Hello,
everyone.
I'm
Bill
Worley,
and
along
with
our
special
guest,
Nick
Strasser,
from
Bas
Enzymes,
we're
going
to
be
talking
to
you
about
some
important
JMP
tools
for
fermentation
processes,
more
specifically,
ethanol
production
in
a
batch
system.
I'm
going
to
turn
it
over
to
Nick
for
a
quick
introduction,
and
then
I'll
give
a
little
bit
more
introduction
on
myself
then,
as
well.
Yes,
hello.
Thank
you,
Bill.
Everyone,
my
name
is
Nick
Strasser.
Like
Bill
said,
I
am
a
technical
support
specialist
for
BASF
Enzymes.
I've
been
supporting
the
fuel
ethanol
industry
in
North
America
for
about
10
years
and
have
found
JMP
to
be
an
extremely
valuable
tool
for
analyzing
production
and
fermentation
processes
happening
at
ethanol
producers
facilities.
I'm
excited
to
be
here
today
with
Bill
and
presenting
this
material.
Hopefully,
if
you
are
an
ethanol
producer,
this
material
will
be
directly
applicable.
If
you
are
not,
some
of
the
things
we'll
talk
about,
definitely
you
can
apply
it
across multiple
different
industries.
I'm
just
excited
to
be
here
and
look
forward
to
it.
Thank
you,
Nick.
A
little
bit
more
background
on
me.
My
name
is
Bill
Worley.
I'm
a
principal
systems
engineer
with
JMP.
I've
been
with
JMP
almost
10
years.
Prior
to
that,
I
worked
for
Procter
and
Gamble,
and
that's
where
I
say
I
grew
up
on
JMP.
I've
got
a
background
in
chemistry
that's
got
a
master's
degree
in
chemistry,
but
I've
been
around.
I've
worked
for
a
few
other
companies
over
the
years.
We'll
just
leave
it
at
that
and
we'll
go
from
there.
All
right,
Nick.
All
right,
to
get
us
started,
I'd
like
to
talk
about
some
general
data
management
fundamentals,
things
that
I
have
found
to
be
valuable
in
the
ethanol
production
industry.
First
and
foremost,
the
things
that
you
see
us
go
through
today,
they're some
of
the
screenshots
we
take
and
the
demonstrations
you'll
see
Bill
go
through,
we
will
be
using
the
latest
version
of
JMP,
JMP
17.1.
You
may
be
using
a
previous
version
of
JMP,
and
that's
perfectly
fine.
Some
of
what
you
see
here
might
be
standard
functions
in
17,
but
you
might
have
to
poke
around
a
little,
find
them
somewhere
else
in
16.
If
they're
available,
they
might
just
be
in
a
different
spot.
Then
some
of
the
version
17
functions
are
completely
new
and
might
not
be
available
in
older
versions.
I
also
want
to
quickly
mention
here
the
compatibility
with
Excel.
Microsoft
Excel
does
have
a
JMP
add-
on
toolbar.
If
you
know
how
to
activate
a
toolbar
in
Excel,
you
can
go
find
it
there
and
have
that
added
to
your
Excel.
For
ethanol
producers,
Excel
is
oftentimes
the
intermediary
between
their
laboratory
management
systems
and
their
data
analysis
with
JMP.
Just
some
regular
best
management
practices
here.
In
the
ethanol
production
industry,
if
you
are
a
batch
fermentation
facility,
keep
a
master
spreadsheet
of
all
of
your
batches
in
one
place.
It's
important
that
it's
all
in
one
place,
so that
you
can
leverage
the
different
things
that
are
going
on,
either
process-
wise,
or
chemical-
wise,
or
ingredient-
wise.
Also
understand
which
data
are
important
to
you.
Sometimes
I
talk
to
a
customer
and
they're
only
backwards-
looking.
They're
collecting
the
data
of
things
that
happened
and
not
necessarily
the
inputs
or
the
changes
that
got
them
there.
Make
sure
that
you
have
a
spot
to
collect
almost
any
information
you
can
think
about
that
might
become
useful
looking
backwards
later
on.
I
highly,
highly,
highly
recommend,
and
most
producers
do
this
now,
add
a
trial
column
to
your
data.
As
you
collect
your
information,
as
your
batches
mature
and
drop
and
your
production
goes
along,
make
sure
you
add
a
column
that
you
can
denote
whether
you
are
trying
something
new,
whether
it's
an
enzyme,
or
a
chemical,
or
even
a
process.
