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DESIGN OF EXPERIMENTS

DESIGN OF EXPERIMENTS. M. Piczak November 2005. THE ANALYST’S PURPOSES. UNDERSTANDING (a command of general cause and effect relationships associated with a particular phenomenon) EXPLANATION (application of selected relationships to a particular observation)

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DESIGN OF EXPERIMENTS

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  1. DESIGN OF EXPERIMENTS M. Piczak November 2005

  2. THE ANALYST’S PURPOSES • UNDERSTANDING (a command of general cause and effect relationships associated with a particular phenomenon) • EXPLANATION (application of selected relationships to a particular observation) • PREDICTION (extending knowledge of relationships to a future event) • CONTROL (intervening with the intent of either stimulating or preventing a particular event)

  3. PREFERRED PREDICTORS • GYPSIES • BOOKIES • TAROT CARDS • PALM READERS • FARMER’S ALMANAC • HOROSCOPE • FORTUNE COOKIES • EQUATIONS

  4. POINT PREDICTION VERSUS GENERAL STATEMENTS

  5. NARROWING THE KNOWLEDGE GAP

  6. THE DOE OBJECTIVE: • To establish a causal link between selected independent variables X’s and particular dependent Y variables • To isolate or disentangle the independent effect being exerted on a response variable

  7. POINT OF DEPARTURE • Ho: That there exists no relationship between the X and Y variable (assumes that the value of the coefficient for Xi = 0) • Ha: That there exists a relationship between the X and Y variable The challenge is to disprove Ho and thus, accept Ha

  8. OPTIONS PRESENT THEMSELVES • REGRESSION METHODS • TAGUCHI METHODS • SHAININ METHODS • 6 SIGMA METHODS • CLASSIC DESIGN OF EXPERIMENTS

  9. SOUND EXPERIMENTAL PROCEDURE • Start with a uniformity, regularity or anomaly worthy of examination (See ‘What is Worth Studying’) • Establish the research questions • Undertake a literature review to identify other key explanatory variables (See ‘Weird Predictors’) • Explicate the relevant theory for each X variable as it relates to Y, a priori • Establish the measures for X and Y • Choose an experimental design using Minitab • Execute the experiment • Gather the data • Analyze the data • Draw conclusions • Repeat as necessary to cumulate knowledge

  10. LEARNING WITH AN EXAMPLE F = M * A Where: F = Force M = Mass A = Acceleration

  11. SETTING UP THE DATA F = M * A

  12. ADDING THE Y DATA F = M * A

  13. CALCULATING COEFFICIENTS F = M * A

  14. STAT, DOE, FACTORIAL, CREATE FACTORIAL DESIGN

  15. WITH PERFECT RESULTS

  16. STAT, DOE, FACTORIAL, ANALYZE FACTORIAL DESIGN

  17. WILL REGRESSION GIVE THE SAME RESULTS? CODED DATA

  18. RUNNING UNCODED ALL X’s

  19. RUNNING F = M * A ALONE

  20. THE THEORY Thus, the actual model:

  21. TRANSFORMING THE CODED MODEL INTO ACTUAL Ma & Mc   or Ma = 7.5 + 2.5Mc Therefore

  22. RELATIONSHIP BETWEEN CODED & UNCODED VALUES

  23. SIMILARLY FOR ACCELERATION or Aa = 150 + 50 Ac Therefore

  24. SUBSTITUTING TERMS F = M * A

  25. “TA DA”…F = M * A

  26. BUT THE ANSWER WAS THERE THE WHOLE TIME…WHERE IS IT?

  27. OTHER RESOURCES • http://www.airacad.com/PaperDOE.aspx • http://www.statease.com/pubs/popcorn.pdf • http://thequalityportal.com/q_know02.htm • http://www.hq.nasa.gov/office/hqlibrary/ppm/ppm35.htm • http://www.isixsigma.com/tt/doe/

  28. DESIGN OF EXPERIMENTS M. Piczak November 2005 THE END

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