Hello,
my
name
is
Laura
Lancaster
and
I'm
here
with
my
colleague,
Annie
Dudley Zangi,
to
talk
about
recent
developments
in
JMP quality
and
SPC.
The
first
thing
I
want
to
talk
about
is some
improvements
that
we've
made
to
the
distribution
platform
specifically
related
to
limits
of
detection.
So
limited
detection
is
when
we're
unable
to
measure
above
or
below
a
certain
threshold.
And
in
JMP
Pro
16,
some
functionality
was
added
for
limits
of
detection.
Specifically
in
the
DOE
platform,
we
added
the
ability
to
account
for
limits
of
detection
and
a
Detection
Limits
column
property
was
added
that's
used
by
the
Generalized
Regression
platform
to
specify
censoring
for
responses.
However,
what
was
left
unaddressed
was
a
problem
with
process
capability
and
limits
of
detection.
The
problem
is
that
when
you
ignore
limits
of
detection
when
analyzing
process
capability,
it
can
give
misleading
results.
And
there
was
no
way
to
do
process
capability
with
censored
data.
But
in
JMP Pro
17,
and
I
just
wanted
to
note
that
this
is
the
only
feature
that
we're
going
to
talk
about
that's
JMP Pro.
Everything
else
is
regular
JMP in
this
talk.
So
in
JMP
Pro
17,
now
in
the
Distribution
Platform,
we
recognize
that
Detection
Limits
column
property
and
we
can
adjust
the
fitters
for
censored data.
That
means
that
the
Process
Capability
report
that's
within
those
fitters
that
use
the
adjusted
fit
to
account
for
censored
data
will
give
more
accurate
results.
And
the
available
adjusted
distribution
fitters
are
Normal,
Log
normal,
Gamma,
Weibull,
Exponential,
and
Beta.
And
before
I
go
to
the
example,
I
just
wanted
to
give
a
shout
out
to
check
out
the
poster
session
Introducing
Limits
of
Detection
in
the
Distribution
Platform
that
Clay
Barker
and
I
worked
on
if
you
want
to
learn
more
about
this.
Let's
go
ahead
and
go
to
JMP.
Here
I
have
some
drug
impurity
data
where
I
have
an
issue
with
being
able
to
detect
impurities
below
a
value
of
one.
And
this
data
that
I've
recorded
is
actually
in
the
second
column
and
anywhere
that
I
wasn't
able
to
record
an
impurity
because
it
was
below
one,
I've
simply
recorded
it
as
a
one.
So
this
is
censored
data.
This
first
column
is
really
the
true
impurity
values
that
I'm
unable
to
know,
unable
to
detect
with
my
detection.
So
let's
go
ahead
and
compare
both
of
these
columns
using
distribution.
So
if
I
go
to
Analyze,
Distribution,
and
I
look
at
both
of
these
columns,
you
can
clearly
see
there's
a
pretty
big
difference
between
having
true
impurity
values
which
I'm
unable
to
know,
and
the
censored
data.
Ultimately,
what
I
want
to
do
is
I
want
to
do
a
log
normal
fit
and
run
a
process
capability
analysis
on
this
data.
So
I'm
going
to
go
ahead
and
do
that
for
both
of
these
distributions.
So
I'm
going
to
do
log
normal
fit
for
both
of
them.
You
can
see
that
I
get...
Obviously
the
histograms
look
pretty
different
and
my
fits
look
pretty
different
too,
which
isn't
surprising.
Now,
I
want
to
do
Process
Capability
on
both
of
these.
I've
already
added
an
upper
spec
limit
as
a
column
property,
and
you
can
see
that
when
I
have
my
true
data,
which
I'm
unable
to
know,
my
capability
analysis
looks
pretty
different
from
having
the
censored
data.
With
the
true
data,
my
capability
looks
pretty
bad.
There's
probably
something
I
need
to
address.
But
because
I'm
not
able
to
see
the
true
data,
and
I
only
have
the
censored
data
that
I
can
analyze
in
JMP,
the
PPK
value
is
a
lot
better.
It's
above
one,
and
I
may
blissfully
move
along
thinking
that
my
process
is
capable
when
in
actuality,
it
really
isn't
so
good.