Maybe
it's
even
a
new
crew.
Maybe
you're
switching
up
your
crew
leads
or
something
like
that.
Make
it
a
trial
and
put
it
in
a
column
where
you
can
go
back
and
reference
that.
In
a
similar
vein,
have
an
upset
column.
Upsets
are
like
trials
that
we
didn't
want
to
have
happen.
But
it's
good
to
note
that
there's
maybe
an
upset
condition
if
for
no
other
reason
than
a
week
or
two
or
month
or
two
later.
This
was
an
out-
of-
ordinary
thing
and
we
can
maybe
disregard
some
of
the
numbers.
All
right.
Like upsets,
data
entry
errors
are
going
to
happen,
so
make
sure
that
you
spend
some
time
cleaning
your
data.
This
is
actually
a
very
important
step
and
it
can
be
rather
time-
consuming.
You
have
multiple
options.
My
biggest
point
of
emphasis
would
be,
clean
the
data
at
the
source
if
you
can.
Go
all
the
way
back
to
where
maybe
a
misentry
was
entered
and
correct
it
there.
That
way,
if
you
ever
have
to
go
back,
you're
not
constantly
cleaning
the
same
errors
over
and
over
again.
I
would
say,
clean
up
the
source
if
you
can.
Clean
and
JMP
if
you
have
to.
You
have
options
in
JMP
that
might
help
you
identify
things
that
need
to
be
cleaned
and
options
for
cleaning
them
up.
You
can
use
the
option
to
recode.
You
can
explore
outliers
or
missing
values.
Oftentimes,
I
see
data
entry
errors
when
I
create
a
control
chart.
Maybe
a
number
that's
normally,
say,
between
15
and
20
shows
up
as
170.
Well,
to
me
that's
a
misplaced
decimal.
It's
probably
supposed
to
be
a
17
and
somebody
misplaced
a
decimal
and
we
ended
up
with
170.
You
can
easily
see
that
in
a
control
chart.
Find
it
and
correct
it.
Then
I
would
also
say,
always
make
sure
you
check
your
data
type
in
JMP.
If
you
have
a
column
that's
supposed
to
have
a
numerical
entry
and
somebody
enters
a
character,
that's
going
to
become
a
problem
later
on
when
you
want
to
do
some
statistical
analysis.
All
right,
one
of
my
favorite
topics
here.
I
really
want
people
to
use
these
terms:
common
cause
and
special
cause.
If
you
have
variation
in
your
data,
it's
for
one
of
two
reasons
or
maybe
both
put
together.
It's
either
a
common
cause
reason
or
a
special
cause
reason.
Common
cause
reasons
are
reasons
that
occur
naturally
because
your
equipment,
your
people,
and
your
processes
have
certain
limitations.
This
is
what
we
call
your
noise
in
your
data.
This
is
just
normal,
everyday
little
variations
in
your
data
because
of
the
imperfect
nature
of
everything
that
you're
using.
There's
also
a
special
cause
variation.
Special
cause
variation
happens
for,
like,
when
things
malfunction.
Maybe
an
environmental
condition
changes,
or
maybe
you
have
a
process
or
an
input
change.
A
process
or
input
change
can
be
special
cause
that
maybe
you
weren't
expecting
to
happen.
But
it
could
also
be
a
trial.
Like
we
said
before,
keep
track
of
that.
Special
cause
variation,
a
process
input
change.
That's
going
to
be
very
valuable
information
down
the
road.
Okay,
next
we're
going
to
talk
a
little
bit
about
the
data
types
that
JMP
will
recognize.
Continuous,
nominal,
and
ordinal.
Continuous
data
types
are
your
number
data
types.
Numbers
anywhere
from
negative
infinity
to
infinity
contain
a
decimal
place
in
ethanol
production.
A
great
example
of
this
would
be
an
HPLC
result,
a
temperature,
a
PH,
a
fermentation
time.
Nominal
data
is
a
type
of
data
that's
typically
a
character.
It's
the
name
of
something.
Sometimes
we
use
numbers
to
name
things,
and
in
ethanol
plants
we
do
that.
But
it's
like
a
recipe
input.
For
example,
a
yeast
or
GA,
a
fermenter
number.
If
you
have
a
trial
condition
or
an
upset
condition,
that's
probably
also
going
to
be
a
character
expression,
and
it
will
become
a
character
type
of
data
or
nominal
data.
Then
we
also
have
ordinal
data.
