Hi,
everyone.
Thank
you
for
joining
us
for
our
presentation
of
From
Data
to
Story:
Using
visualization
to
drive
continuous
improvement.
My
name
is
Allison
Bankaitis,
and
my
co-
presenter
is
Scott
W
ise.
A
little
bit
about
myself.
I
currently
supervise
a
small
team
of process engineers
at
Coherent
Incorporated,
but
I'm
still
very
involved
in
the
daily
process
engineering
efforts.
Previously,
I
held
various
process
engineering
roles
at
Corney
Incorporated,
and
I'm
very
excited
to
show
a
case
study
of
how
we
view
some
of
these
JMP
tools
in
our
process
engineering
work.
A
little
bit
for
Scott.
Thank
you,
Allison.
I'm Scott
Wise.
I'm
from
JMP
in
support
Allison's
JMP
usage,
as
well
as
other
customers
in
the
Northern
California
area.
And
I'm
just
real
excited
to
be
a
part
of
this
really
cool
case
study.
Hopefully,
you'll
pick
up
a
lot
of
best
practices
and
tips
from
some
of
the
things
that
helped
us.
All
right.
I
placed
the
abstract
here
for
future
reference,
but
just
wanted
to
highlight
a
few
things.
Coherent
has
placed
a
recent
focus
on
Lean,
which
aims
to
eliminate
waste.
Tools
from
JMP
have
aided
data
visualization,
which
in
turn
has
enabled
reduction
of
waste.
Another
advantage
of
these
tools
is
the
ability
to
engage
and
empower
employees
throughout
the
organization.
These
areas
will
be
the
focus
of
this
presentation.
Our
first
section
is
about
eliminating
waste
in
the
data
collection
process.
In
this
case
study,
we
had
a
data
collection
process
with
unnecessary
complexity.
It
used
to
take
20
minutes
to
process
one
part.
So
to
do
this,
we
had
built
a
data
query
and
access.
This
is
just
a
screenshot
here
showing
an
example
of
a
few
data
tables
where
we
combined
variables
from
various
tables
to
get
the
output
that
we
are
looking
for.
And
then
we
used
a
macro
to
pull
data
for
an
individual
part
into
Excel.
This is again,
just
a
screenshot
of
an
example
database
connection
in
Excel
and
the
code
that
we
would
write
in
Excel.
This
was,
again,
done
for
each
individual
part.
From
that
data,
then
we
could
attribute
data
in
Excel.
We
could
then
calculate
average
values
of
each
attribute.
We
would
then
pull
additional
data
from
our
MES
website,
such
as
part
type
or
other
items
listed
here.
Then
all
that
data
was
copied
into
an
Excel
summary
log.
So
we
maintain
the
log,
but
we
weren't
really
doing
anything
to
track
or
analyze
the
data.
With
JMP,
I
was
able
to
streamline
the
process.
I
built
this
framework
in
about
an
hour
and
reduced
process
time
to
five
minutes
per
part.
And
in
this
case,
this
is
just
for
one
engineer
myself,
on
one
product
that
I've
worked
on.
But
if
we
can
extend
this
to
multiple
products
and
multiple
engineers,
we
could
really
gain
a
lot
large
savings
of
time.
So
to
do
this,
I
built
a
data
query in
JMP,
which
included
both
the
attribute
and
MES
data
in
one
location.
Just
a
screenshot
of
there'd
be several
tables
here
pulling
in
the
data,
the
different
variables
here,
we
can
do
some
initial
filtering
in
the
data
query.
So
in
this
case
I
selected
a
time
frame
that
I
wanted
to
focus
on
and
then
a
subset
of
variable
that
I
down
selected,
so
I
don't
manage
both
data
set.
And
then
I
can
build
the
data
table
here
and
always
clean
up
more
of
the
data
later
on
as
necessary.
After
I
had
the
data,
I
replicated
some
of
the
charts
that
we
already
had
in
Excel,
just
made
them
very
similar
so
that
people
could
see
what
they're
used
to
dealing
with
for
the
time
being.
After
that,
again,
I
built
the
summary
table
to
replicate
what
they
were
used
to
seeing.
I
calculated
the
average
attribute
data
and
merged
the
original
table
and
the
tabulated
table
into
one
summary
table
so
that
they
could
have
an
output
what
they
are
used
to
seeing.
The
next
thing
is
to
take
this
data
and
move
from
data
to
story.