But
thankfully,
in
JMP Pro
17,
I
can
add
a
detection
limits
column
property
in my
data.
So
this
third
column
is
the
same
as
my
second
column,
except
that
I've
added
a
detection
limits
column
property.
So
I've
added
that
I
have
a
lower
detection
limit
of
one.
And
now
when
I
run
Distribution
platform
on
this
third
column
with
a
detection
limit
column
property,
and
I
do
my
logn
ormal
fit
and
notice
because
I
have
censored
data,
I
have
a
limited
number
of
distributions
available,
I'm
going
to
do
my
log
normal
fit,
and
it's
telling
me
it
detected
that
detection
limit
column
property,
and
it
knows
I
have
a
lower
detection
limit
of
one.
And
when
I
do
Process
Capability,
you
can
see
that
my
capability
analysis
is more
in
line
with
when
I
had
the
true
data
because
my
PPK is 0.546,
doesn't
look
so
good.
And
I
realize
that
there's
probably
something
that
I
need
to
address
with
this
process.
It's
not
very
capable.
All
right,
so
let's
move
along
to
the
next
topic.
The
next
thing
I
want
to
talk
about
is some
improvements
in
Measurement
Systems
Analysis,
specifically
the
Type 1
Gauge
Analysis
platform.
A
Type
1
Gauge
Analysis
platform
is
a
basic
measurement
study
that
analyzes
the
repeatability
and
bias
of
a
gauge
to
measure
one
part
relative
to
a
reference
standard.
It's
usually
performed
before
more
complex
types
of
MSA
studies
such
as
EMP
or
Gauge
R&R
that
are
already
in
JMP.
It's
required
by
some
standard
organizations
such
as
VDA
in
Germany,
and
this
has
been
requested
by
our
customers
for
quite
a
while,
but
we
believe
it's
useful
for
anyone,
whether
it's
required
by
a
standard
organization
or
not.
It's
located
in
JMP
17
in
the
Measurement
Systems
Analysis
launch
dialog
as
an
MSA
Method
type.
It
requires
a
reference
standard
value
to
compare
your
measurements
against,
and
a
tolerance
range
where
you
want
your
measurements
to
be
within
20%
of
your
tolerance
range.
Produces
a
run
chart,
metrics
such
as
Cg, Cgk,
which
are
comparable
to
capability
statistics,
bias
analysis,
and
a
histogram
for
analyzing
normality.
Let's
go
ahead
and
look
at
this
new
platform
in
JMP.
Here
is
my
Type
1
Gauge
data.
It's
simply
measurements
of
one
part
with
one
gauge.
So
to
get
to
the
platform,
I
go
to
Analyze,
Quality
and
Process,
Measurement
Systems
Analysis.
And
the
first
thing
I
want
to
do
is
change
the
method
from
EMP
to
Type
1
Gauge.
I'm
going
to
move
my
measurements
as
the
response
I'm going to
leave
everything
else
at
default,
and
I'm
going
to
click
OK.
But
before
I
can
proceed
to
get
my
report,
I
have
to
enter
that
metadata
that I
mentioned
earlier,
the
reference
value
and
the
tolerance
range.
So
I'm
going
to
go
ahead
and
enter
that
information.
I'm
going
to
enter
it
as
a
tolerance
range
and my
reference
value.
I'm going to skip
resolution
because
that's
optional.
Click
OK.
And
this
is
the
default
report
that
I
get.
I
get
a
run
chart
on
my
measurements
graphed
against
my
reference
line.
And
I
also
get
the
20%
tolerance
range
lines.
One's
10%
tolerance
range
above
reference
and
one
is
10%
below.
So
you
get
some
default
capability
statistics.
Now
notice
that
my
measurements
are
well
within
the
20%
of
my
tolerance
range,
which
is
really
good.
I
could
also
do
a
Bias
Test
to
see
if
my
measurements
are
biased.
It
looks
okay.
And
I
could
also
turn
on
a
histogram
to
test
for
normality.
And
before
we
move
on,
I
wanted
to
find
out
one
more
thing,
and
that's
that
in
this
top
outline
menu,
if
I
click
on
that,
there's
an
option
to
save
that
metadata
that
I
had
to
enter
to
be
able
to
get
this
report.
