical, industrial, aerospace, biomedical, textile, chemical, and computing.
- Dozens of graphs, along with graphical tools, help students learn concepts visually.
- To help students grasp concepts, each topic is introduced with references to several real examples from engineering and the sciences. After the topic has been developed technically, a highlighted box reinforces students' learning by summarizing the important points.
- Many examples illustrate proper application of new methodologies, and are developed throughout the chapters as increasingly sophisticated methodologies are considered. This allows students to build on their learning in a manageable way, and understand connections among methodologies.
- Computer Note sections offer tips for using various software packages to perform analysis of data sets, which are referenced in the text and available for download from the book's website.
- The applied presentation emphasizes the understanding of underlying concepts and the application of statistical methodologies.
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 Multiple 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.
Answers to Odd-Numbered Problems.
Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.
Instructors Solutions Manual
Complete solutions to all the problems in the text.
This online instructor database offers complete worked solutions to all exercises in the text, allowing you to create customized, secure solutions printouts (in PDF format) matched exactly to the problems you assign in class. Visit Cengage.com/solutionbuilder for more information.
Student Solutions Manual
Contains fully worked-out solutions to all of the odd-numbered exercises in the text, giving students a way to check their answers and ensure that they took the correct steps to arrive at an answer.
Student Solutions Manual
Go beyond the answers--see what it takes to get there and improve your grade! This manual provides worked-out, step-by-step solutions to the odd-numbered problems in the text, giving you the information you need to truly understand how these problems are solved.