Transcript No Slide Title

JMP 7 and Minitab 15
Thomas A. Little Ph.D. 07/07/07
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Audience
Description
This presentation is designed for those individuals who are
interested in understanding the differences in the design,
function and capabilities of JMP 7 versus Minitab 15.
Particular attention is made to those features and functions
used for Six Sigma/Lean project application.
Software
JMP 7 and Minitab 15.
Limitations
762 North 470 East
American Fork, UT 84003
1-925-285-1847
[email protected]
www.dr-tom.com
This presentation is limited to those features and functions of
greatest interest to users in the scientific, business,
engineering and six sigma/lean communities. An attempt was
made to review the features and functions in both applications
from a user’s perspective. TLC actively consults with both
applications and finds features and functions in both
applications that are best in class. Any disagreements about
observations found in this presentation should be addressed to
the author who welcomes opposing points of view.
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Presentation Outline
Section I
General Interface and Ease of Use
Section II
Lean Six Sigma Activities
Define
Measure
Analyze
Improve
Control
Section III
Extended Capabilities
Section IV
New Features and Conclusions
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JMP Version 7.0 Overview
Power
 JMP provides more analytical tools, graphs, depth, scripting and
features that are used to solve real world problems
 Static and dynamic visualization of data via meaningful graphs and
options. Version 7 added significantly to this capability.
 JMP is particularly good at large data sets and multivariate modeling
 JMP benefits from SAS’s core capabilities and years of development
 JMP version 7 improves linkage and data transfer to SAS
Speed
 Single define, multiple output
 All graphs and reports in the same window, powerful table commands
not available in excel
 Control, command function to manipulate them all
Ease of Use
 JMP organization simplifies the windows, text and graphs integrated
 Simplified interface to complex activities such as Fit Y by X and Fit
Model
 Ease of data and table manipulation.
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Minitab Version 15
 Both Minitab (MT) and JMP are far superior for data analysis
than using Excel
 MT is a mature, full featured product with years of user input and
product features
 MT was selected by GE and Honeywell as the early six sigma
engine of choice when JMP was just developing version 4. At
the time they were correct, MT was the better, more mature
product. The world has spun since that time and JMP has
surpassed MT’s capabilities in all three of the areas of greatest
interest to users; speed, power and ease of use.
 MT release 15 remains a blessing and a curse. Blessing due to
its years of application development and familiar tools. Curse
due to its old, awkward interface and software design.
 MT continues to be a much slower application once the data sets
rises above 100,000 observations.
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Section I
General Interface and Ease of Use
General design
Windows
Organization
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General Design, Tables
Minitab uses projects and
worksheets as major file
formats; where projects are
collections of worksheets.
JMP has similar capabilities.
Table commands for Minitab
and JMP are very similar and
JMP has some additional
table features not found in MT.
More table manipulation tools
in JMP and more readable file
formats.
Advantage JMP
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Data Table Size
Opening and Manipulating Large Data Sets*
File Size (rows)
Time to File Open
Time to Display One Histogram
JMP
Minitab
JMP
Minitab
1M
<1 sec.
13 sec.
1 sec.
90 sec.
5M
5 sec.
15 sec.
6 sec.
100 sec.
20 M
24 sec.
Failed.
35 sec.
Failed to display
Minitab failed to load 20M rows, all 3 columns, only one column loaded.
Advantage JMP
JMP takes seconds and Minitab takes minutes to manipulate data. If
datasets are large as they are in many transactional environments MT is
not a tenable solution. Even with moderately sized data tables MT feels
slow on response times.
*MT JMP evaluation PC used was running Vista, 1.80 GHz Duo, 2GB RAM
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Data Tables and Graphs Linked
In MT there is row identification
capability; however, no real connection
between the graph and table.
JMP makes the connection which allows
for ease of row location, data and graph
manipulation.
Major Advantage JMP
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Menus
MT displays the analysis method by
name.
JMP layers the analysis based on one
variable, two, paried and multiple Xs
and multiple Ys.
Menu Pros and Cons
Analysis of
One
Two
Paired or
Many
variables of
any data type.
Minitab is easier to use if you are
looking for a specific type of analysis by
name.
JMP’s Analyze tools are organized
based on single, two, paired and
multiple factors. JMP is generalized
and easier to learn and remember.
This is particularly true of Green Belt
level training.
Major Advantage JMP
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Graphs and Analysis
File: Clean.
Minitab uses a separate graph and session window for most of the
output. This feature is very annoying in Minitab and slows down the
user and the time to analysis understanding. It is a very old school
design.
JMP keeps all reports and graphs together in one place.
Advantage JMP
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Subsets
JMP is visual and intuitive when
creating subsets. MT does it with
formulas, row numbers or brushing.
Advantage JMP
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Formulas and Functions
JMP has a complete and rich set
of integrated functions for data
and string manipulation. MT has
fewer overall functions and they
are spread out and segmented in
the Calc function.
Advantage JMP
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Section II
Six Sigma Activities
Define
Link to process flow analysis
Measure
Process capability and
MSA
Analyze
Hypothesis testing and
performance modeling
Improve
Design of Experiments and
Robust Tolerance Design
Control
SPC
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Define, Process Flow Analysis
Minitab and JMP are
developing
partnerships for
linking process
mapping, value
stream mapping and
Lean manufacturing
analysis tools into
their respective
analytical engines.
iGrafx for example
has both JMP and
MT connections.
