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Statistical Tools for Multivariate Six Sigma

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aldan
44 diapositives · 603 Views · Oct 20, 2014

This presentation by Dr. Neil W. Polhemus outlines the critical need for multivariate statistical techniques in Six Sigma quality analysis. It addresses how the performance of an item or service often relies on multiple interdependent characteristics. The talk emphasizes the use of advanced tools including multivariate capability analysis, control charts, regression, and dimensionality reduction methods like PCA and PLS. Practical examples illustrate the importance of considering multiple characteristics together to correctly assess overall performance and optimize processes effectively.

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  1. Statistical Tools for Multivariate Six Sigma • Dr. Neil W. Polhemus • CTO & Director of Development • StatPoint, Inc. • Revised talk: • www.statgraphics.com\documents.htm

  2. The Challenge • The quality of an item or service usually depends on more than one characteristic. • When the characteristics are not independent, considering each characteristic separately can give a misleading estimate of overall performance.

  3. The Solution • Proper analysis of data from such processes requires the use of multivariate statistical techniques.

  4. Important Tools • Statistical Process Control • Multivariate capability analysis • Multivariate control charts • Statistical Model Building* • Data Mining - dimensionality reduction • DOE - multivariate optimization • * Regression and classification.

  5. Example #1 • Textile fiber • Characteristic #1: tensile strength (115.0 ± 1.0) • Characteristic #2: diameter (1.05 ± 0.01)

  6. Individuals Charts

  7. Capability Analysis (each separately)

  8. Scatterplot

  9. Multivariate Normal Distribution

  10. Control Ellipse

  11. Multivariate Capability Determines joint probability of being within the specification limits on all characteristics.

  12. Mult. Capability Indices • Defined to give the • same DPM as in the • univariate case.

  13. More than 2 Variables

  14. Hotelling’s T-Squared • Measures the distance of each point from the centroid of the data (or the assumed distribution).

  15. T-Squared Chart

  16. T-Squared Decomposition

  17. Statistical Model Building • Defining relationships (regression and ANOVA) • Classifying items • Detecting unusual events • Optimizing processes • When the response variables are correlated, it is important to consider the responses together. • When the number of variables is large, the dimensionality of the problem often makes it difficult to determine the underlying relationships.

  18. Example #2

  19. Matrix Plot

  20. Multiple Regression

  21. Reduced Models MPG City = 29.9911 - 0.0103886*Weight + 0.233751*Wheelbase (R2=73.0%) MPG City = 64.1402 - 0.054462*Horsepower - 1.56144*Passengers - 0.374767*Width (R2=64.3%)

  22. Dimensionality Reduction • Construction of linear combinations of the variables can often provide important insights. • Principal components analysis (PCA) and principal components regression (PCR): constructs linear combinations of the predictor variables X that contain the greatest variance and then uses those to predict the responses. • Partial least squares (PLS): finds components that minimize the variance in both the X’s and the Y’s simultaneously.

  23. Principal Components Analysis

  24. Scree Plot

  25. Component Weights C1 = 0.377*Engine Size + 0.292*Horsepower + 0.239*Passengers + 0.370*Length + 0.375*Wheelbase + 0.389*Width + 0.360*U Turn Space + 0.396*Weight C2 = -0.205*Engine Size – 0.593*Horsepower + 0.731*Passengers + 0.043*Length + 0.260*Wheelbase – 0.042*Width – 0.026*U Turn Space – 0.030*Weight

  26. Interpretation

  27. PC Regression

  28. Contour Plot

  29. PLS Model Selection

  30. PLS Coefficients • Selecting to extract 3 components:

  31. Interpretation

  32. Neural Networks

  33. Bayesian Classifier

  34. Classification

  35. Design of Experiments • When more than one characteristic is important, finding the optimal operating conditions usually requires a tradeoff of one characteristic for another. • One approach to finding a single solution is to use desirability functions.

  36. Example #3 • Myers and Montgomery (2002) describe an experiment on a chemical process (20-run central composite design):

  37. Optimize Conversion

  38. Optimize Activity

  39. Desirability Functions • Maximization

  40. Desirability Functions • Hit a target

  41. Combined Desirability • di = desirability of i-th response given the settings of the m experimental factors X. • D ranges from 0 (least desirable) to 1 (most desirable).

  42. Desirability Contours • Max D=0.959 at time=11.14, temperature=210.0, and catalyst = 2.20.

  43. Desirability Surface

  44. References • Johnson, R.A. and Wichern, D.W. (2002). Applied Multivariate Statistical Analysis. Upper Saddle River: Prentice Hall.Mason, R.L. and Young, J.C. (2002). • Mason and Young (2002). Multivariate Statistical Process Control with Industrial Applications. Philadelphia: SIAM. • Montgomery, D. C. (2005). Introduction to Statistical Quality Control, 5th edition. New York: John Wiley and Sons. • Myers, R. H. and Montgomery, D. C. (2002). Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 2nd edition. New York: John Wiley and Sons. • Revised talk: www.statgraphics.com\documents.htm