Jornada de Calidad y Estadística, Universidad de Castilla-La Mancha, campus de Ciudad Real, marzo de 2013

1. StatisticalmethodsforQuality
AnISOstandardsapproachusingR
Emilio L. Cano
Universidad Rey Juan Carlos

2. Introduction
Standardisation process usually unknown.
Except in some areas: Electrical Engineering, Aeronautics,
...
Crossing areas: Statistics, Science ...
Increasing interest (funding €€): FP7, Horizon 2020
∙
∙
∙
∙
2/64

3. Statistics and Quality
Statistical Process Control (SPC)
Control Charts
Capability Analysis
Reliability
Design of Experiments
Six Sigma
...
∙
∙
∙
∙
∙
∙
∙
3/64

4. ISO Standards
4/64

5. ISO Standards
"An ISO standard is developed by a panel of experts, within a
technical committee."
World renowned
Developed by worldwide experts
Voted by all ISO members, from every country
"Standardized" development process
∙
∙
∙
∙
ISO 9000. Something else?∙
5/64

6. ISO Standards
6/64

7. ISO Standards
Often ISO Standards are identified with a quality stamp.
7/64

8. ISO Standards
8/64

9. ISO/TC69
ISO/TC69 web page
Applications of statistical methods
"Standardization in the application of statistical methods,
including generation, collection (planning and design),
analysis, presentation and interpretation of data."
∙
9/64

10. ISO/TC69
SUBCOMMITTEE/WORKING
GROUP
TITLE
TC 69/AHG 1 Documents to support the application of statistical methods standards
TC 69/CAG 1 Chairman Advisory Group
TC 69/WG 3 Statistical interpretation of data
TC 69/SC 1 Terminology and symbols
TC 69/SC 4 Applications of statistical methods in process management
TC 69/SC 5 Acceptance sampling
TC 69/SC 6 Measurement methods and results
TC 69/SC 7 Applications of statistical and related techniques for the implementation of Six Sigma
TC 69/SC 8 Application of statistical and related methodology for new technology and product
development
10/64

11. AENOR
http://www.aenor.es
Miembro español en ISO
Canalizador de la representación española
Miembro también de CEN (normas EN), IEC y CENELEC
Se estructura en Comités Técnicos de Normalización
(CTN)
∙
∙
∙
∙
11/64

12. AENOR: CTN
Seguimiento de Órganos técnicos internacionales o
europeos (comités, subcomités o grupos de trabajo)
Adopción de normas (ISO: UNE­ISO XXX; CEN: UNE­EN
XXX)
Participación en el desarrollo de las normas mediante:
Elaboración de normas nacionales (UNE XXX)
∙
∙
∙
Votación de documentos
Participación en reuniones
­
­
∙
12/64

13. AEN/CTN66
"Normalización en: El campo de la gestión de la calidad en
general incluyendo los sistemas de la calidad, el
aseguramiento de la calidad, [...], así como la normalización
de los métodos estadísticos y de muestreo, la elección de los
métodos de ensayo,[...]"
CTN66: Gestión de la Calidad y evaluación de la
conformidad
AENOR CTN66 website
∙
∙
13/64

14. AEN/CTN66
SUCOMITÉ NOMBRE
SC1 Sistemas de gestión
SC2 Evaluación de la conformidad
SC3 Métodos estadísticos
AENOR CTN66 website
14/64

15. AEN/CTN66/SC3
Métodos Estadísticos
Miembro de ISO/TC 69, Aplicación de los métodos
estadísticos
Funciones: actualización del catálogo de normas
españolas
Identificar normas ISO de interés, traducirlas al español y
adoptarlas como normas UNE­ISO
Participación en la elaboración de normas internacionales
∙
∙
∙
∙
∙
15/64

16. Normas AEN/CTN66/SC3
UNE­ISO 3951­2:2012: Procedimientos de muestreo para
la inspección por variables. Parte 2: Especificación general
para los planes de muestreo simples tabulados según el
NCA para la inspección lote por lote de características de
calidad independientes
UNE­ISO 3951­1:2012 : Procedimientos de muestreo para
la inspección por variables. Parte 1: Especificaciones para
los planes de muestreo simples tabulados según el NCA
para la inspección lote por lote para una característica de
calidad única y un NCA único
∙
∙
16/64

17. Normas AEN/CTN66/SC3
UNE­ISO 13053­2:2012 : Métodos cuantitativos en la
mejora de procesos. Seis Sigma. Parte 2: Herramientas y
técnicas.
UNE­ISO 13053­1:2012 : Métodos cuantitativos en la
mejora de procesos. Seis Sigma. Parte 1: Metodología
DMAIC.
∙
∙
17/64