So
to
do
this,
the
first
thing
I
was
curious
to
know
was
what
does
the
data
tell
us
about
current
performance.
So
I
plotted
the
data
over
time
as
my
first
aspect
and
I
will
show
that
to
you
in
JMP.
So
just
using
this
graph
builder
and
the
timestamp
that
I
chose
and
then
the
main
output
that
I
started
looking
at,
added
that
to
the
chart
here.
What
I
used
to
do
is
manually
go
in
here
and
add
reference
lines
using
this
field
here.
But
what
Scott
showed
me,
which
is
really
neat
and
then
extends
to
all
of
the
graphs,
is
that
you
can
add
it
directly
to
the
data
table,
you
can
add
the
spec
limits.
You
just
go
into
the
Variable
of
Interest,
Column
Properties
and
go
down
to
Spec
Limits
and
add
the
values
in
here.
This
is
checked
so
that
you
can
see
the
graph
reference
lines
on
each
graph.
So
once
I
had
that
output,
I
could
see
that
there
is
a
large
amount
of
variation
in
the
data
and
many
of
the
values
were
outside
of
the
spec
limit.
So
the
next
thing
I
wanted
to
do
was
compare
additional
variables.
So
to
do
that
pretty
quickly,
I
was
able
to
just
add
the
column
switcher
to
this
graph
I
already
had
by
going
here
and
selecting
the
variable
I
wanted
to
change
along
with
the
other
variables
that
I
trust.
Then
from
here
I
can
quickly
click
through
all
these
variables
and
see
the
variation
in
each
one.
Next
for
me,
I
have
some
process
knowledge
and
I'm
sure
you
would
have
process
knowledge
of
your
situation
as
well.
Based
on
this
process
knowledge,
I
was
able
to
select
a
variable
that
I
thought
might
be
driving
some
of
the
variation.
In
my
case,
I
thought
that
X3
might
be
responsible
for
driving
some
of
these
trends
that
I
was
seeing.
I
put
that
into
our
graph
here.
The
other
piece
of
process
knowledge
that
I
have
is
that
our
spec is
based
on
the
average
value
for
each
part.
I
changed
this
to
mean
and
then
I
was
curious
to
see
the
line
of
fit
over
time,
so
I
put
that
and
added
that
here
as
well.
The
next
thing
I
was
interested
in
seeing
is
a
little
bit
more
about
performance
by
looking
at
a
control
chart.
To
see
the
control
chart,
analyze
quality
and
Process
Control
chart
builder,
and
I
was
curious
to
see
it
against
X2
,
which
is
a
part
number
and
that
same
variable
that
I've
been
looking
at.
A
gain,
I
was
going
to
split
it
out
by X3.
From
here,
we
can
see
there's
a
shift
in
the
average
based
on
which
subset
of
X3
.
A
lso
the
thing
that
was
obvious
to
me
is
that
the
sample
sizes
were
uneven.
To
me,
knowing
the
process,
I
know
they
should
each
have
10
collections
of
data
for
each
part.
So
based
on
our
process,
I
said,
well,
to
get
an
initial
look
at
the
performance,
I'm
going
to
limit
to
only
parts
that
have
10
measurements
per.
To
do
that,
we
made
a
new
data
table,
cleaned
up
the
data
again,
and
once
I
had
this,
I
recreated
the
control
chart
with
just
a
small
change.
Then
here
I
added
a
local
data
filter
to
have
X3
split
out
on
two
separate
graphs.
That
was
my
learning.
Now
I
can
see
these
upper
and
lower
control
limits
and
this
process
capability
chart,
since
now
I
have
the
even
subgroup
sample
size
of
tech.
That's
where
I
will
hand
it
over
to
Scott.
Thank
you
very
much.
All
right.
I'm
going
to
pick
up
with
the
rest
of
the
story.
Allison
has
done
a
great
job
of
understanding
where
the
current
performance
of
her
process
was.
But
we
also
thought
there
might
be some
other
key
variables
within
her
data
that
could
be
useful
for
explaining
these
differences
we're
seeing
in
the
output.
One
of
the
things
that
we
tried
was
actually
a
modeling
tool
that's
very
simple
and
often
used
to
screen
for
important
variables.
It's
called
a
partition.
In
this
partition,
all
you
have
to
do
is
of
course,
you're
going
to
pull
up
your
data
and
then
it
is
under
predictive
modeling.
People
call
this
a
decision
tree,
and
I'll
show
you
why
when
we
start
to
fill
it
out.
