Engineering Statistics, 4/e

Douglas C. Montgomery, George C. Runger, Norma F. Hubele





The statistics every engineer should know

Statistics is essential for solving many types of engineering problems. Focusing on the statistical techniques most often used in engineering practice, Montgomery, Runger, and Hubele's Engineering Statistics, Fourth Edition presents a wide range of techniques and methods that you'll be able to call upon in your professional capacities.

Through its three previous editions, Engineering Statistics set the standard for statistical texts serving engineers. This Fourth Edition follows the classic's winning approach, and provides a host of new features and improvements that you'll appreciate. Developed initially with sponsorship from the National Science Foundation, this revision covers all the major aspects of engineering statistics including:
* Descriptive statistics
* Probability and probability distributions
* Statistical tests and confidence intervals for one and two samples
* Building regression models
* Designing and analyzing engineering experiments
* Statistical quality control

The new edition also features numerous enhancements that help you grasp and absorb the material. Emphasizing data analysis and statistical inference, the text makes use of new, cross-sections of real engineering situations and real data sets and takes a step-by-step approach so that you gain insight into the underlying structure of the problems as you master the problem-solving techniques. Notes which help you interpret results are a particularly useful feature of the revised text.

With comprehensive integration of the PC-based statistical software Minitab and online support through WileyPLUS, this newest edition of Engineering Statistics will serve as a practical introduction for students and a reliable reference for every stage of your engineering career.


Table of Contents
1. The Role of Statistics in Engineering.

2. Data Summary and Presentation.

3. Random Variables and Probability Distributions.

4. Decision Making for a Single Sample.

5. Decision Making for Two Samples.

6. Building Empirical Models.

7. Design of Engineering Experiments.

8. Statistical Quality Control.