Calidad Seis Sigma con R: Aplicación a la docencia

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Seminario en la Universidad de Castilla-La Mancha, noviembre 2012

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Calidad Seis Sigma con R: Aplicación a la docencia

  1. 1. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesSeis Sigma con REmilio L. CanoDepartamento de Estad´ıstica e Investigaci´on OperativaUniversidad Rey Juan Carlos (Madrid)December 5, 2012E.T.S. Ingenieros IndustrialesUniversidad de Castilla-La ManchaSeminario EIO UCLM 1/66
  2. 2. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesContents1 Statistical TrainingApproachesExamplesApplication2 Six Sigma with RSix SigmaRPackagesSeminario EIO UCLM 2/66
  3. 3. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesElements of Statistical TrainingSeminario EIO UCLM 3/66
  4. 4. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesCopy-paste ApproachInconsistenciesErrorsOut-of-datenon-reproduciblePainful changesSeminario EIO UCLM 4/66
  5. 5. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesReproducible Research ApproachReproducible ResearchThe goal of reproducible research is to tiespecific instructions to data analysis andexperimental data so that scholarship can berecreated, better understood and verifiedLiterate ProgrammingLiterate programming is a methodology thatcombines a programming language with adocumentation languageSeminario EIO UCLM 5/66
  6. 6. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesReproducible ResearchWorkflowSeminario EIO UCLM 6/66
  7. 7. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesSweave DocumentsSweaveA Sweave document is a plain-text file whichmerges LATEX code and R code. The Rfunction Sweave() converts the Sweavedocument (*.Rnw) into a LATEX file (*.tex).The code chunks are executed and the resultsembedded into the LATEX file.Seminario EIO UCLM 7/66
  8. 8. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesSweave Example documentclass [a4paper ]{ article}usepackage{Sweave}title{Design of Experiments}author{EL Cano and JM Moguerza and A Rechuk}begin{document}maketitlesection{ Introduction }Design of experiments is the most important took in the IDMAIC cycle ldots.<<>>=library(SixSigma)doe.model1 <- lm(score ~ flour + salt + bakPow +flour * salt + flour * bakPow +salt * bakPow + flour * salt * bakPow ,data = ss.data.doe1)summary(doe.model1)@This is the general model:begin{equation}label{eq:doe:model}y_{ijkl }=mu+ alpha_i + beta_j + gamma_k +( alphabeta)_{ij}( alphagamma)_{ik }+( betagamma)_{kl }+( alphabetagammaSeminario EIO UCLM 8/66
  9. 9. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesSweave Example (cont.)varepsilon_{ijkl},end{equation}And here we have a plot of effects:<<maineff , echo=FALSE , fig=TRUE >>=plot(c(-1, 1), ylim = range(ss.data.doe1$score),coef(doe.model1 )[1] + c(-1, 1) * coef(doetype ="b", pch =16)abline(h=coef(doe.model1 )[1])@%input{section2}end{document}Seminario EIO UCLM 9/66
  10. 10. Design of ExperimentsEL Cano and JM Moguerza and A RechukApril 10, 20121 IntroductionDesign of experiments is the most important took in the Improve phase of theDMAIC cycle . . . .> library(SixSigma)> doe.model1 <- lm(score ~ flour + salt + bakPow ++ flour * salt + flour * bakPow ++ salt * bakPow + flour * salt * bakPow,+ data = ss.data.doe1)> summary(doe.model1)Call:lm(formula = score ~ flour + salt + bakPow + flour * salt + flour *bakPow + salt * bakPow + flour * salt * bakPow, data = ss.data.doe1)Residuals:Min 1Q Median 3Q Max-0.5900 -0.2888 0.0000 0.2888 0.5900Coefficients:Estimate Std. Error t value Pr(>|t|)(Intercept) 5.5150 0.3434 16.061 2.27e-07 ***flour+ 1.8350 0.4856 3.779 0.005398 **salt+ -0.8350 0.4856 -1.719 0.123843bakPow+ -2.9900 0.4856 -6.157 0.000272 ***flour+:salt+ 0.1700 0.6868 0.248 0.810725flour+:bakPow+ 0.8000 0.6868 1.165 0.277620salt+:bakPow+ 1.1800 0.6868 1.718 0.124081flour+:salt+:bakPow+ 0.5350 0.9712 0.551 0.596779---Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1Residual standard error: 0.4856 on 8 degrees of freedomMultiple R-squared: 0.9565, Adjusted R-squared: 0.9185F-statistic: 25.15 on 7 and 8 DF, p-value: 7.666e-05This is the general model:yijkl = µ + αi + βj + γk + (αβ)ij + (αγ)ik + (βγ)kl + (αβγ)ijk + εijkl, (1)1
