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Abstract Details
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Activity Number:
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644
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Type:
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Invited
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Education
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| Abstract - #300421 |
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Title:
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Training Statisticians to Solve Large, Complex, Unstructured Problems
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Author(s):
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Roger Hoerl*+ and Jijay Nair*+ and Geoffrey Vining*+ and Michael Longnecker*+
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Companies:
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GE Global Research and University of Michigan and Virginia Tech and Texas A & M University
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Address:
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Applied Statistics Laboratory, Niskayuna, NY, 12309, USA , , , , , , , , ,
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Keywords:
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Statistical Education ;
Large Problems ;
Complex Issues ;
Statistical Engineering ;
Statistical Thinking ;
Problem Solving Frameworks
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Abstract:
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The use of statistical thinking and methods in the solution of large, complex, unstructured problems is not typically addressed in text books, or taught at colleges and universities. The future of statisticians working in business and industry lies in finding workable solutions to very complex and unstructured problems. The profession needs to focus on theoretically-sound frameworks to attack such problems that do not coincide with a recognizable textbook chapter and require creative thought and multiple statistical techniques to solve. Unfortunately, standard university curricula fail to address this issue. As a result, students often have trouble dealing with complex problems and finding reasonable, workable, solutions.
The panelists will discuss the experiences of their organizations in education and training of statisticians to address and solve complex, unstructured problems. An overview o
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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