Advantage - Draw
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Process Capability, Minitab Normal
Process Capability Sixpack of Cn
Individual Value
I C har t
1
180
1
11
C apability H istogr am
1
11
1
1
1
1
_
X=170.62
170
160
UCL=177.95
1
1
1
11
20
40
60
80
1
100
LCL=163.30
1
120
140
160
180
162
165
M oving Range C har t
Moving Range
1
1
10
168
177
180
1
1
UCL=9.00
__
MR=2.76
0
LCL=0
1
20
40
60
80
100
120
140
160
180
160
Last 2 5 O bser vations
170
180
C apability P lot
Within
S tD ev 2.44247
Cp
1.09
C pk
1.01
C C pk
1.09
180
Values
174
Nor mal P r ob P lot
A D: 0.666, P : 0.081
1
5
175
170
Within
Overall
O v erall
S tDev 3.99757
Pp
0.67
P pk
0.62
C pm
*
Specs
175
File: Cn
171
180
185
Observation
190
195
MT’s process potential study is poorly named in this
graph. Missing PPM and sigma quality.
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Process Capability, JMP Normal
Contr ol Char t
Individual M e asur em ent of Cn
Dis tributions
1
1 11
Cn
180
1
1
11 1
1
3
. 99
U CL=177.95
. 95
. 90
175
170
Av g=170.62
165 1
1
160
. 75
. 50
11 1 1
LCL=163.30
1
0
. 25
. 10
. 05
15 3045 6075 90 120 150 180 210
Sample
. 01
M oving Range of Cn
2
LSL
Target
U SL
Normal Quantile Plot
Cn
185
*
-3s
*
U CL=9.00
Av g=2.76
0
Mean
LSL
20
*
+3s
160
Target
170
U SL
180
10
160
165
170
175
180
LCL=0.00
15 3045 6075 90 120 150 180 210
Sample
Normal(170. 624, 3. 99247)
JMP’s six graph analysis is hard to find
without training; however, it is very good
and is easy to interact with. It is a
feature under control charts. JMP
includes sigma quality in its report and
has more secondary options. It allows
for nonnormal fit selection on the fly.
Advantage JMP
% Act ual
0.5076
2.5381
3.0457
Param et
Ty pe
P
Loc at ion µ
D ispersions
Over all, Sigm a = 3.99247
-3
40
*
*
Port ion
Below LSL
Abov e U SL
Tot al Outs ide
-2
Count
Moving Range of Cn
10 *
Fitted Nor
Value
162
178
170
-1
30
*
Capability Analys is
Spec if ication
Lower Spec Limit
U pper Spec Limit
Spec Target
C apability
CP
C PK
C PM
C PL
C PU
I ndex Lower CI U pper C I
0.668
0.602
0.734
0.616
0.539
0.692
0.660
0.596
0.724
0.720
0.635
0.805
0.616
0.539
0.692
Port ion
Below LSL
Abov e U SL
Tot al Outs ide
Perc ent
PPM Sigma Quality
1.5380 15380.212
3.660
3.2346 32345.562
3.347
4.7726 47725.774
3.167
Benc hmark Z
Z Bench
Z LSL
Z USL
I ndex
1.667
2.160
1.847
Control Chart, Sigm a = 2.44165
-3s
LSL
160
Mean
+3s
Target
U SL
170
180
JMP’s second
capability graph is
poorly named. It
should be called
process potential.
C apability
CP
C PK
C PM
C PL
C PU
I ndex Lower CI U pper C I
1.092
0.984
1.200
1.007
0.897
1.117
1.058
0.957
1.160
1.177
1.052
1.303
1.007
0.897
1.117
Port ion
Below LSL
Abov e U SL
Tot al Outs ide
Perc ent
PPM Sigma Quality
0.0206 206.0636
5.032
0.1261 1260. 6901
4.521
0.1467 1466. 7537
4.475
Benc hmark Z
Z Bench
Z LSL
Z USL
I ndex
2.975
3.532
3.021
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Nonnormal Capability Fitting
Dis tributions
File: Skewed
Particle s
Quantile s
60
40
20
10
Count
U SL
20
M om e nt s
17. 910
17. 397
14. 691
11. 322
8.245
5.959
3.928
2.766
1.757
1.378
1.209
Mean
St d Dev
St d Err Mean
upper 95% Mean
lower 95% Mean
N
Sum W gt
Sum
Varianc e
Sk ewnes s
Kurt os is
CV
N Miss ing
Fitted Gam m a
6.4978344
3.353378
0.1505711
6.7936717
6.2019971
496
496
3222. 9258
11. 245144
0.8678386
0.4573768
51. 607625
0
Param et er Es tim ate s
Ty pe
Paramet er
Shape
a
Sc ale
s
Thres hold ?
JMP and MT have similar fitting
capabilities, JMP has an interactive
interface and an overall better report.
Advantage JMP
Es timate Lower 95% U pper 95%
3.7928509 3.3585488 4.2645893
1.7131795 1.5120833 1.9521407
0
.
