Teaching Statistics In Quality Science Using The R · PDF fileA Students Example...

Introduction To QualityThe qualityTools Package
Contents Of The R-CourseResume
Teaching Statistics In Quality Science UsingThe R-Package qualityTools
Thomas Roth, Joachim Herrmann
The Department of Quality Science - Technical University of Berlin
July 21, 2010
Thomas Roth, Joachim Herrmann Teaching Statistics In Quality Science Using The R-Package qualityTools

Outline1 Introduction To Quality
Quality And Quality ManagementProcess-Model For Continual ImprovementStatistics In Problem Solving
2 The qualityTools PackageScope Of The qualityTools Package (S4)Overview Of Methods Within The qualityTools Package
3 Contents Of The R-CourseContents And Teaching MethodologyImprovement ProjectA Students Example
ProjectCharterProcess CapabilityDesign Of Experiments
4 ResumeOpinions Regarding The ContentsOpinions Regarding RSummary

A (Very) Short Introduction To Quality Sciences
quality
degree to which a set of inherent characteristics fulfils requirements
management
coordinated activities to direct and control
quality management system
to direct and control an organization with regard to quality

Process-based Quality Management System For ContinualImprovement (EN ISO 9001:2008)
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Management
responsibility
Resource
management
Input OutputProduct
Measurement,
analysis and
improvement
Continual improvement of
the quality management system
Product
realization
Value-adding activities
Information flow

The Role Of Statistics In The Field Of Quality
Process Capability
Pareto Chart
Desirabilities
Hypothesis Test
Quality Control Charts
CorrelationMulti Vari Chart Probability Plot
Problem 1: engineers dislike statisticsor engineers fail to see applications
Solution: exchange statistics with dataanalysis and problem solving
Problem 2: statistics comprise tomuch calculations
Solution: Use R with all its favorableaspects
Use R to keep the focus onmethods rather than calculation
Use R as a software that isavailable on all platforms
Use R to visualize important keyconcepts by simulation

Scope Of The qualityTools Package (S4)Overview Of Methods Within The qualityTools Package
Scope Of The qualityTools Package
Accessibility
give access to the most relevant subset of methods frequently used in industry
DMAIC Driven Toolbox
provide a complete toolbox for the statistical part of the Six Sigma Methodology
Ease of Use
support an intuitive approach to these methods i.e. consequent implementation ofgeneric methods (show, print, plot, summary, as.data.frame, nrow, . . .)
S4 OOP
Accessor and Replacement functions as well as Validity functions i.e. check thevalidity of instances of a class
Powerful Visualization
provide powerful visualization that are easy to accomplish

Scope Of The qualityTools Package (S4)Overview Of Methods Within The qualityTools Package
Visual Representation Of The qualityTools Package
N = 14k = 2p = 0.centerPointsCube: 3Axial: 3
2 3 4 5 5 6 6 7 7number of factors k
1
2
3
5
9
17
num
ber o
f blo
cks
C = AB
2III(31)
D = ABC
2IV(41)
E = ACD = AB
2III(52)
F = BCE = ACD = AB
2III(63)
G = ABCF = BCE = ACD = AB
2III(74)
E = ABCD
2V(51)
F = BCDE = ABC
2IV(62)
G = ACDF = BCDE = ABC
2IV(73)
H = ABDG = ABCF = ACDE = BCD
2IV(84)
J = ABCDH = ABDG = ACDF = BCDE = ABC
2III(95)
K = ABJ = ABCDH = ABDG = ACDF = BCDE = ABC2III
(106)
L = ACK = ABJ = ABCDH = ABDG = ACDF = BCDE = ABC
2III(117)
F = ABCDE
2VI(61)
G = ABDEF = ABCD
2IV(72)
H = BCDEG = ABDF = ABC
2IV(83)
J = ABCEH = ABDEG = ACDEF = BCDE
2IV(94)
K = BCDEJ = ACDEH = ABDEG = ABCEF = ABCD
2IV(105)
L = ADEFK = AEFJ = ACDH = CDEG = BCDF = ABC2IV
(116)
G = ABCDEF
2VII(71)
H = ABEFG = ABCD
2V(82)
J = CDEFH = ACEFG = ABCD
2IV(93)
K = ABCEJ = ABDEH = ACDFG = BCDF
2IV(104)
L = ADEFK = BDEFJ = ABFH = ABCDG = CDE
2IV(115)
H = ABCDEFG
2VIII(81)
J = BCEFGH = ACDFG
2VI(92)
K = ACDFJ = BCDEH = ABCG
2V(103)
L = ABCDEFGK = ACDFJ = BCDEH = ABCG
2V(114)
num
ber o
f run
s N
number of variables k3 4 5 6 7 8 9 10 11
48
1632
6412
80.
81.
0
Half-Normal plot for effects of fdo
A:B:C
A:B
p > 0.1p < 0.05
Lenth Plot of effects
0.6
0.686ABCD
Standardized main effects and interactions
40
50
41.461
ABC
winglengthbodylengthcut
Standardized main effects and interactions
40
50
41.461
ABC
winglengthbodylengthcut
0.0 0.5 1.0
0.0
0.2
0.4
0.6
Effects
Theo
retic
al Q
uant
iles
B
A
B:C
A:C
C
A B C D
A:B
A:C
B:C
y
0.0
0.2
0.4
0.113ME
0.271SME
0.017 0.007 0.023
A B C
A:B
B:C
A:B
:C
A:C
fligh
ts
-20
-10
0
10
20
30
-16.503
4.656
-2.719 -1.438
0.653 0.077
-2.228
2.228
A B C
A:B
B:C
A:B
:C
A:C
fligh
ts
0
10
20
30
40
-16.503
4.656-2.719
-1.438 0.653 0.077 2.228
A: winglength B: bodylength C: cut
Main Effect Plot for fdo
A
-1
-1 1
A
-1 7.0
-1 1
Interaction plot for fdo
> -2> -1.5> -1
1.5
Filled Contour for yRespone Surface for y
> -2> -1.5> -1
-200 -100 0 100 200 300 400
0.00
00.
002
0.00
40.
006
0.00
8 LSL USLTARGET
Process Capability using normal distribution for x
x = 94.302 Nominal Value =

Teaching Statistics In Quality Science Using The R · PDF fileA Students Example...

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