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Visual Six Sigma : Making Data Analysis Lean by Ian, Ramsey, Philip J., Wright, Leo, Gaudard, Marie A., Stephens, Mia L. Cox - Ian, Ramsey, Philip J., Wright, Leo, Gaudard, Marie A., Stephens, Mia L. Cox
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Ian, Ramsey, Philip J., Wright, Leo, Gaudard, Marie A., Stephens, Mia L. Cox:

Visual Six Sigma : Making Data Analysis Lean by Ian, Ramsey, Philip J., Wright, Leo, Gaudard, Marie A., Stephens, Mia L. Cox - livre d'occasion

ISBN: 9780470506912

Through Visual Six Sigma, learn what your data is telling your business In the typical business environment of process improvement, you want simple-to-use tools that everyone can use at a… Plus…

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Cox, Ian, Ramsey, Philip J., Wright, Leo, Gaudard, Marie A., Stephens, Mia L:

Visual Six Sigma : Making Data Analysis Lean - livre d'occasion

ISBN: 9780470506912

Wiley & Sons, Incorporated, John. Used - Very Good. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor… Plus…

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Six Sigma (SAS) (Wiley and SAS Business Series) - Cox
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Cox:
Six Sigma (SAS) (Wiley and SAS Business Series) - edition reliée, livre de poche

2009

ISBN: 0470506911

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Cox, Ian, Gaudard, Marie A., Ramsey, Philip J., Stephens, Mia L., Wright, Leo:
Visual Six Sigma: Making Data Analysis Lean - edition reliée, livre de poche

2009, ISBN: 0470506911

[EAN: 9780470506912], Gebraucht, [PU: Wiley (edition 1)], Heavy wear. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported, Books

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Six Sigma (SAS): Making Data Analysis Lean (Wiley and SAS Business Series) - edition reliée, livre de poche

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Détails sur le livre
Visual Six Sigma: Making Data Analysis Lean (SAS Institute Inc)
Auteur:
Titre:
ISBN:

9780470506912


Because of its unique visual emphasis, Visual Six Sigma opens the doors for you to take an active role in data-driven decision making, empowering you to leverage your contextual knowledge to pose relevant questions and make sound decisions. This book shows you how to leverage dynamic visualization and exploratory data analysis techniques to: * See the sources of variation in your data * Search for clues in your data to construct hypotheses about underlying behavior * Identify key drivers and models * Shape and build your own real-world Six Sigma experience Whether you work involves a Six Sigma improvement project, a design project, a data-mining inquiry, or a scientific study, this practical breakthrough guide equips you with the strategies, process, and road map to put Visual Six Sigma to work for your company. Broaden and deepen your implementation of Visual Six Sigma with the intuitive and easy-to-use tools found in Visual Six Sigma: Making Data Analysis Lean.

Informations détaillées sur le livre - Visual Six Sigma: Making Data Analysis Lean (SAS Institute Inc)


EAN (ISBN-13): 9780470506912
ISBN (ISBN-10): 0470506911
Version reliée
Date de parution: 2009
Editeur: John Wiley & Sons
492 Pages
Poids: 1,021 kg
Langue: eng/Englisch

Livre dans la base de données depuis 2009-09-17T13:58:08+02:00 (Zurich)
Page de détail modifiée en dernier sur 2024-03-11T15:19:37+01:00 (Zurich)
ISBN/EAN: 9780470506912

ISBN - Autres types d'écriture:
0-470-50691-1, 978-0-470-50691-2
Autres types d'écriture et termes associés:
Auteur du livre: stephen cox, ramsey, wright, ian stephens, gaudard, leo per, marie
Titre du livre: six sigma, lean sigma, inglés visual, data, analysis, mia

Données de l'éditeur

Auteur: Ian Cox; Marie A. Gaudard; Philip J. Ramsey; Mia L. Stephens; Leo Wright
Titre: SAS Institute Inc; Visual Six Sigma - Making Data Analysis Lean
Editeur: John Wiley & Sons
504 Pages
Date de parution: 2010-01-12
Poids: 1,014 kg
Langue: Anglais
61,90 € (DE)
Not available, replaced by new product
186mm x 257mm x 44mm

BB; gebunden; Hardcover, Softcover / Wirtschaft/Management; Wirtschaftsmathematik und -informatik, IT-Management; Business Technology; Wirtschaft u. Management; Business & Management; Unternehmenstechnologie; Unternehmenstechnologie

Preface. Acknowledgements. Part I: Background. Chapter 1 Introduction. What Is Visual Six Sigma? Moving Beyond Traditional Six Sigma. Making Data Analysis Lean. Requirements of the Reader. Chapter 2 Six Sigma and Visual Six Sigma. Background: Models, Data, and Variation. Six Sigma. Variation and Statistics. Making Detective Work Easier through Dynamic Visualization. Visual Six Sigma: Strategies, Process, Roadmap, and Guidelines. Conclusion. Notes. Chapter 3 A First Look at JMP. The Anatomy of JMP. Visual Displays and Analyses Featured in the Case Studies. Scripts. Personalizing JMP. Visual Six Sigma Data Analysis Process and Roadmap. Techniques Illustrated in Case Studies. Conclusion. Notes. Part II: Case Studies. Chapter 4 Reducing Hospital Late Charge Incidents. Framing the Problem. Collecting Data. Uncovering Relationships. Uncovering the Hot Xs. Identifying Projects. Conclusion. Chapter 5 Transforming Pricing Management in a Chemical Supplier. Setting the Scene. Framing the Problem: Understanding the Current State Pricing Process. Collecting Baseline Data. Uncovering Relationships. Modeling Relationships. Revising Knowledge. Utilizing Knowledge: Sustaining the Benefits. Conclusion. Chapter 6 Improving the Quality of Anodized Parts. Setting the Scene. Framing the Problem. Collecting Data. Uncovering Relationships. Finding the Team on the VSS Roadmap. Modeling Relationships. Revise Knowledge. Utilizing Knowledge. Conclusion. Notes. Chapter 7 Informing Pharmaceutical Sales and Marketing. Setting the Scene. Collecting the Data. Validating and Scoping the Data. Investigating Promotional Activity. A Deeper Understanding of Regional Differences. Summary. Conclusion. Additional Details. Notes. Chapter 8 Improving a Polymer Manufacturing Process. Setting the Scene. Framing the Problem. Reviewing Historical Data. Measurement Systems Analysis. Uncovering Relationships. Modeling Relationships. Revising Knowledge. Utilizing Knowledge. Conclusion. Note. Chapter 9 Classification of Cells. Setting the Scene. Framing the Problem and Collecting the Data: The Wisconsin Breast Cancer. Diagnostic Data Set. Uncovering Relationships. Constructing the Training, Validation, and Test Sets. Modeling Relationships: Logistic Model. Modeling Relationships: Recursive Partitioning. Modeling Relationships: Neural Net Models. Comparison of Classification Models. Conclusion. Notes. Index.
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