.
N ot e: U nable to c onv erge on all conf idence limits .
Quantile Plot
11
9
Gamma Quantile
Gamma(3. 79285,1.71318, 0)
100.0% maximum
99. 5%
97. 5%
90. 0%
75. 0%
quart ile
50. 0%
median
25. 0%
quart ile
10. 0%
2.5%
0.5%
0.0%
minimum
7
5
3
1
0
0
5
10
15
20
Part icles
Capability Analys is
Spec if ication
Lower Spec Limit
U pper Spec Limit
Spec Target
Value
.
20
.
Perc ent
%Below LS L
%Abov e U SL
Ac tual
.
0.000
Over all, Sigm a = 3.33646
-3s
Mean
+3s
U SL
0
10
20
C apability
CP
C PK
C PM
C PL
C PU
I ndex
.
0.928
.
.
0.928
Port ion
Below LSL
Abov e U SL
Tot al Outs ide
Perc ent
PPM Sigma Quality
.
.
.
0.2236 2235. 6188
4.343
0.2236 2235. 6188
4.343
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Nonnormal Capability in Minitab
Process Capability of Particles_1
Calculations Based on Gamma Distribution Model
USL
P rocess Data
LS L
*
Target
*
USL
20.00000
S ample M ean
6.50403
S ample N
496
S hape
3.74418
S cale
1.73710
O v erall C apability
Pp
*
PPL
*
PPU
0.92
P pk
0.92
E xp. O v erall P erformance
P P M < LS L
*
P P M > U S L 2373.00
P P M Total
2373.00
O bserv ed P erformance
P P M < LS L *
PPM > USL 0
P P M Total
0
0.0
3.6
7.2
10.8
14.4
18.0
MT is missing the sigma quality level and the quantile plot to
look at the quality of the fit. The sixpack report is a better
option in general when using MT.
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Minitab Pareto
Pareto Chart of Causes
300
100
250
Count
MT does not allow for
easy selection of
comparison groups and
does not allow for DPU
summary tables from
the Pareto platform.
Cannot directly
generate a cost or
severity weighted
Pareto plot.
60
150
Causes
100
40
50
20
0
a
in
m
a
nt
o
C
Count
Percent
Cum %
n
tio
e
id
x
O
110
41.0
41.0
f
De
t
ec
e
isc
M
86
32.1
73.1
s
ou
e
n
l la
on
ilic
S
18
6.7
79.9
fe
De
ct
17
6.3
86.2
rr
Co
n
io
s
o
e
M
16
6.0
92.2
a
la liz
t
n
tio
11
4.1
96.3
g
in
p
Do
Percent
80
200
0
10
3.7
100.0
20
© TLC, SS0 070402
JMP Pareto
Plots
100
250
90
70
Count
60
150
50
40
100
Cum Percent
80
200
30
20
50
03/ 01/1991
03/ 02/1991
03/ 03/1991
03/ 04/1991
Doping
Metallization
Corrosion
Silicon Defect
Oxide Defect
Miscellaneous
Plots
Contamination
10
0
0
C auses
03/ 05/1991
100
90
80
70
60
50
40
30
20
10
0
30
Count
Process A
25
20
15
10
5
0
Cum Percent
35
Sample Size = 26488
100
90
80
70
60
50
40
30
20
10
0
Count
Process B
25
20
15
10
C auses
C auses
C auses
C auses
C auses
Doping
Silicon Defect
Corrosion
Metallization
Oxide Defect
Miscellaneous
Contamination
Doping
Silicon Defect
Corrosion
Metallization
Oxide Defect
Miscellaneous
Contamination
Doping
Silicon Defect
Corrosion
Metallization
Oxide Defect
Miscellaneous
Contamination
Doping
Silicon Defect
Corrosion
Metallization
Oxide Defect
Miscellaneous
Contamination
Doping
Silicon Defect
Corrosion
Metallization
Oxide Defect
Miscellaneous
0
Contamination
5
Cum Percent
35
30
Per Unit Rate s
C ause
C ontaminat ion
Oxide Def ect
Misc ellaneous
Silic on D ef ec t
C orrosion
Met allization
D oping
Pooled Tot al
C ount
110
86
18
17
16
11
10
268
D PU
0.0042
0.0032
0.0007
0.0006
0.0006
0.0004
0.0004
0.0014
Lower 95%
0.0034
0.0026
0.0004
0.0004
0.0003
0.0002
0.0002
0.0013
U pper 95%
0.0050
0.0040
0.0011
0.0010
0.0010
0.0007
0.0007
0.0016
JMP allows for easy grouping variables, DPU summary tables and cost and
severity weighted Pareto generation. Advantage JMP
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Surface Plots, MT
Contour Plot of Yield vs tpd, vph
0.00000010
Yield
< 0.0
0.0 - 0.1
0.1 - 0.2
0.2 - 0.3
0.3 - 0.4
0.4 - 0.5
0.5 - 0.6
0.6 - 0.7
0.7 - 0.8
0.8 - 0.9
0.9 - 1.0
> 1.0
0.00000009
0.00000008
tpd
0.00000007
0.00000006
0.00000005
0.00000004
0.00000003
0.00000002
0.00000001
6
7
8
9
10
11
Surface Plot of Yield vs tpd, vph
vph
Both MT and JMP have
nice surface
characterization
capabilities. MT is slow
to generate and difficult
to manipulate. Control
over the image is
slower and has less
options.
