Analyzing and Interpreting Continuous Data Using JMP: A Step-by-Step Guide

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SAS Institute, 2009 - Computers - 492 pages
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Annotation Based on real-world applications, Analyzing and Interpreting Continuous Data Using JMP: A Step-by-Step Guide, written by Jose Ramirez and Brenda Ramirez, combines statistical instructions with a powerful and popular software platform to solve common problems in engineering and science. In the many case studies provided, the authors clearly set up the problem, explain how the data were collected, show the analysis using JMP, interpret the output in a user-friendly way, and then draw conclusions and make recommendations. This step-by-step format enables users new to statistics or JMP to learn as they go, but the book will also be helpful to those with some familiarity with statistics and JMP.
 

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Contents

Characterizing the Measured Performance of a Material Process or Product
81
Comparing the Measured Performance of a Material Process or Product to a Standard
151
Comparing the Measured Performance of Two Materials Processes or Products
217
Comparing the Measured Performance of Several Materials Processes or Products
291
Characterizing Linear Relationships between Two Variables
369
Index
461
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About the author (2009)

José G. Ramírez, Ph.D., is an industrial statistician working closely with engineers and scientists to help them "make sense of data," through collaborative education, promoting statistical thinking, and JMP usage. He has been using JMP and SAS for more than fifteen years as a catalyst to turn data into knowledge. He received a degree in mathematics from Universidad Simón Bolívar in Caracas, Venezuela, and an M.S. in applied statistics and a Ph.D. in statistics, both from the University of Wisconsin at Madison, where he was one of the founding members of the Center for Quality and Productivity Improvement. In 1998, at SUGI 23, he won the best contributed statistics paper, and in 2002 he was awarded the SAS User Feedback Award.

Brenda S. Ramírez, M.S., is an industrial statistician with more than fifteen years of experience working in the chemical and semiconductor industries. In this role, Brenda partners with engineers and scientists to help bring new products to market, sustain manufacturing operations, and guide process improvements through the union of science and statistics. She also has years of experience driving continuous improvement initiatives, like Six Sigma and Quality Function Deployment (QFD). Brenda received an M. S. in applied statistics from Worcester Polytechnic Institute and an M.S. in industrial and management Engineering from Rensselaer Polytechnic Institute. She has been an avid user of statistical software from SAS Institute since 1992.

 

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