Remember,
I
had
to
enter
the
reference
value
and
the
tolerance
range.
So
I
could
either
save
this
metadata
as a
column
property
and
we've
introduced
a
new
column
property
called
MSA,
or
I
could
save
it
to
a
table.
I'm
going
to
go
ahead
and
save
it
as
a
column
property
so
I
can
show
you
the
new
MSA
column
properties.
If
I
go
back
to
the
data
table,
this
is
the
new
MSA
column
property.
You
can
see
it's
storing
my
tolerance
range
and
my
reference
value,
and
it
also
can
hold
other
metadata
for
other
types
of
MSA
analysis.
Let's
move
along
to
the
next
topic.
I
also
want
to
talk
about
some
improvements
to
existing
MSA
platforms,
the
EMP
MSA
platform.
EMP
stands
for
Evaluating
the
Measurement
Process,
and
this
is
the
platform
based
on
Don Wheeler's
approach
and
a
variability
chart
platform.
So
in
both
of
these
platforms,
we've
improved
the
usability
when
analyzing
multiple
measurements
at
one
time.
We
have
better
handling
of
the
metadata,
such
as
[inaudible 00:10:16]
or
tolerance
values
or
process
Sigma.
So
this
has
been
improved
in
variability
charts
and it's added
to
the
EMP
MSA
platform.
We've
also
reorganized
the
reports
so that
they
work
better
with
data
filters.
In
addition,
we've
filled
in
the
gaps
between
the
EMP
MSA
platform
and
the
Variability
Chart.
We've
done
this
by
adding
some
reports
to
the
EMP
MSA
platform.
We've
added
the
Misclassification P
robability
Report,
the
AIAG
Gauge
R&R
report,
and
a
Linearity
and
Bias
report.
In
addition,
we've
modernized
the
Linearity
report
and
the
Variability
Chart
to
match
the
new
Linearity
report
in
the
EMP
MSA
platform.
So
let's
go
ahead
and
look
at
some
of
these
changes.
So
here
I
have
some
measurement
systems
analysis
data
for
some
tablets
where
I've
measured
two
different
attributes
with
multiple
operators.
And
I
want
to
analyze
this
using
the EMP
platform.
So
I'm
going
to
go
to
Analyze,
Quality
and
Process
Measurement
Systems
Analysis.
First
thing
I
want
to
do
is
change
the
method
back
to
EMP,
take
my
measurements
as
response,
Tablet as
Part,
Operator
as Grouping.
Notice
there's
now
this
standard
role,
if
I
were
doing
a
linearity
and
bias
study,
I
would
use
that,
but
I'm
not
in
this
example.
Also some
new
options
down
here
in
the
dialogue,
but
the
one
I
want
to
point
out
is the
Show
EMP
Metadata
Entry
Dialogue.
I
want
to
set
that
to
Yes
so
I
can
enter
tolerance
values
and a
historical
Sigma
for
the
AIAG
Gauge
R&R
report.
So
I'm
going
to
click
OK
and
this
dialogue
pops
up.
I
don't
have
to
enter
this
data
during
the
launch,
but
I'm
going
to
because
I
think
it's
easier.
So
I'm
going
to
go
ahead
and
enter
the
data,
and
when
I
click
OK,
my
report
looks
similar
to
how
it's
always
looked
when
I've
had
multiple
measurements.
But
I
also
have
an
additional
outline
at
the
top,
and
we'll
look
at
that
in
a
minute.
But
the
first
thing
I
want
to
do
is
I
want
to
turn
on
the
Misclassification
Probabilities
report
for
both
of
these
analyses.
So
I'm
going
to
choose
Misclassification
Probabilities,
and
you
can
see,
I
get
a
new
misclassification
probability
report
for
both
of
these
and
it's
available
without
a
prompt
because
I've
already
entered
my
lower
and
upper
tolerance
values.
Now,
if
I
had
not
already
entered
that
information,
I
would
have
been
prompted.
Or
I
could
use
the
new
option,
Edit
MSA
Metadata,
to
either
enter
or
edit
any
of
that
information,
which
would
automatically
update
any
of
the
corresponding
reports.
Let's
go
ahead
and
turn
on
the
AIAG Gauge R&R
report
for
both
of
these
as
well.