1.0
Yield
0.5
0.0
0.00000000
10.5
9.0
vph
0.00000005
7.5
6.0
tpd
0.00000010
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Surface Plots, JMP
11. 0
10. 0
vph
9.0
8.0
7.0
6.0
1e-8 2e-8 3e-8 4e-8 5e-8 6e-8 7e-8 8e-8 9e-8
t pd
3D visualization in JMP is excellent in either the contour or surface
plots. JMP allows for up to 100 gradients and MT allows for only
11 in the contour plot. JMP’s Surface Profiler is based on Open
GL a full 3D graphics engine.
Advantage JMP
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GR&R in MT
File: Gage study
Gage R&R (ANOVA) for Measurement
Reported by :
Tolerance:
M isc:
G age name:
Date of study :
Components of Variation
Measurement by Part
80
% Contribution
1.5
Percent
% Study Var
% Process
% Tolerance
40
1.0
0.5
0
Gage R&R
Repeat
Reprod
1
Part-to-Part
2
3
Sample Range
Cindy
George
9
10
0.5
Cindy
George
George
Operator
Tom
Operator * Part Interaction
Tom
UCL=0.9265
_
_
X=0.8106
LCL=0.6946
Operator
1.00
Average
Cindy
Sample Mean
8
1.0
Xbar Chart by Operator
0.50
7
1.5
UCL=0.292
_
R=0.113
LCL=0
0.0
0.75
6
Measurement by Operator
Tom
0.5
1.00
5
Part
R Chart by Operator
1.0
4
Cindy
George
Tom
0.75
0.50
1
2
3
4
5
6
Part
7
8
9
10
ANOVA analysis is similar, JMP has the variability graph which is better at
displaying variation patterns. MT removes some of the misleading AIAG
reports and provides an easier to read report format. MT is missing the
secondary breakdown of variation.
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JMP GR&R Functionality
JMP has the variability chart that is
better for showing variation patterns in
the data; however, it is missing the
control chart for outlier detection and
the summary graphs. JMP needs to
add the control chart, summary graphs
and secondary breakdown of the
variation patterns to be best in class.
Gage R&R
Measurement
R epeatability
Operator* Part
R eproduc ibilit y
Gage R&R
Part Variat ion
Tot al Variation
5.15
63. 4896
0.82177
1
2
0.33546
0.35
Variation % of Toleranc e % Proces s
0.5751474
28. 76
31. 91
0.2918240
14. 59
16. 19
0.3454725
17. 27
19. 17
0.6709290
33. 55
37. 22
0.8164450
40. 82
45. 30
1.0567536
52. 84
58. 63
which is k *s qrt of
V(Wit hin)
V(Operat or*Part )
V(Operat or)+V(Operator*Part )
V(Wit hin)+V(Operat or)+V(Operator*Part)
V(Part)
V(Wit hin)+V(Operat or)+V(Operator*Part)+V(Part )
k
% Gage R &R = 100*(RR /TV)
Prec ision t o Part Variation = R R/ PV
N umber of Dis tinct Categories = 1.41(PV/ RR )
Toleranc e = U SL-LS L
Prec ision/ Toleranc e R at io = R R/ (U SL-LSL)
H ist orical Sigma
Var iance Com pone nts for Gage R&R
C omponent
Var Component % of Total
Gage R&R
0.01697222
40. 31
R epeatability
0.01247222
29. 62
R eproduc ibilit y
0.00450000
10. 69
Part -t o-Part
0.02513272
59. 69
20 40 60 80
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Bias and Linearity, MT
Gage Linearity and Bias Study for Measurement
Reported by :
Tolerance:
M isc:
G age name:
Date of study :
1.00
95% CI
Data
Avg Bias
0.75
S
Linearity
0.131509
0.064619
Reference
A v erage
0.5
0.55
0.8
0.95
1
1.05
0.25
0
R-S q
% Linearity
G age Bias
Bias % Bias
-0.019444
5.6
-0.027778
7.9
0.111111
31.7
-0.018056
5.2
-0.044444
12.7
0.011111
3.2
-0.086111
24.6
P
0.042
0.017
6.3%
18.5
P
0.090
0.339
0.291
0.226
0.144
0.516
0.003
Percent of Process Variation
-0.25
20
-0.50
0.5
0.6
0.7
0.8
0.9
Reference Value
1.0
Percent
Bias
0.50
0.00
G age Linearity
C oef S E C oef
0.13379 0.06474
-0.18463 0.07619
P redictor
C onstant
S lope
Regression
10
0
Linearity
Bias
The linearity graph in MT
is in error. The reference
line should be relative to
the mean and not to zero.
MT does not have the
secondary breakdown of
bias by part and by
comparison group.
MT does have the p-values
for all of the comparisons
which is very desirable.