Ordinal
data
shows
a
certain
order
or
progression.
A
great
example
of
this
in
batch
fermentation
would
be
a
batch
number.
We
could
have
named
our
batches
any
names
we
want,
but
it
really
makes
sense
to
our
human
brains
to
use
a
number,
make
it
sequential
so
we
know
which
batch
happens
first,
which
batch
happens
later,
so
on
and
so
forth.
Why
does
all
this
matter?
Why
does
the
type
of
data
matter?
Well,
depending
on
what
you
want
to
accomplish
with
JMP,
the
program
is
going
to
look
for
different
types
of
data
to
match
up.
For
example,
if
you
want
to
create
a
bivariate
analysis,
you
will
have
to
have
continuous
data
plotted
against
other
continuous
data.
If
you
want
to
do
a
one-
way
ANOVA
test,
you
will
have
to
have
continuous
data
plotted
against
nominal
or
ordinal
types
of
data.
Let's
dig
in
a
little
bit
deeper.
Bill
and
I
are
going
to
assume
that
there's
a
certain
background
level
of
knowledge
here
on
importing
data
from
JMP,
but
I
just
wanted
to
point
to
some
of
these
things
just
to
know
that
you
have
options.
Most
often
what
I'm
doing
is
I'm
opening
JMP,
and
then
I
go
to
Open,
and
I'm
looking
for
a
file.
Oftentimes,
that's
an
Excel
file.
Make
sure
you
select
all
file
types
in
the
drop
down
menu,
and
then
all
of
your
file
types
will
show
up.
You
can
find
your
Excel
file,
open
it,
and
go
through
the
Import
wizard.
If
you've
already
done
that,
or
if
you
like
to
maybe
just
update
a
table
that
already
exists,
you
might
have
Excel
open
on
one
half
of
your
screen
and
JMP
open
on
another.
You
might
just
be
copying
and
pasting
maybe
new
material
into
an
old
file,
just
updating
it.
In
addition,
if
you're
like
me
and
you're
constantly
going
back
to
the
same
Excel
files
where
I'm
keeping
all
of
my
data
organized
and
in
one
place,
JMP
will
have
a
source
script.
As
long
as
I
haven't
moved
or
renamed
that
source
file,
that
Excel
source
file,
I
can
easily
open
my
JMP
file,
click
Source
Script,
and
it
will
bring
in
all
the
information
that's
in
that
file,
whether
it
be
new
or
old.
You
have
all
sorts
of
other
methods.
There's
the
Excel
toolbar,
the
JMP
feature
in
the
Excel
toolbar,
you
can
just
open
a
new
table
and
start
dropping
data
in,
and
new
for
JMP
17,
there's
something
called
Workflow
Builder.
We're
not
going
to
get
into
all
these.
I
just
want
you
to
know
you
have
options.
To
quickly
point
out
here,
we're
not
going
to
spend
a
lot
of
time
on
this.
What
you'll
see
Bill
and
I
using
here
for
cursors
are
the
arrow,
the
selection,
the
grabber,
and
the
lasso.
Those
are
the
tools
that
we're
going
to
use
most
often.
Other
tools
are
available,
and
if
you
have
questions
about
these,
there's
a
lot
of
good
resources
out
there
to
do
some
training
with
JMP
directly
or
on
their
website.
You
can
check
those
out.
But
these
are
the
cursor
controls
that
we'll
be
using.
Getting
into
the
nitty-
gritty
here,
and
I'm
going
to
start
analyzing
my
data.
But
before
I
do
that,
one
really
important
question.
Because
JMP
is
going
to
assume
that
your
data
are
normally
distributed,
it
might
be
good
practice,
or
it
is
good
practice,
for
you
to
know
ahead
of
time
whether
a
certain
type
of
data
should
be,
is
expected
to
be,
is,
or
isn't
normally
distributed.
On
the
left
side
of
the
screen
here,
you'll
see
your
typical
normal
bell
curve
distribution.
These
data
are
basically
normally
distributed.
JMP
is
going
to
assume
all
of
your
data
is
like
this.
On
the
right
hand
side
of
my
screen,
I
see
some
data
that's
not
normally
distributed,
it's
skewed
a
little
bit.
Just
from
an
ethanol
production
fermentation
process,
my
total
sugar
at
drop,
I
would
never
expect
to
be
normally
distributed.
I
would
expect
it
when
there's
upset
conditions,
that
upset
only
draws
my
sugars
one
direction
and
never
the
other.