And
you
can
see
I
get
an
AI
AG
Gauge
R&R
report
that
looks
a
lot
like
what's
in
the
Variability
C
hart
platform,
and
it
includes
that
percent
tolerance
column
because
I
entered
tolerance
values
and
percent
process,
because
I
entered
historical
Sigma.
I
could
also
turn
on
the
discrimination
ratio
if
I
desired.
And
before
we
move
on,
I
just
want
to
point
out
at
this
top
outline
menu,
once
again,
we
have
an
option
to
save
the
metadata.
I
can
save
the
metadata,
which
includes
not
only
the
MSA
metadata,
but
also
I
can
save
out
measurement
Sigma,
which
is
a
result
of
my
MSA
analysis,
which
can
be
consumed
by
the
Process
Screening
platform.
So
it's
going
to
be
considered
process
screening
metadata,
and
there's
actually
a
new
process
screening
column
property
for
that.
But
I'm
going
to
save
this
as a
table
just
so
we
can
look
at
it.
I can
see
I
have
my
MSA
metadata,
plus
I've
saved
out
the
measurement
Sigma
once
I've
computed
those
variance
components.
So
let's
go
on
to
the
next
topic,
my
final
topic
before
I
hand
this
over
to
Annie,
the
last
thing
I
wanted
to
talk
about
was some
improvements
to
the
Manage
Spec
Limits
utility.
In
fact,
the
name
has
been
changed
to
the
Manage
Limits
utility
because
now
it
handles
more
than
just
spec
limits.
It
still
handles
spec
limits
and
anything
related
to
process
capability.
But
now
it
also
can
handle
Process
Screening
metadata,
which
includes
centerline,
specified
Sigma,
and
measurement
Sigma,
MSA
metadata,
and
Detection
Limits.
So
now
I'm
going
to
hand
this
over
to
Annie.
Hi,
everyone.
I
am
Annie
Dudley
Zengi,
and
I
am
the
developer
responsible
for
control
charts
in
JMP.
I'm
here
to
talk
with
you
about
some
of
the
new
features
that
I
added
for
Control
Chart Builder
in
version
17.
So
I
added
a
Label
Role
in
addition
to
the
Y,
the
subgroup,
and
the
phase
role,
there's
now
a
label
role.
I've
added
a
button
so
that
you
can
switch
an
XBar
and
R
chart
to
an
IMR
chart.
I
added
a
row
legend,
a
Connect Thru
Missing
Command,
and
I've
done
some
Dialog U
pdates.
I'll
start
with
this
data
table
diameter,
which
you
can
find
in
the
sample
data.
And
let's
start
with
the
label
role.
So
I'm
going
to
alternate
between
using
the
interface
and
using
the
dialogs
so that
everybody
can
get
a
feel
for
both.
If
I
start
with
the
interface,
and
I
drag D
iameter
in
to
the
graph,
we
immediately
see
we
get
an
Individual and
Moving
Range
chart.
Now,
one
thing
that
you'll
notice
that's new
here
is
this
new
role
in
the
lower
left-
hand
corner
of
the
chart
for
the
label.
Now
I
can
drag
Day
in.
Now
I
want
to
take
a
look
at
Day
here
in
the
data
table.
So
you
might
notice
that
there
are
six
different
rows
that
are
associated
with
May
1st,
1998.
There
are
six
rows
associated
with
every
date
in
this
particular
data
table.
And
we
know
that
if
we
were
to
drag
that
to
the
Subgroup
role,
then
Control
Chart
Builder
will
automatically
aggregate.
But
sometimes
we
don't
want
that.
So
for
this
example,
I'm
going
to
drag
this
to
the
Label
role.
You
notice
we
still
have
an
Individual
and Moving
Range
chart.
It
did
not
switch
and
it
did
not
aggregate
the
data.
We
can
see
that
it's
a
regular
axis.
We
currently
have
an
increment
of
24.
We
can
change
the
increment
to
six.
We
can
see
every
date
on
the
x-
axis
and
we
can
still
see
that
we
have
an
Individual
and Moving
Range
chart
of
Diameter.
So
there's
the
Label
role.
Now
the
next
option
is
the
switch
to
the
IMR
chart.