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© TLC, SS0 070402
Bias and Linearity, JMP
Bias Report for Oper ator
0.6
0.4
0.2
0.0
-0. 2
Av g Bias
0.02500
-0. 01833
-0. 06500
1.0
0.8
Bias/Accuracy
Bias/Accuracy
0.8
-0. 4
Bias Re port for Par t
Operator
C indy
George
Tom
1.0
0.6
0.4
0.2
0.0
-0. 2
C indy
George
Tom
-0. 4
Operator
1 2 3 4 5 6 7 8 9 10
Part
1
2
3
4
5
6
7
8
9
10
Av g Bias
0.11111
0.01111
0.01111
0.02778
-0. 02778
-0. 08889
-0. 04444
0.00000
-0. 08333
-0. 11111
Part
Linearity Study
1.0
Bias/Accuracy
0.8
0.6
0.4
0.2
Standard Value Avg Response
0.50000
0.47222
0.55000
0.66111
0.80000
0.78194
0.95000
0.90556
1.00000
1.01111
1.05000
0.96389
0.0
Linearity
-0.2
% Linearity
Avg Bias/Accuracy
-0.4
.5
.6
.7
.8
.9
1.0
1.1 % Accuracy
Process Variation
Standard Value
t Ratio
Measurement = 0.1337949 - 0.1846257 Standard Value Prob>|t|
R-Squared
-0.065
18.463
-0.00903
2.579
0.350
-2.423
0.017
0.082
Avg Bias Lower CL
-0.02778 -0.43030
0.11111 -0.37941
-0.01806 -0.22487
-0.04444 -0.32794
0.01111 -0.38733
-0.08611 -0.45229
Upper CL
0.513260
0.443913
0.197055
0.244745
0.285671
0.332170
JMP’s reports are correct
and more detailed in
general. JMP is missing
the p-values for the bias
errors. JMP displays the
impact to the standard
deviation based on rotation
effects.
Advantage JMP
Which equals
Slope * Process Variation
100 * abs(Slope)
Bias averaged over all parts
100 * AvgBias / Process Variation
Entered on dialog
tests H0: the slope equals 0
small pvalues = slope is not likely 0
27
© TLC, SS0 070402
Attribute GR&R, MT
Date of study :
Reported by :
Name of product:
Misc:
Assessment Agreement
Within Appraisers
Appraiser vs Standard
100
95.0% C I
P ercent
90
90
80
80
Percent
Percent
100
70
60
70
60
50
Ernesto
95.0% C I
P ercent
50
Juan
Appraiser
Maria
Ernesto
Juan
Appraiser
Maria
MT has a very good and very
detailed agreement analysis
report; however, it is poor on
graphing and labeling of
effectiveness.
Agreement/effectiveness by
part, prob(miss), prob(false
alarm), bias report and escape
rate are all missing in MT.
28
© TLC, SS0 070402
% Agreement
Attribute GR&R, JMP
100
80
60
40
20
J uan
Maria
Ernest o
R at er
Agreement between & wit hin raters
Ef f ect iv eness (Agreement t o St andard)
Agre em e nt Re por t
Attr ibut e Gage
R at er
% Agreement 95% Lower CI 95% Upper C I
J uan
68. 8889
51. 0005
82. 4890
Maria
76. 6667
59. 0717
88. 2076
Ernest o
74. 4444
56. 7146
86. 6248
% Agreement
Gage Attr ibute Char t
100
80
N umber I nspec ted N umber Matc hed % Agreement 95% Lower CI 95% Upper C I
30
16
53. 333
36. 142
69. 768
60
40
Agre em e nt w ithin Rat er s
20
0
1 2 3 4 5 6 7 8 9 101112131415 16171819 202122232425 2627282930
% Agreement
Part N o.
R at er
N umber I nspec ted N umber Matc hed R at er Sc ore 95% Lower CI 95% Upper C I
J uan
30
28
93. 3333
78. 6765
98. 1523
Maria
30
28
93. 3333
78. 6765
98. 1523
Ernest o
30
29
96. 6667
83. 3296
99. 4091
Effe ctivenes s Repor t
Agre em e nt Counts
100
80
60
40
20
J uan
Maria
Ernest o
R at er
Agreement between & wit hin raters
Ef f ect iv eness (Agreement t o St andard)
Agre em e nt Re por t
JMP’s attribute GR&R
report is very good and
covers agreement and
effectiveness very well. It
is missing bias and
escape rate. JMP’s
graphs are better at
showing agreement
(blue line) and
effectiveness (red line).
R at er
% Agreement 95% Lower CI 95% Upper C I
J uan
68. 8889
51. 0005
82. 4890
Maria
76. 6667
59. 0717
88. 2076
Ernest o
74. 4444
56. 7146
86. 6248
N umber I nspec ted N umber Matc hed % Agreement 95% Lower CI 95% Upper C I
30
16
53. 333
36. 142
69. 768
Agre em e nt w ithin Rat er s
R at er
C orrect (0) C orrect (1) Tot al Correct I nc orrect (0) I nc orrect (1) Grand Tot al
J uan
25
41
66
14
10
90
Maria
38
50
88
1
1
90
Ernest o
34
42
76
5
9
90
Effe ctivenes s
R at er
Ef f ect iv eness 95%
J uan
73. 3333
Maria
97. 7778
Ernest o
84. 4444
Lower CI 95% Upper C I Error rate
63. 3802
81. 3762
0.2667
92. 2555
99. 3885
0.0222
75. 5672
90. 5017
0.1556
M isclas sifications
St andard Lev el
0
1
Other
0
.