But
all
you
got
to
do
right
now
is
give
it
an
output
that's
our
21 there,
X
21,
and
get
it
the
inputs
we
want.
I'm
going
to
put
all
the
inputs
in
except
for
X2
,
which
was
the
kind
of
a
part
ID.
I'm
going
to
remove
that
one.
There
was
another
one
that
Allison
recommended
I
removed,
given
her
process
knowledge,
and
that
was
X8.
But
we'll
leave
all
the
others
in.
When
I
say
Okay,
it
brings
up
the
start
of
a
decision
tree.
What
it's
doing
is
saying,
I
can
make
a
bunch
of
splits
and
I'm
going
to
look
at
all
the
inputs
and
I'm
going
to
try
to
find
a
cut
point.
We'll
breaking
basically
any
of
those
variables
into
two
groups.
Will
that
give
any
explanatory
value
toward
the
differences
I'm
seeing
in
the
output?
In
this
case,
X
21.
So
if
you
make
the
first
split,
it's
saying
that
I've
explained
27%
of
all
the
difference
you're
seeing
in
the
output
via
just
splitting
X
19
at
500.
If
it's
greater
or
equal
to
500,
I'm
going
to
have
a
much
lower
mean
of
12.
67.
If
it's
less
than
500,
watch
out,
it
jumps
up
to
13.
This
is
really
cool
to
find
other
things
I
might
want
to
split,
break,
view
on
my
graphs.
You
can
continue
splitting
and
it
will
look
at
other
variables
like
X3
came
into
place
here
and
Allison
already
knew
that
was
going
to
be
an
important
variable.
A
s
you
keep
splitting,
you
can
see
it
starts
to
add
in
terms
of
the
predictability.
This
RSquare,
the
closer
to
one,
the
more
predictable.
So it's
like
56%
predictability
here.
I've
gone
ahead
and
done
that,
I'll
show
you
what
that
view
looks
like.
Here's
the
finished
view
I
came
up
with.
I've
got
these
nice
big
column
contribution
bars
here
at
the
bottom.
You
can
see
that
X
19
got
split.
Actually
found
five
cut
points
for
X
19,
but
52%
of
all
the
splits
it
was
doing
involved
X
19,
so
it
gave
it
a
nice
big
bar.
The
next
three
would
be
next.
Then
everybody
else
was
very
small
contribution
or
no
contribution.
It leads
us
to
say,
"Hey,
X
19
might
be
important
and
it
reinforces
X3
being
important."
Now
that
we
have
that
information,
well,
how
confident
are
we
that
these
things
do
belong
in
our
study?
Here
it
would
be
nice
to
look
at
X
21
by
X
19
broken
out.
This
one,
of
course,
is
going
to
be
just
simply
going
back
into
our
graph
builder.
This
chance
we
can
put
the
X
19
down
on
the
bottom
axis
so that would be the only X.
Let's
go
ahead
and
put
our
X
21
right
there
on
the
Y.
We
can
break
that
out
by
the
X3
variable,
which
is
pretty
cool.
Now,
one
thing
we
might
want
to
do,
X2 was
the
part
ID.
We
can
give
it
some
color
or
some
overlay.
Either
way,
I
think
I
will
just
go
ahead
and
give
it
some
color
here
and
I
will
turn
off
the
line.
That's
helpful.
But
what
would
be
helpful
is
to
use
that
local
data
filter
that
Allison
showed,
in
case
they
want
to
really
look
at
a
specific
sequence
of
parts.
I'll
go
under
the
red
hotspot
there,
that
red
triangle.
I'll
go
local
data
filter
and
then
we'll
add
the
X2,
and
beautiful.
Now
we
can
go
and
just
change
up
our
view
by
that
local
data
filter.
That
was
a
cool
view
that
we've
got.
I
can
see
that
it's
making
a
lot
of
differences
there.
Now
one
thing
you
might
ask
is
could
we even
model
this?
Before
I
even
go
and
model
it
so
we
can
make
some
predictions,
how
sure
am
I
that
X3
and
X
19
really
are
affecting
X
21?
Well,
we
can
actually
do
a
statistical
test.
We
can
test
means.
The
way
we're
going
to
do
that
here
is
we
are
going
to
go
back
to
our
data.
We're
just
going
to
go
to
Analyze,
fit
Y
by
X
and
now
we're
going
to
go
into
our
output.
We
want
to
look
at
things
either
the
effect
on
X
21
by
those
things
we
care
about,
X3
and
X
19.