  11. 11. And here we have a plot of effects:qq−1.0 −0.5 0.0 0.5 1.034567c(−1, 1)coef(doe.model1)[1]+c(−1,1)*coef(doe.model1)[2]2
  12. 12. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesProject ExampleStrategiesPartial Sweave files can be compiled to getpartial LATEX files. R scripts can Sweave .Rnwfiles and“source”.R files. The final documentis obtained by compiling the“master”LATEX file.> source("code/myoptions.R")> source("code/myfunctions.R")> source("code/mydata.R")> Sweave("rnw/theorem01.Rnw")> Sweave("rnw/lesson01.Rnw")> Sweave("rnw/exercises01.Rnw")> ...> texi2pdf("master.tex")Seminario EIO UCLM 12/66
  13. 13. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesEADAPUSeminario EIO UCLM 13/66
  14. 14. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesEADAPU - ProgramaSesi´on 1 (4 horas)1 Introducci´on a la Metodolog´ıa SeisSigma.a2 Herramientas de la fase de definici´on.3 Herramientas de la fase de medida.aIncluye introducci´on a RStudioSeminario EIO UCLM 14/66
  15. 15. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesEADAPU - Programa (cont.)Sesi´on 2 (4 horas)1 La importancia de experimentar.2 Introducci´on al dise˜no de experimentos.3 Dise˜no de experimentos comoherramienta de mejora.4 Dise˜no robusto.5 Dise˜nos avanzados.Seminario EIO UCLM 15/66
  16. 16. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesContents1 Statistical TrainingApproachesExamplesApplication2 Six Sigma with RSix SigmaRPackagesSeminario EIO UCLM 16/66
  17. 17. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesPublisherhttp://www.springer.com/statistics/book/978-1-4614-3651-5Seminario EIO UCLM 17/66
  18. 18. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesBook websitehttp://www.sixsigmawithr.com/Seminario EIO UCLM 18/66
  19. 19. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesR Packagehttp://cran.r-project.org/web/packages/SixSigma/index.htmlSeminario EIO UCLM 19/66
  20. 20. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesContentsForeword (Thanks David R´ıos)PrefacePart I: BasicsPart II: R Tools for the Define PhasePart III: R Tools for the Measure PhasePart IV: R Tools for the Analyze PhasePart V: R Tools for the Improve PhasePart VI: R Tools for the Control PhasePart VII: Further and BeyondSeminario EIO UCLM 20/66
  21. 21. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages1. Six Sigma in a NutshellHerbert Spencer“Science is organised knowledge”Seminario EIO UCLM 21/66
  22. 22. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages2. R from the BeginningLinus Torvalds“Software is like sex; it’s better when it’s free”Seminario EIO UCLM 22/66
  23. 23. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages3. Process Mapping with RCharles Franklin Kettering“A problem well stated is a problem halfsolved”Seminario EIO UCLM 23/66
  24. 24. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesA Process MapSix Sigma Process MapPaper Helicopter ProjectINPUTSXoperatorstoolsraw materialfacilitiesINSPECTIONINPUTSsheets...Param.(x):width NCoperator CMeasure pattern Pdiscard PFeatur.(y):okASSEMBLYINPUTSsheetsParam.(x):operator Ccut Pfix Protor.width Crotor.length Cpaperclip Ctape CFeatur.(y):weightTESTINPUTShelicopterParam.(x):operator Cthrow Pdiscard Penvironment NFeatur.(y):timeLABELINGINPUTShelicopterParam.(x):operator Clabel PFeatur.(y):labelOUTPUTSYhelicopterLEGEND(C)ontrollable(Cr)itical(N)oise(P)rocedureSeminario EIO UCLM 24/66