This
option
was
made
available
because
there's
now
a
Label
role.
To
switch
to
an
IMR
chart,
we
first
have
to
have
an
XBar
on
our
chart.
So
I
will
create
an
XBar
on
our
chart
through
the
dialog.
You can choose
Control
Chart
and
then
XBar
Control
Chart.
Again
I'll
move
Diameter
to
the
Y.
And
this
time
I'm
going
to
put
Day
in
as
a
Subgroup.
You
can
see
here
it's
aggregated
the
data
because
we
have
Day
as actually
the
subgroup.
But
if
I
show
the
control
panel
and
I
scroll
down,
you'll
notice
there's
a
new
button
here
underneath
the
old
button
of the
Three
Way
Control
Chart.
And
when
I
click
that
button,
it
moves
the
variable
from
the
Subgroup
role
into
the
Label
role.
So
you
see
we
now
have
an
Individual
and
Moving
Range
Chart of
Diameter.
Now,
the
next
option
is a
Row
Legend.
Row
Legend
is
new
for
Control
Chart
Builder.
And
I
have
a
little
note
here.
The
Row
Legend
option
is
only
going
to
appear
when
there's
only
one
row
per
subgroup.
So
if
you
right-
click
like
you
do
in
a
lot
of
other
graphs
in
many
other
platforms
in
JMP,
you'll
now
see
a
Row
Legend
here,
but
only
if
you
have
one
row
per
subgroup.
And
the
Row
Legend
acts
like
a
row
legend
does
anywhere
else.
I
can
choose,
say,
for
example,
Operator,
and
it
will
color
by
Operator
by
default.
And
now
you
have
your
points
colored
accordingly.
The
next
optionâ
I'm
going
to
close
thisâ
is
Connect Thru
Missing.
Now, Connect Thru
Missing
is going
to
involve
some
missing
data.
So
let's
open
up
Coding,
which
happens
to
have
the
Weight
that you might
normally
be
measuring,
but
it
also
has
Weight
2
that
has
missing
data.
If
I
go
through
the
interface
and
create
two
control
charts,
you
notice
we
have
a
good-
looking
control
chart
here.
Everything
is
connected
and
so
forth.
But
if
we
scroll
down
to
the
second
one,
we
see
some
gaps.
And
sometimes
management
doesn't
want
to
see
the
gaps,
so
we
need
to
connect
those.
So
there's
a
new
option
under
the
red
triangle
menu
called
Connect Thru
Missing.
You
can
see
the
little
caption
there.
It
says,
"This
item
is
new
as
of
version
17."
This
was
in
the
old
Legacy
platform.
And
so
I've
been
bringing
more
options
into
Control
Chart
Builder
that
were
available
in
the
old
Legacy
platform.
So
there's
your
Connect Thru
missing.
Now,
the
next
optionâ
I'm
going
to
switch
back
to
my
slides
here
for
a
momentâ
so
the
next
option
is
the
Laney
and
P
prime
control
charts.
This
is
a
bigger
option.
So
let's
think
about
Control C
harts
for
a
moment.
The
purpose
of
Control
Charts
is to
show
the
stability
of
your
process.
If
your
process
is
not
stable,
then
you
cannot
reliably
make
the
same
sized
part,
which
is
going
to
be
a
problem
for
all
of
your
customers.
And
so
there's
lots
of
tests
involved
in
making
sure
that
you
are
stable,
that
you're
reliably
able
to
make
the
same
part.
Now,
if
you're
looking
at
attribute
control
charts,
those
are
based
on
either
the
Binomial
or
the
Poisson
distribution,
and
those
assume
a
constant
variance.
Now,
what
happens
if
the
variance
changes
over
time?
Maybe
there's
humidity
or
there's
temperature
problems
or
there's
wear
and
tear
on
a
gear.
This
is
what
statisticians
refer
to
as
over
dispersion,
or
in
rare
instances,
under
dispersion.
And
one
parameter
distribution
cannot
model
this.
So
Laney
proposes
that
we
normalize
the
data
in
order
to
account
for
the
variant
and
account
for
varying
subgroup
sizes.
And
David
Laney
wrote
a
paper
in
2002,
Improved
Control
Charts
for
Quality.