I'm
going
to
put
them
both
in
here
and
it's
going
to
give
me
some
different
views.
It's
going
to
enable
me
to
compare
means
in
this
one
way
analysis.
I'm
going
to
right
click,
I'm
going
to
turn
on
the
means
test.
I'm
going
to
right
click
here.
I
even
like
this
all
pairs
too
key.
I'm
going
to
adjust
our
axis
here.
It's
got
these
cool
means
diamonds.
The
middle
of
your
diamond
is
the
mean.
The
edges
is
your
95%
confidence
around
the
mean.
The
way
it
works,
if
you
would
slide
these
things
over,
would
they
overlap?
It
looks
like
they
would
pass
like
ships
in
the
night.
There's
no
overlap.
As
well
as
you
got
these
comparison
circles,
you
can
click
on
one
and
see
if
the
other
one
turns
a
different
color.
A
ll
this
is
based
off
a
0.5
alpha.
What
does
that
mean?
That's
your
confidence
so
that's
95%
confident.
We'd
be
right
95
times
out
of
100
to
say
input
X3
does
have
the
level
there,
is
having
an
effect
on
what
my
observed
measures
are
for
X
21.
Given
that
before
I
go
and
try
to
fit
a
line
or
a
curve
line,
I
can
go
under
this
red
triangle
hotspot.
I
can
go,
you
know
what,
let's
go
ahead
and
group
by
X3
.
Now
when
I
go
back
under
this
triangle
option
and
I
go
to
fit
a
line,
or
in
this
case,
I
know
there's
a
little
curvature,
so
I'm
going
to
fit
a
quadratic
line or
polynomial
line.
Now
it
broke
it
out
by
X-3
so
I'm
really, really
excited
about
that
one.
The
blue
line,
which
is
his
first
version,
3-0,
there's
the
formula
for
it.
It
only
has
20%
explainability.
It's
not
a
great
fit,
but
you
can
see
that
jumped
up
to
near
70%
predictability for the 3_1.
It's
telling
me
that
I've
got
not
only
significance
in
saying
X3
is
different
and
I'm
seeing
a
difference
when
it
comes
to
X
19
by
X
21,
but
it
matters
for
X
19
what
level
of
X3
we're
talking
about.
That's
why
the
red
line
and
the
blue
line
are
not
on
top
of
each
other.
Therefore,
that's
an
interaction.
If
I'm
going
to
try
to
predict
something,
I
need
to
include
that.
So
at
this
point,
I
think
I
have
all
that
we're
going
to
need
to
do
to
get
in
the
hands
of
Allison
Spears,
a
really
cool
tool
that
can
help
them
predict
what
the
output
is
going
to
be
based
on
settings
of
X3
and
X
19.
You're
seeing
on
the
screen
a
profiler
that
comes
off
our
modeling
platform,
and
it's
very
easy
to
go
and
set
up.
If
we
go
back,
I'm
going
to
go
to
the
fit
model
here.
We'll
do
our
output
for
X21
again.
Under
my
inputs,
I
know
X3
and
X
19,
there
it
is,
are
very
important.
I
told
myself
X3
and
X
19
might
need
to
be
crossed,
I
might
need
to
see
those
interactions.
I
know
for
X19,
there's
some
curvature.
The
way
I
would
check
for
this
is
I
go,
I'd
select
X
19,
I
go
under
this
macros,
and
I'd
say
polynomial
two
degree.
I
have
it
set
at
two
so
I
would
get
this
curve
term,
polynomial
term
here.
There's
the
interaction,
and
these
are
the
main
effects,
so
it's
really
two
factors,
but
it's
the
four
things
in
my
model.
So
I'm
just
going
to
run
it
and
it's
going
to
try
to
fit
a
line.
This
should
look
very
much
like
the
fit
Y
by
X.
It's
only
really
explaining
52%.
This
model
is
only
explaining
52%
of
the
differences
I'm
seeing
in
x
21.
Not
perfect,
but
it's
pretty
much,
think
about
it,
just
for
having
two
factors
and
their
interaction
in
one
of
their
curve
terms,
that's
pretty
good.
But
what
I
can
do
now
under
that
red
hotspot
is
turn
on
the
profiler.
This
is
worth
the
price
of
admission.
This
right
here
is
going
to
enable
Allison
and
her
team
to
sit
there
and
talk
about
what
settings
we
should
have.
S
hould
I
be
at
v3_1
?
Should
I
be
at
v3_0
for
this
x_3
input?