  25. 25. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages4. Loss Funtion Analysis with RW. Edwards DemingDefects are not free. Somebody makes them,and gets paid for making themSeminario EIO UCLM 25/66
  26. 26. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesA Loss Function ExampleLoss Function Analysis10 mm. Bolts Project0e+001e−042e−043e−044e−045e−04LSL USLT9.6 9.8 10.0 10.2 10.4Observed ValueCostofPoorQualityL = 0.002 ⋅ (Y − 10)2DataCTQ: diameterY0 = 10∆ = 0.5L0 = 0.001Size = 1e+05Mean = 10.0308k = 0.002MSD = 0.0337Av.Loss = 1e−04Loss = 6.7441Seminario EIO UCLM 26/66
  27. 27. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages5. Measurement System AnalysisLord Kelvin“If you cannot measure it,you cannot improve it.”Seminario EIO UCLM 27/66
  28. 28. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesMSA with RSix Sigma Gage R&R StudyHelicopter ProjectComponents of VariationPercent020406080G.R&R Repeat Reprod Part2Part%Contribution %Study VarVar by Partvar1.01.21.41.61.8prot #1 prot #2 prot #3qqqqqqqqqqqqqqqqqqqqqqqqqqqVar by appraiservar1.01.21.41.61.8op #1 op #2 op #3qqqqqqqqqqqqqqqqqqqqqqqqqqqPart*appraiser Interactionvar1.11.21.31.41.51.61.7prot #1 prot #2 prot #3qqqqqqqqqop #1op #2op #3x Chart by appraiserpartvar1.11.21.31.41.51.61.7prot #1 prot #2 prot #3qqqop #1prot #1 prot #2 prot #3qqqop #2prot #1 prot #2 prot #3qqqop #3R Chart by appraiserpartvar0.10.20.30.40.5prot #1 prot #2 prot #3qqqop #1prot #1 prot #2 prot #3qqqop #2prot #1 prot #2 prot #3qqqop #3Seminario EIO UCLM 28/66
  29. 29. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages6. Pareto Analysis with ROvidioCausa latet: vis est notissima. [The cause ishidden, but the result is known.]Seminario EIO UCLM 29/66
  30. 30. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesCause-and-effect diagramSix Sigma Cause−and−effect DiagramPaper Helicopter ProjectFlight TimeOperatoroperator #1operator #2operator #3EnvironmentheightcleaningToolsscissorstapeDesignrotor.lengthrotor.width2paperclipRaw.MaterialthicknessmarksMeasure.ToolcalibratemodelSeminario EIO UCLM 30/66
  31. 31. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesPareto ChartDelaysMaterialsCustomerTrainingReworkErrorsRainWindPermissionsInadequateTemperaturePareto Chart for b.vectorFrequency0204060qqqqqqqqqqq80%CumulativePercentageSeminario EIO UCLM 31/66
  32. 32. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages7. Process Capability AnalysisJohann Wolfgang von GoetheOne cannot develop taste from what is ofaverage quality but only from the very best.Seminario EIO UCLM 32/66
  33. 33. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesCapability Analysis OutputSix Sigma Capability Analysis StudyWinery ProjectHistogram & DensityLSLTargetUSL740 745 750 755 760Check Normalityqqq qqqqqqqqqqqqqq qqq Shapiro−Wilk Testp−value: 0.07506Lilliefors (K−S) Testp−value: 0.2291Normality accepted when p−value > 0.05Density Lines LegendDensity STTheoretical Dens. STDensity LTTheoretical Density LTSpecificationsLSL: 740Target: 750USL: 760ProcessShort TermMean: 749.7625SD: 2.1042n: 20Zs: 3.14Long TermMean: 753.7239SD: 2.6958n: 40Zs: 2.33DPMO: 9952.5IndicesShort TermCp: 1.5841CI: [1.4,1.7]Cpk: 1.5465CI: [1.4,1.7]Long TermPp: 1.2365CI: [1.1,1.3]Ppk: 0.7760CI: [0.7,0.8]Seminario EIO UCLM 33/66
  34. 34. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages8. Charts with RJohn Tukey“The greatest value of a picture is when itforces us to notice what we never expected tosee.”Seminario EIO UCLM 34/66
  35. 35. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesMulti-vari chartMulti−vari chart for Volume by color and operatorFillerVolume14151617181 2 3qqqqqqqqqqqqB1qqqqqqqqqqqqC1qqqqqqqqqqq qB21415161718qqqqqqqqqqqqC21415161718q q qqq qq qqqqqB31 2 3q q qqqqq qqq q qC3batch1 2 3 4q q q qSeminario EIO UCLM 35/66
  36. 36. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages9. Statistics and Probability with RAaron Levenstein“Statistics are like bikinis. What they reveal issuggestive, but what they conceal is vital.”Seminario EIO UCLM 36/66