20
0
1
20
.
0
Conform ance Repor t
R at er
P(Fals e
J uan
Maria
Ernest o
Alarms ) P(Mis ses)
0.1961
0.3590
0.0196
0.0256
0.1765
0.1282
As sumpt ions
N onConf orm =0
C onf orm =
1
R at er
N umber I nspec ted N umber Matc hed R at er Sc ore 95% Lower CI 95% Upper C I
J uan
30
28
93. 3333
78. 6765
98. 1523
Maria
30
28
93. 3333
78. 6765
98. 1523
Ernest o
30
29
96. 6667
83. 3296
99. 4091
Effe ctivenes s Repor t
Advantage JMP
Agre em e nt Counts
R at er
C orrect (0) C orrect (1) Tot al Correct I nc orrect (0) I nc orrect (1) Grand Tot al
29
© TLC, SS0 070402
Context Sensitive Fit Y by X
This is where JMP shines over
Minitab and provides the user
with the proper analysis
depending on the data type.
JMP automatically switches
between four different
analytical platforms depending
on the column attributes.
Advantage JMP
30
© TLC, SS0 070402
Correlation Fit Y by X
Correlation studies, exploratory data
analysis, fit special, group by, etc.,
this is where JMP outperforms MT on
option after option.
Advantage JMP
File: Factory RSM
31
© TLC, SS0 070402
Fit Y by X Contingency Tables
Continge ncy Analys is of Caus e s By Proces s
M osaic Plot
1.00
Silic on D ef ec t
JMP and MT have
similar summary table
capabilities; however,
MT is missing the
visualization graphs.
Oxide Def ect
Causes
0.75
Misc ellaneous
Met allization
D oping
C orrosion
0.50
0.25
C ontaminat ion
0.00
Proc es s A
Proc es s B
Advantage JMP
Proc es s
Freq: Failure Count
Process
Continge ncy Table
C ount
C ontaminat ionC orrosion
D oping
Tot al %
Proc es s A
86
8
32. 09
2.99
Proc es s B
24
8
8.96
2.99
110
16
41. 04
5.97
C auses
Met allization
5
1.87
5
1.87
10
3.73
5
1.87
6
2.24
11
4.10
Misc ellaneous Oxide Def ect Silic on D ef ec t
8
2.99
10
3.73
18
6.72
42
15. 67
44
16. 42
86
32. 09
8
2.99
9
3.36
17
6.34
162
60. 45
106
39. 55
268
Tes ts
Source
Model
Error
C . Total
N
DF
6
256
262
268
-LogLike R Square (U)
12. 85597
0.0318
391.44640
404.30237
Tes t
C hiSquare Prob>C hiS q
Likelihood Ratio
25. 712
0.0003*
Pearson
24. 743
0.0004*
File: Failures
32
© TLC, SS0 070402
Multiple Regression, N-Way, ANCOVA
MT requires detailed statistical
and modeling training to
remember the names of all of
the types of ANOVA. Once the
analysis is preformed there is
not an easy to use suite of tools
and secondary graphs for the
user to interact with for further
visualization, characterization
and optimization. Tools are
segmented and not well
integrated for optimization.
File: cement
33
© TLC, SS0 070402
Multiple Regression, N-Way, ANCOVA
Pre diction Profile r
30
25
20
C onsolidat ed
Brand
reinf orced
Addit iv e
51. 01
H umidity
.75
1.00
.50
.25
70
.00
65
60
55
50
45
40
standard
reinforced
Graystone
EZ Mix
Consolidated
Desirability
0.462776
0.00
0.50
1.00
Simple
model
definition no
matter the
data type.