18. Normas AEN/CTN66/SC3
UNE­ISO 2859­1:2012 : Procedimientos de muestreo para
la inspección por atributos. Parte 1: Planes de muestreo
para las inspecciones lote por lote, tabulados según el
nivel de calidad aceptable (NCA).
UNE­ISO 2859­2:2012 : Procedimientos de muestreo para
la inspección por atributos. Parte 2: Planes de muestreo
para las inspecciones de lotes independientes, tabulados
según la calidad límite (CL).
∙
∙
18/64

19. Normas AEN/CTN66/SC3
UNE­ISO 3534­2:2008 : Estadística. Vocabulario y
símbolos. Parte 2: Estadística aplicada.
UNE­ISO 3534­1:2008 : Estadística. Vocabulario y
símbolos. Parte 1: Términos estadísticos generales y
términos empleados en el cálculo de probabilidades.
UNE­ISO 7870­1:2013 : Gráficos de control. Parte 1:
Directrices generales
∙
∙
∙
19/64

20. The R Project
20/64

21. What is R?
"R is a system for statistical computation and graphics. It
consists of a language plus a run­time environment with
graphics, a debugger, access to certain system functions,
and the ability to run programs stored in script files."
Hornik (2012), “The R FAQ”.
21/64

22. The R Project
22/64

23. The R GUI
23/64

24. RStudio
24/64

25. R Packages
25/64

26. More IDEs / GUIs
RKWard
Deducer
StatET (Eclipse)
EMACS
26/64

27. Other Statistical Software
Minitab
IBM SPSS
SAS
STATA
Statgraphics
http://en.wikipedia.org/wiki/List_of_statistical_packages
27/64

28. Minitab
28/64

29. ! Excel
Excel (TM) is a wonderful tool for many things. Statistics is
not among them
"Perhaps surprising to engineers, Excel's lackluster
performance as a statistics engine has been well­
documented by practicing statisticians for more than a
decade." Charles Annis, P.E.
29/64

30. ! Excel
Microsoft's Excel Might Be The Most Dangerous Software On
The Planet
"No, really, it’s possible that Microsoft‘s Excel is the most
dangerous software on the planet. Yes, more dangerous than
rogue code running a nuclear power plant, than the Stuxnet
that was deliberately sent off to sabotage Iran‘s nuclear
program, worse, even, than whatever rent in the fabric of
space time led to the invention of Lolcats. Really, that
serious." Tim Worstall, Forbes contributor.
30/64

31. A little bit of web scrapping
library(XML)
rssTC69SC4<-"http://www.iso.org/iso/rss.xml?commid=49774&rss=TCbrowse"
doc<-xmlTreeParse(rssTC69SC4)
src<-xpathApply(xmlRoot(doc),"//item")
for(iin1:(length(src))){
if(i==1){
foo<-xmlSApply(src[[i]],xmlValue)
DATA<-data.frame(t(foo),stringsAsFactors=FALSE)
}else{
foo<-xmlSApply(src[[i]],xmlValue)
tmp<-data.frame(t(foo),stringsAsFactors=FALSE)
DATA<-rbind(DATA,tmp)
}
}
searchStd<-function(text,df=DATA){
df[grep(text,df$title),"title",drop=FALSE]
}
31/64

32. ISO/TC69/SC4 Standards
grep("^ISO.",DATA[,1],value=TRUE)
## [1]"ISO22514-6:2013-Statisticalmethodsinprocessmanagement--Capabilityandperformance--Part6
## [2]"ISO7870-2-Controlcharts--Part2:Shewhartcontrolcharts"
## [3]"ISO11462-1:2001-Guidelinesforimplementationofstatisticalprocesscontrol(SPC)--Part1:Ele
## [4]"ISO22514-7:2012-Statisticalmethodsinprocessmanagement--Capabilityandperformance--Part7
## [5]"ISO7870-3:2012-Controlcharts--Part3:Acceptancecontrolcharts"
## [6]"ISO7966:1993-Acceptancecontrolcharts"
## [7]"ISO7870-4:2011-Controlcharts--Part4:Cumulativesumcharts"
## [8]"ISO22514-3:2008-Statisticalmethodsinprocessmanagement--Capabilityandperformance--Part3
## [9]"ISO7870-1:2007-Controlcharts--Part1:Generalguidelines"
##[10]"ISO11462-2:2010-Guidelinesforimplementationofstatisticalprocesscontrol(SPC)--Part2:Cat
##[11]"ISO21747:2006-Statisticalmethods--Processperformanceandcapabilitystatisticsformeasuredq
##[12]"ISO22514-1:2009-Statisticalmethodsinprocessmanagement--Capabilityandperformance--Part1
##[13]"ISO7873:1993-Controlchartsforarithmeticaveragewithwarninglimits"
##[14]"ISO7870:1993-Controlcharts--Generalguideandintroduction"
##[15]"ISO8258:1991-Shewhartcontrolcharts"
##[16]"ISO8258:1991/Cor1:1993"
32/64

