Happy’s Essential Skills: The Need for Total Quality Control (Six Sigma and Statistical Tools), Part 2


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Happy_fig2.JPG 

Figure 2:  Four main techniques are basic to selecting the right statistical tool for problem analysis.

SEMATECH/NIST e-Handbook of Statistical Methods

I was looking for Weibull Reliability Plot information and the SEMATECH/NIST e-Handbook of Statistical Methods (also called the Engineering Statistics e-Handbook) from the National Institute of Standards & Technology (NIST) popped up. After playing with it a while, I discovered it was designed for just us process engineers. The organization of the handbook follows the statistical tools that a process engineer would need to ferret out a problem; measure the extent of the problem; look for root causes; uncover how many factors are involved in the problem; postulate a solution; verify the solution; and then monitor the process to be sure the problem is gone and does not reappear. These preceding eight stages form the main chapters of the handbook (Figure 3).

Happy_fig3.JPGFigure 3:  The NIST/SEMATECH e-Handbook of Statistical Methods is available on the Internet.

The usefulness of this handbook comes from the experimental data sets supplied in it (case studies). As you read, you are encouraged to make comparisons with its statistical tools. It does this by supplying a complete statistical software program for you to use: Dataplot. When used with the data sets supplied, it then coaches you through the interpretation of the results. You can substitute your own data and look at the results. This “First we run the demo and then we run your problem” system is a very effective way to coach a person through the statistical tools.

NIST has prepared version of the Dataplot software[3] for nearly all computers’ operating systems: Windows, NT, UNIX, MAC OS, etc. It is a bit large, but the download time is worth it.

Handbook Integrated with the Software

The majority of the sample output, graphics, and case studies were generated with Dataplot. This aspect does not require you to have Dataplot or know anything about it. Most of the sample output and graphics could have been created with any general-purpose statistical program.

The case studies contain a "Work this Example Yourself" section that is implemented using Dataplot. That is, you click on a link that starts Dataplot and executes a pre-existing Dataplot macro. Dataplot is run in a separate window, so you can see the handbook pages and the Dataplot output together. Once Dataplot is running, you can also generate your own commands in addition to running the handbook generated macros. This aspect of the integration requires you to have Dataplot installed on your local system.

Dataplot can access the handbook as an on-line help system. This complements the normal Dataplot on-line help in that the handbook accesses descriptions of the statistical techniques where the regular on-line help is focused on how the technique is implemented in Dataplot.

Correlation Plots and Curve Fitting

One of the Six-Sigma (TQC) Tools is Correlation Plots and Curve Fitting/Regression. Curve fitting is the process of constructing a curve, or mathematical function that has the best fit to a series of data points, possibly subject to constraints. Curve fitting can involve either interpolation, where an exact fit to the data is required, or smoothing, in which a "smooth" function is constructed that approximately fits the data.

There is a very good shareware software for this called CurveExpert 1.4[4], authored by Daniel Hyams. The author asks serious users to register the software. It is a comprehensive curve fitting system for Windows. XY data can be modelled using a toolbox of three (linear, quadratic & polynomials to the 16th order) linear regression models, nonlinear regression models (exponential, modified exponential, logarithmic, reciprocal logarithm, vapor pressure, power, modified power, shifted power, geometric, modified geometric, root, Hoerl, modified Hoerl, reciprocal, reciprocal quadratic, Bleasdale, Harris, exponential association, saturation growth, Gompertz, logistic, Richards, MMF, weibull, sinusoidal, gaussian, hyperbolic, heat-capacity and rational function), interpolation, or spline, with over 35 models built in.

You can also build custom regression models with the 15 additional models provided (Bacon, Watts, Bent Hyperbola, BET, Beta distribution, Cauchy, Chapman-Richards, Freudlich, Gamma, generalized hyperbola, Gunary, inverse, Langmuir, log normal, Lorentz equation, sum-of-exponentials, truncated fourier series and two-paramenter bell) and compare the fit of various models or let the software pick the best for you. It can be downloaded from a number of Web sites featuring shareware software.

CurveExpert screens are shown in Figure 4.

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Figure 4: CurveExpert screens: a) XY data input with ranking of top 27 models to the XY data; b) graph of the XY data with the regression fit measures of S and r; and c) the regression model, residuals and variables coefficients.

Summary

No matter what you do, learning engineering statistics will be the most useful tool in your bucket. I had the fortunate opportunity to learn it during my chemical engineering coursework and it proved to be the one tool that helped me solve problems (and get promotions).

CLICK HERE TO DOWNLOAD A PDF OF THIS ARTICLE'S FIGURES

References

  1. www.iSixSigma.com
  2. NIST/SEMATECH e-Handbook of Statistical Methods
  3. Dataplot is downloadable by clicking here.
  4. CurveExpert

Happy Holden has worked in printed circuit technology since 1970 with Hewlett-Packard, NanYa/Westwood, Merix, Foxconn and Gentex. He currently is the co-editor, with Clyde Coombs, of the Printed Circuit Handbook, 7th Ed. To contact Holden,click here.

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