Strength
25.53118
±1.762336
35
D es irability
Res pons e Stre ngth
20
20
25
30
35
St rength P redic ted P<.0001
R Sq=0. 82 RMSE=1.7691
25
20
24
25 26 27 28 29 30
Brand Lev erage, P=0. 0002
Lev el
Least Sq Mean
C onsolidat ed
24. 510344
EZ Mix
25. 187449
Gray st one
28. 535844
R Square
0.815622
R Square Adj
0.732652
R oot Mean Square Error
1.769063
Mean of R es pons e
25. 99761
Obs erv at ions (or Sum Wgts )
30
St d Error
0.58174366
0.68951528
0.60951464
25
20
24. 5 25. 5 26. 5 27. 5 28. 5
Addit iv e Lev erage, P=0. 0037
Leas t Squar es M eans Table
Sum m ar y of Fit
35
30
Lev el
Least Sq Mean
reinf orced
27. 313739
s tandard
24. 842019
St d Error
0.58483741
0.50664460
30
25
20
40
45 50 55 60 65 70
H umidity Lev erage, P=0. 0025
Mean
27. 9040
24. 0912
Analys is of Variance
Source
Model
Error
C . Total
D F Sum of Squares Mean Square
F Ratio
9
276.88262
30. 7647
9.8303
20
62. 59167
3.1296 Prob > F
29
339.47429
<.0001*
Param et er Es tim ate s
Term
Es timate
I nt ercept
39. 078555
Brand[ Consolidated]
-1. 567535
Brand[ EZ Mix ]
-0. 89043
Addit iv e[ reinf orced]
1.2358601
H umidity
-0. 254865
Brand[ Consolidated] *Addit iv e[ reinf orced] -0. 21502
Brand[ EZ Mix ]*Addit iv e[reinf orc ed]
-0. 590918
Brand[ Consolidated] *(Humidit y -51.01) 0.0187235
Brand[ EZ Mix ]*(Humidity -51.01)
-0. 105633
Addit iv e[ reinf orced] *(Humidit y -51.01)
0.0848815
File: cement
St d Error t R at io Prob>| t|
3.670883 10. 65 <.0001*
0.473612
-3. 31 0.0035*
0.502441
-1. 77 0.0916
0.375834
3.29 0.0037*
0.073724
-3. 46 0.0025*
0.513278
-0. 42 0.6797
0.551437
-1. 07 0.2967
0.094179
0.20 0.8444
0.086185
-1. 23 0.2346
0.07312
1.16 0.2594
Effe ct Te sts
Source
Brand
N parm
2
D F Sum of Squares F Ratio Prob > F
2
84. 839165 13. 5544
0.0002*
Leve rage P
35
Leas t Squar es M eans Table
Mean
24. 2011
25. 8237
27. 9681
Brand*Hum id
Leve rage Plot
In addition to the detailed statistical
summary tables JMP offers a full suite of
graphs for visualization, characterization
and optimization. Advantage JMP
35
Strength
Leverage Residuals
25
30
Brand*Additive
Leve rage Plot
35
Strength
Leverage Residuals
30
Hum idity
Leve rage Plot
35
Strength
Leverage Residuals
35
Strength Actual
Additive
Leve rage Plot
Strength
Leverage Residuals
Brand
Actual by Pre dicte d Plot
Strength
Leverage Residuals
Whole M ode l
30
25
20
23 24 25 26 27 28 29 30 31
Brand*Additiv e
Lev erage, P=0. 3287
30
25
20
25. 0 25
Leas t Squar es M eans Table
Lev el
Least Sq Mean
C onsolidat ed,reinf orc ed
25. 531184
C onsolidat ed,s tandard
23. 489504
EZ Mix ,reinf orc ed
25. 832392
EZ Mix ,s tandard
24. 542507
Gray st one,reinf orced
30. 577642
Gray st one,s tandard
26. 494047
St d Error
0.8448547
0.8660803
1.1401751
0.7918990
0.8031139
0.9428028
34
© TLC, SS0 070402
Design of Experiments - Design
DOE in Minitab is awkward to use for
designing experiments as it does not
allow for the direct design of the
experiment in line with the problem
that needs characterization.
Minitab uses a candidate points
method for customization and
augmentation. This is very old school
and tedious for the user. Covariates
are not part of the design, they are
secondary in the analysis.
Minitab does not allow for correct
factor identification when designing
the experiment. There are many
more factor types than those allowed
by MT. MT fails the ease of use test
for DOE.
File: Yield
35
© TLC, SS0 070402
DOE Analysis, MT
Analysis
flow
MT’s analysis tools for DOE are segmented, do not flow well and
the optimizer is missing a more intuitive set of controls for
constraining, fixing, optimizing and predicting the response.
MT’s DOE design and analysis flow is segmented, complicated, not
seamlessly integrated and has too many steps.
36
© TLC, SS0 070402
Design of Experiments in JMP
JMP custom designs match the
problem. Any combination of
factors, factor types, covariates,
blocking sizes, categorical
factors and mixtures with a
minimum sample size. Simple to
define the model terms to be
characterized. Allows the most
flexible environment for DOE
treating the engineer and
scientist as the customer.
JMP is best is class for DOE.
JMP wins on DOE ease of use.
In JMP the DOE design always
fits the problem.
37
© TLC, SS0 070402
DOE Analysis in JMP is the Same Fit Model Engine
Output
1275
±20.74915
Pre diction Profile r
1600
1200
Diameter
2.499
±0.025936
800
2.65
2.55
2.45
10
5
0
150
Speed
275
Temp
7.5
Time
22. 5
Pres sure
1.00
.25
.50
.75
30
.00
25
20
200
250
260
270
280
290
300
5
6
7
8
9
10
15
175
150
125
100
Desirability
0.053217
Cracks
4.8
±1.037457
0.00 0.50 1.00
2.35
20
15
D es irabilit y
In JMP learn one set of tools and use them for a variety of
characterization, DOE, modeling problem solving activities. JMP’s
profiler allows for improved visualization and control of the transfer
functions.
Major Advantage JMP
38
© TLC, SS0 070402
JMP’s Simulator Linked to Transfer Functions
Optimize performance, improve
robustness and predict full
distribution at target. MT does not
have this capability. Set and
evaluate tolerances.
Major Advantage JMP
Diam ete r
Capability Analys is
U SL
75000
50000
25000
Count
LSL
Spec if ication
Lower Spec Limit
U pper Spec Limit
Spec Target
Value
2.51
2.57
.
