Miller & Freund's Probability and Statistics for Engineers, Global Edition

Series
Pearson
Author
Richard A. Johnson / Irwin Miller / John E. Freund  
Publisher
Pearson
Cover
Softcover
Edition
9
Language
English
Total pages
552
Pub.-date
June 2017
ISBN13
9781292176017
ISBN
1292176016
Related Titles


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9781292176017
Miller & Freund's Probability and Statistics for Engineers, Global Edition
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Description

For an introductory, one or two semester, or sophomore-junior level course in Probability and Statistics or Applied Statistics for engineering, physical science, and mathematics students.


An Applications-Focused Introduction to Probability and Statistics

Miller & Freund's Probability and Statistics for Engineers is rich in exercises and examples, and explores both elementary probability and basic statistics, with an emphasis on engineering and science applications. Much of the data has been collected from the author's own consulting experience and from discussions with scientists and engineers about the use of statistics in their fields. In later chapters, the text emphasizes designed experiments, especially two-level factorial design. The Ninth Edition includes several new datasets and examples showing application of statistics in scientific investigations, familiarizing students with the latest methods, and readying them to become real-world engineers and scientists.

Features

  • A clear, concise presentation helps students quickly gain an understanding of the concepts.
  • NEW! Many new examples on important current engineering and scientific data further strengthen the text’s orientation towards an applications-based introduction to statistics.
  • NEW! Added graphs illustrating P-values appear in several examples along with an interpretation.
  • NEW! More details about using R commands make it easy for students to check calculations on their own laptop or tablet, while reading an example.
  • NEW! Key formulas are stressed and calculation formulas are downplayed. Computation formulas are set in the context of an application which only requires all, or mostly all, integer arithmetic, and now appear only at the end of sections. Students can then check their results with their choice of software.
  • NEW! All examples are now numbered within each chapter.
  • Rich problem sets give students the practice they need to learn the material.
  • Do's and Don'ts at the end of each chapter help students to apply statistics correctly to avoid misuses.
  • NEW! New data-based exercises feature real applications to help stimulate interest and strengthen a student’s appreciation of the role of statistics in engineering applications.
  • Computer exercises for MINITAB® help students learn and become familiar with this software.
  • Many data sets are drawn from author Richard Johnson's own consulting activities as well as discussions with scientists and engineers about their statistical problems. This helps illustrate the statistical methods and reasoning required in order to draw generalizations from data collected in actual experiments.
  • Content highlights:
    • Case studies in the first two chapters illustrate the power of even simple statistical methods to suggest changes that make major improvements in production processes.
    • Graphs of the sampling distribution show the critical region and P value, and accompany the examples of testing hypotheses. These graphs help reinforce student understanding of the critical region, significance level, and P value.
    • Summary tables of testing procedures provide a convenient reference for students.
    • NEW! A section on graphic presentation of 22 and 23 designs includes coverage of (i) systematically varying several input variables at a time and (ii) how to interpret interactions. This serves as a stand-alone introduction to the design of experiments for those instructors who can only devote two or three lectures to the subject.
    • Solid treatment of confidence interval techniques and hypothesis testing procedures, which clearly and consistently delineates the steps for hypothesis testing in each application.
    • Clear, current coverage of two-level factorial design. To explore interactions, engineers have to know about experiments where more than one variable has been changed at the same time in design.
    • A full chapter on modern ideas of quality improvement provides up-to-date coverage of this popular significant trend in the field.
    • Accessible discussion on joint distributions and the properties of expectation--this is a difficult topic not always covered in the course, but if so desired, here is a nice, quick treatment of it.

New to this Edition

  • Many new examples on important current engineering and scientific data further strengthen the text’s orientation towards an applications-based introduction to statistics.
  • Added graphs illustrating P-values appear in several examples along with an interpretation.
  • More details about using R commands make it easy for students to check calculations on their own laptop or tablet, while reading an example.
  • Key formulas are stressed and calculation formulas are downplayed. Computation formulas are set in the context of an application which only requires all, or mostly all, integer arithmetic, and now appear only at the end of sections. Students can then check their results with their choice of software.
  • All examples are now numbered within each chapter.
  • New data-based exercises feature real applications to help stimulate interest and strengthen a student’s appreciation of the role of statistics in engineering applications.
  • Content highlights:
    • A section on graphic presentation of 22 and 23 designs includes coverage of (i) systematically varying several input variables at a time and (ii) how to interpret interactions. This serves as a stand-alone introduction to the design of experiments for those instructors who can only devote two or three lectures to the subject.

Table of Contents

  1. Introduction

  2. Organization and Description of Data

  3. Probability

  4. Probability Distributions

  5. Probability Densities

  6. Sampling Distributions

  7. Inferences Concerning a Mean

  8. Comparing Two Treatments

  9. Inferences Concerning Variances

  10. Inferences Concerning Proportions

  11. Regression Analysis

  12. Analysis of Variance

  13. Factorial Experimentation

  14. Nonparametric Tests

  15. The Statistical Content of Quality Improvement Programs

  16. Application to Reliability and Life Testing