33. R Tools for ISO Standards
33/64

34. ISO 7870 series - Control charts
cat(paste(searchStd("Controlcharts")[,1],collapse="n"))
##ISO/DIS7870-5-Controlcharts--Part5:Specializedcontrolcharts
##ISO/DIS7870-1-Controlcharts--Part1:Generalguidelines
##ISO7870-2-Controlcharts--Part2:Shewhartcontrolcharts
##ISO7870-3:2012-Controlcharts--Part3:Acceptancecontrolcharts
##ISO7870-4:2011-Controlcharts--Part4:Cumulativesumcharts
##ISO7870-1:2007-Controlcharts--Part1:Generalguidelines
##ISO7873:1993-Controlchartsforarithmeticaveragewithwarninglimits
##ISO7870:1993-Controlcharts--Generalguideandintroduction
34/64

35. qccPackage
library(qcc)
data(pistonrings)
diameter<-qcc.groups(pistonrings$diameter,pistonrings$sample)
qccobj<-qcc(diameter[1:25,],type="xbar",newdata=diameter[26:40,],
confidence.level=0.99,plot=FALSE)
35/64

36. qccPackage
summary(qccobj)
##
##Call:
##qcc(data=diameter[1:25,],type="xbar",newdata=diameter[26:40, ],confidence.level=0.99,plot
##
##xbarchartfordiameter[1:25,]
##
##Summaryofgroupstatistics:
## Min.1stQu. Median Mean3rdQu. Max.
## 74 74 74 74 74 74
##
##Groupsamplesize: 5
##Numberofgroups: 25
##Centerofgroupstatistics: 74
##Standarddeviation: 0.009785
##
##Summaryofgroupstatisticsindiameter[26:40,]:
## Min.1stQu. Median Mean3rdQu. Max.
## 74 74 74 74 74 74
36/64

37. qccPackage
plot(qccobj)
37/64

38. ISO 22514 series - Capability
and performance
cat(paste(sub(pattern="-Statisticalmethodsinprocessmanagement--Capabilityandperformance--",
"...",searchStd("Capabilityand")[,1]),collapse="n"))
##ISO/CD22514-8...Part8:Machineperformanceofamulti-stateproductionprocess
##ISO/FDIS22514-2...Part2:Processcapabilityandperformanceoftime-dependentprocessmodels
##ISO22514-6:2013...Part6:Processcapabilitystatisticsforcharacteristicsfollowingamultivariateno
##ISO22514-7:2012...Part7:Capabilityofmeasurementprocesses
##ISO22514-3:2008...Part3:Machineperformancestudiesformeasureddataondiscreteparts
##ISO22514-1:2009...Part1:Generalprinciplesandconcepts
##ISO/TR22514-4:2007...Part4:Processcapabilityestimatesandperformancemeasures
38/64

39. SixSigmaPackage
library(SixSigma)
ss.ca.cp(ss.data.ca$Volume,740,760)
##[1]1.584
ss.ca.cpk(ss.data.ca$Volume,740,760)
##[1]1.547
ss.ca.z(ss.data.ca$Volume,740,760)
##[1]3.14
39/64

40. SixSigmaPackage
ss.study.ca(ss.data.ca$Volume,rnorm(40,753,3),LSL=740,USL=760,T=750,
alpha=0.05,f.sub="WineryProject")
40/64

41. SixSigmaPackage
41/64

42. ISO 18414, 13448 - Acceptance
Sampling (SC5)
cat(paste(searchStd("Acceptance",df=DATA5)[,1],collapse="n"))
##ISO18414:2006-Acceptancesamplingproceduresbyattributes--Accept-zerosamplingsystembasedoncred
##ISO14560:2004-Acceptancesamplingproceduresbyattributes--Specifiedqualitylevelsinnonconforming
42/64