Should
I
be
low
or
high?
A
gain,
it
shows
that
interaction
live.
For
example,
I'll
shift
this
color
here.
Watch
what
happens
when
I'm
low.
I'm
sorry,
not
low
but
high
on
X
19,
I'm
way
out
here
to
500.
By
the
way,
you
can
type
in
what
you
care
about.
Maybe
I
want
to
see
what
it's
at
480.
Look
how
flat
that
line
is
between _0
and
_1.
It doesn't
really
matter
which
one
I
select.
I'm
going
to
get
the
same
kind
of
prediction.
The
red
is
my
prediction,
and
the
blue
around
it
here
is
my
confidence
interval
around
that
prediction.
Of
course,
this
wouldn't
be
good
because
I'm
right
on
the
lower
spec
limits.
Watch
what
happens
when
I
start
to
pull
it.
Well,
I
might
be
happier
here
with
version
3-0
in
a
setting
around
350
because
that
gets
me
close
to
the
targets.
But
if
I
keep
going
up
here,
you
see
how
steep
this
line
is
begin,
and
I
definitely
don't
want
to
be
on
version
3_1.
Because
it
has
a
steeper
line
and
it
has
made
this
slope
very
steep.
It's
all
coming
out,
but
it's
interactive
in
this
profiler
and
now
we
can
play
with
what
would
be
the
right
settings for
if
I
had
to
stay
with
version
3-1.
If
I
go
to
version
3-0,
what
would
be
the
right
settings
here?
They
might
be
different
settings.
There's
always
multiple
optimal
settings
you
can
select.
This
is
really
cool.
We
now
have
the
ability
to
predict.
All
right.
Continuous
process
improvements.
All
this
was
great.
We
now
have
a
faster
way
to
get
our
analysis
done.
We've
gone
through
a
flow
that
enable s
us
to
find
what's
important
and
see
what's
important.
But
what
if
we
want
to
use
that
information
to
monitor
over
time
and
continually
improve
our
process?
It
might
be
nice
to
have
for
different
levels
of
X-3
a
dashboard.
Allison
and
I
worked
to
create
a
standard
type
of
dashboard
that
her
team
is
used
to
seeing.
They're
used
to
seeing
control
charts
first,
and
then
the
process
capability
around
their
specs.
Then
next
they
would
want
to
see
the
output
over
time.
That's
the
top
chart
in
the
middle
there
and
then
below
that
if
there's anything
else
they
should
worry
about.
That
was
our
big
finding,
that,
"Hey,
x
19
has
an
effect,"
so
they
would
want
to
see
that.
Lastly,
on
the
right
hand
side,
we
put
a
table
with
the
average
means
for
the
output
of
interest,
plus
even
some
more
outputs
they
like
to
take
a
look
at.
Of
course,
we
want
this
to
be
interactive.
So
how
can
we
build
this
dashboard
for
level
zero
and
level
one?
We're
going
to
bring
up
our
data
here.
I
think
I
already
have
it
opened
up
here.
I
will
go
now
and
just
create
in
just
one
swoop.
This
is
why
it's
nice
to
be
able
to
save
your
graphs,
your
analysis
back
to
data.
I'm
just
going
to
click
and
create
a
whole
bunch
of
views
here
that
are
going
to
replicate
what
the
team
wants
to
see.
Here
is
that
control
chart
builder
for
the
X
bar
and
R.
Next,
we
have
the
process
capability.
Next,
we
have
that
output
over
time.
Next,
we
have
the
output
over
the
X
19
that
we
wanted
to
show.
Now
we
have
the
table.
I
have
all
the
elements,
and
if
you
have
all
the
elements,
you
don't
have
to
save
them
back
to
make
someone
run
them
one
at
a
time.
You
can
combine
them
into
a
dashboard
template
and
it's
under
File,
New
Dashboard.
It
will
allow
you
to
pick
some
type
of
template
to
start
off
with.
I'm
just
going
to
pick
this
blank
template.
Now
it's
got
all
my
reports,
all
the
graphs
and
tables
and
things
I've
opened
on
the
left.
Now
I
can
just
bring
into
the
body
of
the
dashboard
what
I
care
about
and
I
can
orient
things
the
way
I
would
like
to
see
them
on
my
dashboard.
When
I'm
done,
it's
easy
to
go
and
run
that
dashboard
and
then
later
save
that
dashboard
when
I'm
ready.
But
I've
already
got
that
run
here.