This
is
perfectly
normal.
But
if
I'm
going
to
get
into
some
more
advanced
data
analysis,
that's
going
to
be
something
good
for
me
to
keep
in
mind.
Once
you've
done
it and you know,
you
probably
don't
have
to
do
it
again.
All
right,
let's
get
into
what
I
would
consider
the
bread
and
butter.
The
main
feature
in
JMP
that
I
use
when
I'm
looking
at
my
process
and
want
to
know
if
it's
in
control,
is
a
control
chart.
Process
control
charts,
as
we
talked
about
before,
can
show
you
control,
can
show
possible
outliers,
and
they
can
be
used
to
very
quickly
get
a
visual
and
mathematical
representation
of
means
for
different
phases
of
something
like
perhaps
a
trial
or
maybe
a
month-
to-
month
comparison
on
how
my
operation
is
going.
The
basic
control
chart
output
is
going
to
look
like
this.
What
you
see
here,
I
would
say,
is
probably...
Most
of
this
is
that
type
of
data
that
would
be
considered
within
my
normal
noise.
I
don't
see
anything
here
that
really
screams
at
me
like,
"This
is
an
outlier,
this
is
a
data
entry
error,"
or
anything
like
that.
Your
basic
control
chart
output
will
look
like
this.
If
you
want
to
dress
it
up
a
little
bit
on
the
left
side
there,
you
can
select
for
zones
and
zone
shades.
This
is
going
to
shade
your
zones
that
are 1
,
2,
and
3
standard
deviations
from
your
average.
Again,
this
is
developed
by
or
used
a
lot
in
the
automotive
industry
where
you're
doing
some
Six
Sigma
improvement
projects.
This
type
of
visual
is
really
useful
for
any
kind
of
Six
Sigma
projects
you
have
going
on.
In
addition
to
the
zones
and
shadings,
we
can
also
use
some
rules
that
have
been
developed
by
the
industries
to
say,
"Is
my
process
in
control
or
out
of
control?"
Here
we
have
an
example
of
a
violation
of
a
warning.
I
believe,
Bill,
when
you
and
I
talked
about
this,
it
had
something
to
do
with
the
two
data
points
being
a
certain
width
apart,
a
certain
too
many
standard
deviations.
I
can't
recall
if
it
was
2
or
3.
But
here
we
have
one
data
point
that
was
quite
a
bit
higher
and
quite
a
bit
different
than
the
previous
data
point,
and
it
threw
up
a
warning.
Aside
from
control
charts
comparing
means,
it's
another
very
useful
tool.
In
the
ethanol
production
industry,
it
is
used
all
of
the
time.
When
you
have
a
trial
going
on,
something
that
you
wanted
to
know,
"Is
this
process
any
different?
Is
it
statistically
significant,
and to
what
degree?"
We're
going
to
take
a
look
at
comparing
means,
and
it's
going
to
be
used
to
look
at
a
continuous
set
of
data
against
ordinal
or
nominal
types
of
data.
Again,
we
talked
about
that
process
upset
condition
or
a
trial
condition
being
a
nominal
type
of
data.
Comparing
means
analysis.
First
and
foremost,
take
a
look.
Is
this
right
for
me?
It
is
appropriate
to
use
when
you
have
large
data
sets.
My
rule
of
thumb
is,
30
data
points
or
more.
If
you
have
few
outliers,
outliers
can
really
pull
your
average.
If
it's
time
constricted
or
time
restricted
to
a
local
or
small
amount
of
time.
In
that
time,
all
other
conditions
have
been
controlled
to
the
best
of
your
ability,
it
would
not
be
appropriate
with
small
data
sets
where
outliers
can
skew
the
mean
calculation
pretty
drastically.
I
wouldn't
use
this
over
a
broad
range
of
time,
say,
comparing
now
to
three
years
ago,
where
conditions,
an
unknown
number
of
conditions,
would
probably
be
different
between
now
and
three
years
ago.
If
you're
continuously
improving,
you're
going
to
want
to
keep
this
kind
of
analysis
to
a
certain
time
restriction
as
well.
How
do
we
get
into
this?
We
want
to
do
a
fit,
X
by
Y.
In
the
example
you're
going
to
see
here,
we're
going
to
use
phase.
This
would
be
a
trial
phase
as
our
X-
factor
and
ethanol
to
liq
solids,
or
how
much
ethanol
are
we
getting
for
the
amount
of
corn
we're
putting
in
as
the
Y-
response?