So
let's
take
a
look
at
the
Laney
charts.
Here
I
have
some
data
also found
in
the
sample
data.
This
is
not
a
terribly
large
lot
size,
but
here
we
have
a
column
for
teaching
purposes
that
has
a
varying
lot
size.
So
let's
explore
how
this
works.
If
we
were
to
look
at,
say,
a
P
chart
of
the
number
of
defective
out
of
this
varying
lot
size.
I'm
going
to
use
the
menus,
the
dialogs
again,
I'm going to create
a
P
chart
to
start
with
and
let's
see
how
that
performs.
Okay,
we're
going
to
look
at
our
number
defective,
and
then
we
have
the
lot
as
our
Subgroup
identifier.
Now,
I'm
going
to
use
Lot
Size
2
because
that's
the
varying
lot
size.
And
click
OK.
All
right, so
on
first
glance,
yes,
we
expected
the
non-
constant
limits
because
we
have
the
varying
subgroup
sizes.
But
we
also
notice
immediately
that
our
chart,
this
process
is
out
of
control
because
we
have
these
points
that
are
beyond
the
limits
and
we
can
turn
on
the
Test
Beyond
Limits
and
they're
flagged.
And
so
this
process
would
probably
raise
all
kinds
of
alarms
and
people
would
be
trying
to
retool
things.
Now,
if
I
show
the
control
panel
when
I
have
the
statistics
set
to
proportion,
because
Laney
only,
in
his
paper,
gave
formulas
for
the
P
and
the
NP
chart,
or the
P
and
the
U
chart.
So
currently
it's
only
implemented
for
a
proportion.
But
when
you
have
your
statistics
set
to
proportion,
you
have
four
choices
now
instead
of
just
two
on
your
Sigma.
So
we
could
switch
to
the
Laney
P
prime
chart
and
see
what
that
difference
is
going
to
be.
And
suddenly
you
see
your
process
is
not
nearly
as
problematic.
It's
not
out
of
control
at
all.
It
looks
like
this
process
is actually
stable,
which
is
great
news.
Now,
is
this
is
this
really
okay,
you
might
ask,
or
is
this
cheating?
Let's
take
a
look
at
the
formulas
and
help
us
figure
this
out.
So
Laney
suggested
that
we
compute
a
moving
range,
Sigma
on
the
standardized
values.
So
these
Z's,
those
are
our
standardized
values.
We
compute
an
average
moving
range
on
that.
And
we
have
a
Sigma
sub z
,
which
is
the
average
moving
range
divided
by
1.128.
And
then
we
take
that
Sigma
sub
z
and we
insert
it
into
the
exact
same
formula
that
we
saw
for
our
P
limits.
And
so
what
you
can
see
from
this
is
if
you
actually
have
a
constant
variance,
this
Sigma
sub
z
is
going
to
approach
one.
Many argue, including Laney, that
it is generally safe to use this
instead of the P chart
since it's going to approach one
and
it's
going
to
be
the
same
anytime
you
actually
do
have
constant
limits.
So
there's
the
Laney
P
prime
chart.
I
wanted
to
show
you
also,
there's
a
few
dialog
updates.
Let
me
show
you
some
of
those
right
here.
So
I
hinted
a
little
bit
at
it.
You
can
see
the
Laney
P
prime
and
U
prime.
Those
are
two
new
dialogs
that
you
can
see
there.
The
IMR
chart
now
has
a
label
role
on
the
dialog.
The
XBar
and
our
Control
Chart
now
has
a
Constant
Subgroup
Size
option
in
case
you
don't
have
a
subgroup
that
you
want
to
specify.
There's
a
little
more
work
that
was
done
on
the
Three
Way
Control
Charts.
So
that
now,
not
only
can
you
specify
the
constant
subgroup
size
if
you
don't
have
a
subgroup
already
identified,
you
can
also
choose
your
Grouping
Method,
your
Between
and
Within
Sigmas
for
your
control
chart.
So
there's
different
options
that
are
added
on
the
Three W
ay
Control
Chart
dialog.
And
I
want
to
thank
you
very
much
for
your
time.
If
you
have
any
questions,
please
feel
free
to
ask.
Thank
you.