  37. 37. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesDistributions0 1 2 3 40.00.3HypergeometricElements in class AProbability0 10 300.000.10GeometricItems extracted until first successProbability10 20 30 400.000.06Negative BinomialNumber of trials until 3 eventsProbability0 5 10 200.000.15PoisonNumber of successful experiments per unitProbability0 1 2 3 4 50.00.6ExponentialRandom Variable XProbabilityDensity0 2 4 60.00.40.8LognormalRandom Variable X>0ProbabilityDensity−0.5 0.5 1.50.00.61.2UniformRandom Variable XProbabilityDensity0 2 4 6 80.00.20.4GammaRandom Variable XProbabilityDensity0.0 0.4 0.80.01.02.0BetaRandom Variable XProbabilityDensity0 2 4 60.00.30.6WeibulRandom Variable XProbabilityDensity−4 0 2 40.00.3t−StudentRandom Variable XProbabilityDensity1.7395%5%10 30 500.000.06Chi−squaredRandom Variable XProbabilityDensity30.1495% 5%0 1 2 3 40.00.6FRandom Variable XProbabilityDensity2.3495%5%Seminario EIO UCLM 37/66
  38. 38. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages10. Statistical Inference with RGeorge E.P. Box“All models are wrong; some models areuseful.”Seminario EIO UCLM 38/66
  39. 39. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesConfidence Interval ExampleConfidence Interval for the MeanMean:StdDev:n:Missing:950.0160.267120095% CI:P−Var:t:[949.967, 950.064]unknown1.98Shapiro−WilksNormality Test0.985p−value: 0.19qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqNormal q−q Plot0.00.51.01.5949.0 949.5 950.0 950.5Value of lendensityHistogram & Density PlotSeminario EIO UCLM 39/66
  40. 40. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages11. Design of Experiments with RR.A. Fisher“Sometimes the only thing you cando with a poorly designedexperiment is to try to find out whatit died of”Seminario EIO UCLM 40/66
  41. 41. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesThe Importance of Experimenting“An engineer who does not knowexperimental design is not anengineer”Comment made by to oneof the authors [of“Statisticsfor experimenters”] by anexecutive of the ToyotaMotor Company.Seminario EIO UCLM 41/66
  42. 42. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages12.Process Control with RWalter A. Shewhart“Special causes of variation may be found andeliminated.”Seminario EIO UCLM 42/66
  43. 43. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesControl Chart Plottingp Chartfor stockoutsGroupGroupsummarystatistics1 3 5 7 9 11 13 15 17 19 210.000.050.100.150.200.25qqqqqqqqqqqqqqqqqqqqqqLCLUCLCLqNumber of groups = 22Center = 0.1212294StdDev = 0.3263936LCL is variableUCL is variableNumber beyond limits = 1Number violating runs = 0Seminario EIO UCLM 43/66
  44. 44. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackages13. Other Tools andMethodologiesJohann Wolfgang von GoetheInstruction does much, but encouragementeverything.Seminario EIO UCLM 44/66
  45. 45. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesOther topicsFailure Mode, Effects, and CriticalityAnalysisDesign for Six SigmaLeanGantt ChartSome Advanced R TopicsSeminario EIO UCLM 45/66
  46. 46. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesCase StudySeminario EIO UCLM 46/66
  47. 47. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesHelicopter Template> ss.heli()null device1> #vignette("HelicopterInstructions") tSeminario EIO UCLM 47/66
  48. 48. Six Sigma with R | Paper Helicopter templatecutfold ↑ fold ↓tape?cutfold↓↓cutfold↑↑cuttape?tape?clip?min(6.5cm)std(8cm)max(9.5cm)←bodylength→← body width →min(4cm)min(4cm)max(6cm)max(6cm)min(6.5cm)std(8cm)max(9.5cm)←wingslength→
  49. 49. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesEnjoy the Case Study!Seminario EIO UCLM 49/66
  50. 50. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesEnjoy the Case Study!Seminario EIO UCLM 49/66