So
I'm
going
to
close
down
the
dashboard
builder.
I'm
going
to
show
you
the
dashboard
we
have
already
created
to
capture
all
this
information.
With
one
click
of
the
button
here's,
my
dashboard.
And
boy,
beautiful
looking
dashboard
here,
just
the
way
I
want
to
see
it.
Now,
the
thing
that
we
loved
about
this
was
your
ability
as
well
to
still
use
the
junk
dynamic
linkage.
I
can
select
a
couple
of
high
points
and
I
can
see
where
they
will
flow
in
the
other
graphs.
I
can
even
see
down
here
where t
hey're
highlighted
to
my
table.
So
this
is
great,
but
what
about
that
X3
variable?
We
knew
we
wanted
to
be
able
to
create
separate
dashboards
for
each
of
those.
So
instead
of
using
a
local
data
filter,
I'm
going
to
use
a
global
data
filter.
It's
actually
under
your
Rose
venue.
It's
right
at
the
bottom.
This
one
affects
all
graphs,
all
analysis.
It
affects
what's
hidden
and
selected
back
to
your
data
table.
On
this
one,
I'll
just
go
ahead
and
put
X3
.
Now
when
I
click
on
Show
and
Include,
I'll
turn
off
the
select
so
I
can
make
my
own
selections.
Now
I
can
toggle
between
that _0
and
that
_1
.
Now
it
works
the
same
way.
I
can
see
things
that
were
out
of
control
or
out
of
spec
here
for
just
version
3-0,
then
I
can
do
the
same
thing
for
3-_1.
There
we
go.
We
have
a
nice
tool
that
can
be
really
used
to,
again,
not
get
data
just
quicker
and
not
just
do
one
analysis,
but
actually
make
this
a
continuous
process
improvement
tool
that
we
can
use
day
in
and
day
out
to
quickly
get
the
view
we
want
and
ask
the
questions
we
need
to
drive
improvements.
All
right.
So
that
is
our
story
of
moving
from
data
to
story,
I
should
say.
We
wanted
to
leave
you
with
where
to
learn
more,
where
to
get
more
information.
Of
course,
we're
going
to
give
you
the
presentation.
We're
going
to
give
you
the
journal
we
use
so
you
can
replicate
these
views
we're
seeing.
But
Allison
and
I
felt
that
if
you
were
wanting
to
really
get
started
with
JMP,
go
to
the
Getting
Started
with
JMP
webinars
that
we
have.
So
it's
on
the
JMP
website,
will
include
links
in
the
journal,
and
it
covers
about
everything
we
showed
you
today.
We
had
a
few
more
tips
and
tricks,
but
the
new
user
welcome
kit
is
another
really
good
thing
to
take.
This
one
allows
you
to
work
with
a
data
set,
it gives
you
a
data
set
that
you
can
follow
along,
and
it's
really
nice
step-
by-
step
instructions.
We're
both
big
fans
of
the
Statistical
Thinking
for
Industrial
Problem Solving.
Free
online
learning,
basically
E-learning
course,
and
you
have
so
many
different
places
you
can
do.
I've
used
this
to
do
just
in
time
learning,
and
I've
had
a
lot
of
people t
ake
all
the
sections
just
to
get
up
to
speed
on
everything
JMP
can
do
to
help
you
compare
and
describe
and
predict
all
those
fun
things
you
want
to
do.
Don't
forget,
if
you
have
specific
things
you
want
to
do,
we
do
have
Mastering
JMP
webinars
that
are
available
here.
The
JMP community, communityjmp
.com
is a good place to look
for
just
in
time
learning,
and
as
well,
JMP
Education,
if
you
want
to
get
more
of
the
underlying
theory
on
how
a
lot
of
these
things
work.
We
do
a
lot
of
public
training
or
can
customize
training
for
you
as
well
Just
talk
to
JMP
Education.
All
right.
I
will
allow
Allison
to
say
a
few
words
when
we
finish.
But
thanks,
everybody,
for
joining
us,
and
we
hope
you
picked
up
on
a
few
things
you
would
like
to
try
within
JMP.
Thanks,
Scott.
Thanks,
everyone,
for
joining
us.
It
was
really
a
pleasure
to
share
this
case
study
from
Coherent
with
you
and
to
share
all
the
new
cool
tricks
that
Scott
has
taught
me
and
that
we've
learned
through
our
journey
with
JMP
at
Coherent.
So
thanks
again
and
take
care.
Bye.
