George C. Runger, Ph.D., is a Professor in the department of Industrial Engineering at Arizona State University. His research is on data mining, real-time monitoring and control, and other data-analysis methods with a focus on large, complex, multivariate data streams. His work is funded by grants from the National Science Foundation and corporations. In addition to academic work, he was a senior engineer at IBM. He holds degrees in industrial engineering and statistics.

Norma Faris Hubele, Professor of Engineering and Statistics at Arizona State University, and Director of Strategic Initiatives for the Ira A. Fulton School of Engineering, has taught and done research at the University level and with industry for over 20 years. Her cutting edge educational research has been supported by the National Science Foundation and is reflected in this textbook.

• Emphasized in probability and hypothesis testing problems
• Provides insight into the underlying problem structure for students
• Eases the instructor’s burden in explaining a logic approach to a variety of problems

Enhanced pedagogy supports student learning

• Margin notes highlight important concepts and illustrate fundamental ideas
• These notes guide the student through the thought process to promote more critical thinking and analysis
• Text now provides enhanced presentation of graphics and data displays providing better visualization of information

25% new homework Exercises

• Even more varied background for problems (including EE, CS, bioengineering), and more real data from published sources
• Students are asked to select and/or critique the method for solving a problem.
• Students are asked to analyze and interpret the results, and make a recommendation.

WileyPLUSis a powerful online tool

• Provides instructors and students with an integrated suite of teaching and learning resources
• Online version of the text, in one easy-to-use website

• Demonstrate real world application of concepts
• Demonstrates actual engineering examples
• Uses real data and real engineering situations to motivate students
• Motivates students to learn new concepts and provides practical engineering examples 

Strong emphasis on data analysis and statistical inference, with a lesser focus on distributions and probability

• Provides the type of coverage most useful to engineering students
• Organized to become a useful reference text long into the student’s career

Now includes more comprehensive integration of PC-based statistics software

• MINITAB, the most widely used statistical packaged, is featured but other commercial packages can be easily used in conjunction with Engineering Statistics 4e providing the greatest flexibility for faculty and students
• All data sets are available from the book companion web site in several common formats offering the greatest ease of use in the classroom and for homework assignments

Strong focus on P-value approach to hypothesis testing including approximating and determining exact P-values using software tools

• Familiarizes students with practical approaches to engineering problems
• Teaches approximation, a tool practicing engineers frequently use 

Thorough, concise coverage of more advanced topics including regression modeling, design of experiments, and statistical process control

• Presents key material critical for all engineers

Lays the foundation for learning or self-learning more advanced