  51. 51. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesBook ProductionReproducible ResearchWritten applying reproducible researchtechniques. All figures (except screencaptures) are generated while compiling thebook using R code.Seminario EIO UCLM 50/66
  52. 52. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesThe DMAIC CycleSeminario EIO UCLM 51/66
  53. 53. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesSix Sigma RolesIn Six Sigma, everyone in the organization hasa role in the project. Six Sigma methodologyuses an intuitive categorization of these roles.Seminario EIO UCLM 52/66
  54. 54. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesSix Sigma RolesIn Six Sigma, everyone in the organization hasa role in the project. Six Sigma methodologyuses an intuitive categorization of these roles.Seminario EIO UCLM 52/66
  55. 55. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesWhy 6 and why SigmaSigma refers to the Z-Score of the process:Z = min(USL − x)σ,(x − LSL)σ; ZLT = ZST −1,5.CTQFrequencyShort TermLong Term1.5σ 4.5σ> (1-pnorm(4.5))*(10^6)[1] 3.397673DPMOSeminario EIO UCLM 53/66
  56. 56. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesSo what?Seminario EIO UCLM 54/66
  57. 57. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesSo what?The Scientific MethodSeminario EIO UCLM 54/66
  58. 58. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesThe Scientific Method and SixSigmaDefineAsk a questionMeasureAnalyzeImproveControlDo some backgroundresearchConstruct a hypothesisTest the hypothesiswith an experimentAnalyze the data anddraw conclusionsCommunicate resultsDMAIC Cycle Scientific MethodSeminario EIO UCLM 55/66
  59. 59. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesThe Key to Success“Six Sigma speaks the language of business”ISO 13053-1:2011Six Sigma methodology is a quality paradigmthat translates the involved scientificmethodology into a simple way to apply thescientific method within every organization.Seminario EIO UCLM 56/66
  60. 60. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesThe R Projecthttp://www.r-project.orgSeminario EIO UCLM 57/66
  61. 61. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesThe R EnvironmentSeminario EIO UCLM 58/66
  62. 62. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesReproducible Researchknitr, pgfSweave: enhanced options forSweaveexams: Automatic generation of printableexamsodfWeave: Open Document formatdocuments generationMore in the“Reproducible Research”TaskView at CRAN.http://cran.r-project.org/web/views/ReproducibleResearch.htmlSeminario EIO UCLM 59/66
  63. 63. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesPackage RGIFTOpen format GIFTIntegration with MoodleAutomatic correctionhttp://cran.r-project.org/web/packages/RGIFT/Seminario EIO UCLM 60/66
  64. 64. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesCommunityCommunity4131 packages at CRAN (18/11/2012)aBioconductor, R-forge, Github,Omegahat.Task viewsManualsPublicationshttp://cran.r-project.org/web/packages/a4181 04/12Seminario EIO UCLM 61/66
  65. 65. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesR InterfacesGUI, IDERStudioEclipse + StatETEMACS + EESDeducer. . .Seminario EIO UCLM 62/66
  66. 66. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesR Interfaces (cont.)Seminario EIO UCLM 63/66
  67. 67. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackageshttp://r-es.org/Seminario EIO UCLM 64/66
  68. 68. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackageshttp://www.r-project.org/useR-2013Seminario EIO UCLM 65/66
  69. 69. Seis Sigma con RDecember, 2012Emilio L. CanoStatistical TrainingApproachesExamplesApplicationSix Sigma with RSix SigmaRPackagesDiscussionThanks !emilio.lopez@urjc.es@emilopezcanohttp://www.sixsigmawithr.comSeminario EIO UCLM 66/66

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