43. AcceptanceSampling
Package
library(AcceptanceSampling)
##Astandardbinomialsamplingplan
x<-OC2c(10,1)
x ##printoutabriefsummary
##AcceptanceSamplingPlan(binomial)
##
## Sample1
##Samplesize(s) 10
##Acc.Number(s) 1
##Rej.Number(s) 2
plot(x,xlim=c(0,0.5)) ##plottheusefulpartoftheOCcurve
43/64

44. AcceptanceSampling
Package
plot(x,xlim=c(0,0.5)) ##plottheusefulpartoftheOCcurve
44/64

45. ISO 13053 series - Six Sigma
(SC7)
cat(paste(searchStd("SixSigma",df=DATA7)[,1],collapse="n"))
##ISO/CD17258-BenchmarkforSixSigmamethod
##ISO/WD18404-CompetenciesforkeypersonnelinrelationtoSixSigmaandLeanimplementation
##ISO13053-1:2011-Quantitativemethodsinprocessimprovement--SixSigma--Part1:DMAICmethodology
##ISO13053-2:2011-Quantitativemethodsinprocessimprovement--SixSigma--Part2:Toolsandtechnique
45/64

46. SixSigmaPackage
example(ss.pMap)
46/64

47. SixSigmaPackage
ss.rr(time1,prototype,operator,data=ss.data.rr,sub="SixSigmaPaperHelicopterProject")
##AnalysisofVarianceTable
##
##Response:var
## DfSumSqMeanSqFvaluePr(>F)
##part 2 1.201 0.600 28.04 3e-06
##appr 2 0.053 0.026 1.24 0.31
##part:appr 4 0.083 0.021 0.97 0.45
##Repeatability18 0.385 0.021
##
##GageR&R
## VarComp%Contrib
##TotalGageR&R 0.0220358 25.50
## Repeatability 0.0214111 24.77
## Reproducibility0.0006247 0.72
## appr 0.0006247 0.72
## part:appr 0.0000000 0.00
##Part-To-Part 0.0643901 74.50
##TotalVariation 0.0864259 100.00
47/64

48. SixSigmaPackage
ss.rr(time1,prototype,operator,data=ss.data.rr,sub="SixSigmaPaperHelicopterProject")
48/64

49. Further R packages and
functions
Design of Experiments (task view)
lm, glm, anova, ...
Any type of chart
Probability distributions (task view)
Time series
...
∙
∙
∙
∙
∙
∙
49/64

50. Statistics?
50/64

51. Statistics?
Statistics
Data Analysis
Statistical Computing
Data Mining
Data Science
Big Data
∙
∙
∙
∙
∙
∙
Data Scientist :­)∙
51/64

52. Opportunities
For Today’s Graduate, Just One Word: Statistics
“I keep saying that the sexy job in the next 10 years will be
statisticians,” said Hal Varian, chief economist at Google.
“And I’m not kidding.” The New York times, 2009.
52/64

53. Opportunities
Data Scientist: The Sexiest Job of the 21st Century
" ... the “data scientist.” It’s a high­ranking professional with
the training and curiosity to make discoveries in the world of
big data ...". Harvard Business Review, October 2012
53/64

54. Opportunities
Gartner Sees 4.4M Big Data Jobs by 2015
"Gartner Inc. says worldwide IT spending will increase 3.8
percent in 2013 to reach $3.7 trillion, and that excitement for
big data is leading the way. By 2015, the analyst and market
research firm says, 4.4 million jobs will be created to support
big data." Information Management, October 2012
54/64

55. Opportunities
Lack of data scientists could derail big data projects: IBM
"Analysis of customer data is being used for competitive
advantage by companies around the world, but a high
number of vacancies for data scientists, particularly in the
US, could mean challenges ahead as more companies look
to use big data, according to the findings of a new IBM
survey." CIO, October 2012
55/64

56. Opportunities
Son las matemáticas, estúpido
"La economía del conocimiento exige una educación
sustentada en tres fundamentos: un nivel avanzado en
matemática y estadística, una capacidad elevada para
escribir un argumento y un nivel avanzado de inglés" El País,
Noviembre 2012
56/64

57. EADAPU
57/64

58. useR! 2013
58/64

59. Six Sigma with R
59/64

60. MOOCs
(Massive open online courses)
Coursera
Miriadax
Más recursos (cursos, libros, etc.)
∙
∙
∙
60/64

61. International Year of Statistics
61/64

62. Yes, Statistics!
(Science : Technical) Degree + Data Analysis = Success∙
∙
62/64

63. Acknowledgements
AENOR CTN66/SC3
Javier M. Moguerza
Mariano Prieto
Springer
63/64

64. Discussion
Thanks for your attention !
Questions?
emilio.lopez@urjc.es
64/64