Port ion
Below LSL
Abov e U SL
Tot al Outs ide
% Act ual
0.4050
0.7275
1.1325
Over all, Sigm a = 0.01178
C apability
CP
C PK
C PM
C PL
C PU
2.48 2.5 2.52 2.54 2.56 2.58 2.6
-3s
Mean
LSL
2.48
2.52
+3s
U SL
2.56
2.6
I ndex Lower CI U pper C I
0.849
0.000
0.850
0.846
0.844
0.847
.
.
.
0.853
0.851
0.854
0.846
0.844
0.847
Port ion
Below LSL
Abov e U SL
Tot al Outs ide
Perc ent
PPM Sigma Quality
0.5271 5270. 6210
4.058
0.5586 5586. 2125
4.037
1.0857 10856.834
3.795
Benc hmark Z
Z Bench
Z LSL
Z USL
I ndex
2.295
2.558
2.537
39
© TLC, SS0 070402
Power and Sample Size
JMP has sample size calculation for counts
per unit and for estimating the standard
deviation. MT identifies sample size for
replicates for two specific forms of DOE and
JMP does not. JMP also has a sigma quality
converter and calculator.
Minor Advantage JMP
40
© TLC, SS0 070402
SPC
JMP 6 to Minitab 14 Comparison
11/22/2005
SPC Control Charts
Control Charts for Subgroups
Xbar R
Xbar S
Presummarize
Delta to Target, subgroup
Z subgroup
Control Charts for Individuals
Run Chart
I/MR
Z/MR individual
Delta to Target, individual
Levey Jennings
Control Charts for Small Mean Shifts
UWMA (moving average)
EWMA
CUSUM
Control Charts for Attributes
P
NP
C
U
Multivariable Control Charts
T2
Multivariate EWMA
JMP 6.0
MT 14.1
Y
Y
Y
N
N
Y
Y
Y
N
Y
Y
Y
N
N
Y
Y
Y
Y
N
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
N
Y
Y
© 11/22/05
MT and JMP’s capabilities
are quite similar. MT offers
more charts; however,
JMP’s charts are easier to
manipulate and are better
for larger data sets. JMP
needs to add the short run
Z and delta to target
charts. Both platforms
allow for phased control
charts to show before and
after effects.
Advantage - Draw
41
© TLC, SS0 070402
Section III
Extended Capabilities
Reliability
Multivariate
Time Series
Graphs
Advanced Modeling
Summary
42
© TLC, SS0 070402
Reliability
MT offers reliability planning tools for
sample size determination and JMP does
not. JMP has stronger modeling and
multivariate tools for reliability modeling.
Advantage - Draw
43
© TLC, SS0 070402
Multivariate
JMP has a richer set of tools for multivariate
analysis. Factor analysis and principle
components analysis are in the multivariate
platform and are harder to locate from the
menu.
Advantage JMP
44
© TLC, SS0 070402
Time Series
JMP and Minitab similar tools and
capabilities. JMP has a few more options
and the ease of use and graphical
manipulation makes it superior to MT.
Minor Advantage JMP
45
© TLC, SS0 070402
Graphs
JMP offers similar graphs to MT;
however, it outperforms in the
profiler, contour profiler, surface
plot and custom profiler options.
MT does not have the same rich
tools for optimization and robust
design.
Advantage JMP
46
© TLC, SS0 070402
Advanced Modeling Tools
JMP offers a much richer and versatile set of modeling tools
and analytical methods. Neural nets, recursive partitions and
nonlinear modeling are all available modeling tools in JMP.
Advantage JMP
47
© TLC, SS0 070402
For A More Detailed Comparison
JMP 6 to Minitab 14 Comparison
11/22/2005
Product Features
File and Data access
Table design and tools
Supporting file formats
Large data table manipulation (1M rows +)
Database connection
Project file management
Customization
Programmability, scripting
Menus (names and graphics)
Toolbars
Keyboard commands
Full automation
Ease of Use
JMP Starter
Graph Manipulation
Menus
Help functions
Context sensitive help
Toolbars
Graph and data table link
Documentation
Dynamic graphs using scripts
Integrated graphs and reports
Data editing and modification
JMP 6.0
MT 14.1
A
A
A
A
no feature
B
B
D
B+
A
A
A
A
no feature
A
B
A
A
A
B
A
A
A
B
A
A
A
B
A
A
A
no feature
C
B
A
no feature
B
C
A
no feature
C
A
For a more detailed comparison of JMP versus MT take a look at the
JMP 6 to MT 14 comparison table.
48
© TLC, SS0 070402
Summary
 JMP is in general a superior product
 JMP is world class for regression, modeling, DOE, and simple studies such as
process capability and MSA and the user interface is very well designed
 JMP is easier to use, more powerful, much faster in completing analysis of
data and needs to address some of the minor gaps identified in this
comparison
 Having two great applications is good for the market and keeps both
applications improving to meet customer needs and expectations
 MT is a good application and has a rich set of tools. JMP is a great application
and has an overall better designed and better integrated tool set.
 Helping companies understand why Excel is not enough for analysis is the
greatest opportunity
 Minitab must address the ease of use, some missing tools and speed issues.
49
© TLC, SS0 070402
762 North 470 East
American Fork, UT 84003
925-285-1847
[email protected]
www.dr-tom.com
50
© TLC, SS0 070402