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  • Produktbild: Improving Almost Anything Rev Ed
  • Produktbild: Improving Almost Anything Rev Ed

Improving Almost Anything Rev Ed Ideas and Essays

Aus der Reihe Wiley Series in Probability and Statistics

125,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.04.2006

Herausgeber

George E. P. Box

Verlag

John Wiley & Sons Inc

Seitenzahl

632

Maße (L/B/H)

23,4/15,6/3,4 cm

Gewicht

939 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-471-72755-2

Beschreibung

Rezension

"The author, George Box, presents statistical methods in a way that almost anyone can understand and appreciate...I cannot think of a better book." (Software Quality Professional, December 2006)
 
"...bring[s the reader]...back to the point where scientist morphs into inventor, driven by the insatiable need to make the whole of the process (from design to production) more stable and efficient." (Electric Review, September/October 2006)

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.04.2006

Herausgeber

George E. P. Box

Verlag

John Wiley & Sons Inc

Seitenzahl

632

Maße (L/B/H)

23,4/15,6/3,4 cm

Gewicht

939 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-471-72755-2

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Improving Almost Anything Rev Ed
  • Produktbild: Improving Almost Anything Rev Ed

  • Foreword by (J. Stuart Hunter).

    Friends of George Box.

    My Professional Life.

    PART A: SOME THOUGHTS ON PROCESS AND QUALITY IMPROVEMENT.

    Introduction.

    Good Quality Costs Less? How Come?

    When Murphy Speaks-Listen.

    Changing Management Policy to Improve Quality and Productivity.

    Scientific Method: The Generation of Knowledge.

    PART B: DESIGN OF EXPERIMENTS FOR PROCESS IMPROVEMENT.

    Introduction.

    Do Interactions Matter?

    Teaching Engineers Experimental Design with a Paper Helicopter.

    What Can You Find Out from Eight Experimental Runs?

    What Can You Find Out from Sixteen Experimental Runs?

    What Can You Find Out from Twelve Experimental Runs?

    Sequential Experimentation and Sequential Assembly of Designs.

    Must We Randomize Our Experiment?

    A Simple Way to Deal with Missing Observations from Designed Experiments.

    Finding Bad Values in Factorial Designs.

    How to Get Lucky.

    Dispersion Effects from Fractional Designs.

    The Importance of Practice in the Development of Statistics.

    PART C: SEQUENTIAL INVESTIGATION AND DISCOVERY.

    Introduction.

    A Demonstration of Response Surface Methods.

    Response Surface Methods: Some History.

    Statistics as a Catalyst to Learning.

    Experience as a Guide to Theoretical Development.

    The Invention of the Composite Design.

    Finding the Active Factors in Fractionated Screening Experiments.

    Follow-up Designs to Resolve Confounding in Multifactor Experiments.

    Projective Properties of Certain Orthogonal Arrays.

    Choice of Response Surface Design and Alphabetic Optimality.

    An Apology for Ecumenism in Statistics.

    PART D: CONTROL.

    Introduction.

    Six Sigma, Process Drift, Capability Indices, and Feedback Adjustment.

    Understanding Exponential Smoothing: A Simple Way to Forecast Sales and Inventory.

    Feedback Control by Manual Adjustment.

    Bounded Adjustment Charts.

    Statistical Process Monitoring and Feedback Adjustment-A Discussion.

    Dicrete Proportional-Integral Control with Constrained Adjustment.

    Dicrete Proportional-Integral Adjustment and Statistical Process Control.

    Selection of Sampling Interval and Action Limit for Discrete Feedback Adjustment.

    Use of Cusum Statistics in the Analysis of Data and in Process Monitoring.

    Influence of the Sampling Interval, Decision Limit, and Autocorrelation on the Average Run Length in Cusum Charts.

    Cumulative Score Charts.

    PART E: VARIANCE REDUCTION AND ROBUSTNESS.

    Introduction.

    Multiple Sources of Variation: Variance Components.

    The Importance of Data Transformation in Designd Experiments for Life Testing.

    Is Your Robust Design Procedure Robust?

    Split Plot Experiments.

    Robustness in Statistics.

    Split Plots for Robust Product and Process Experimentation.

    Designing Products that Are Robust to the Experiment-A Response Surfacem Approach.

    An Investigation of the Method of Accumulatin Analysis.

    Signal-to-Noise Ratios, Performance Criteria, and Transformations.

    PART F: SONG.

    There's No Theorem Like Bayes Theorem.

    It's distribution Free.

    I Am the Very Model of a Professor Statistical.

    References.

    Biography.

    Books and Articals Written by George Box from 1982 to 2005.

    Index.