The
example
that
is
kicked
out
to
us
shows
that in
this
particular
example,
looking
at
the
diamonds
and
circles
and
then
looking
down
at
the
connecting
letters
report.
This
is
an
example.
First
of
all,
by
default,
JMP
is
going
to
assign
a
95%
confidence
interval.
Here
we
see
that
the
mean
differences
shown
by
the
connecting
letters
report,
they
have
two
different
letters.
For
each
phase
of
this
trial,
there
are
no
overlapping
letters.
We've
got
a
baseline
and
then
we've
got
a
trial.
What
JMP
is
telling
us
here
is
that
these
averages
are
statistically
different
with
95%
confidence.
Looking
at
a
little
bit
more
complicated
situation,
you
might
find
something
like
this.
I
love
this
example.
It
already
violates
what
I
said
about
large
data
sets.
We've
got
like
4
or
5
data
points
in
the
baseline
and
then
just
a
few
points
for
a
yeast
trial,
and
then
a
couple
more
baseline
points.
What
JMP
is
telling
us
here,
if
we
looked
at
the
connecting
letters
report,
that
the
baseline
and
the
yeast
trial
are
significantly
different,
but
it
also
says
that
baseline
two
is
connected
to
both
the
yeast
trial
and
the
baseline
with
no
statistically
significant
difference.
Maybe.
I'm
going
to
eyeball
that.
Do
this
pass
the
eyeball
check?
Boy,
baseline
and
baseline
two
look
really
similar.
I'm
going
to
dig
a
little
bit
deeper.
If
I
look
down
at
my
order
differences
report,
I
can
see
that
my
yeast
and
baseline
did
pass
a
95%
confidence
level
for
being
statistically
different.
But
if
I
look
at
yeast
and
baseline
two,
it
just
barely,
barely
missed
the
cut
off.
If
I
were
to
go
back
and
assign
a
90%
confidence,
then
I
would
get
a
result
that
I
would
expect.
That
my
baseline
and
baseline
two
are
basically
the
same
and
that
the
yeast
trial
was
different
with
a
90%
confidence.
Okay.
Thanks,
Nick.
I'm
going
to
dive
in
a
little
deeper.
Actually,
we'll
show
you
a
few
things,
few
slides,
and
then
we'll
get
into
JMP.
But
we're
going
to
talk
about
bivariate
and
multivariate
analysis
first.
This
is
for
use
with
continuous
data
against
other
sets
of
continuous
data.
This
is
a
bivariate
example
where
we're
looking
at
ethanol
liq
solids,
versus
another
term
called
liq
solids.
We
can
see
that
by
this
line
here
that
there's
really
no
correlation,
especially
if
you
look
at
the
R
squared
down
here.
This
has
a
very
small
R
square,
which
indicates
that
there's
no
correlation
between
these
two
variables.
But
it
is
pointing
at
that
there
is
a
general
decline
in
the
fermentation
yield
as
solids
are
increased,
or
that
the
widest
variation
was
noticed
around
this
33%
line.
We
can
further
play
around
with
that
and
add
some
colors
and
see
where
we
might
see
some
differences
there
as
well.
The
next
step
in
this
would
be
to
say,
"Okay,
we're
looking
at
a
multivariate
example."
We're
looking
at
a
bunch
of
different
process
parameters
and
saying,
"Okay,
are
they
correlated
in
any
way?"
We've
got
these
numbers
that
tell
us
that
we
can
look
at
the
lines
and
see
which
way
they
slope.
We
can
look
over
here
where
we've
got
the
correlation
coefficients
matched
up
with
the
particular
squares.
Those
numbers
are
telling
us
whether
something
might
be
correlated
or
not.
The
redder
it
is,
the
higher
the
correlation
in
a
positive
direction,
and
the
bluer
it
is,
the
more
it's
correlated
in
a
negative
direction.
One
important
note
here
is
that
correlation
does
not
imply
causation.
That's
a
very
important
thing
to
remember.
Also,
we're
going
to
look
at
Graph
Builder.
This
is
maybe
more
of
an
artistic
than
analytical
type
of
analysis,
but
it
allows
you
to
look
at
all
kinds
of
different
properties
and
you
can
play
around
with
it.
You
can
remove
and
hide
items
with
column
switcher
and
a
data
filter.
You
can
change
access
label
orientation.
You
can
modify
spacing.
There's
all
kinds
of
different
things
you
can
do.
This
is
an
example
of
how
you
can
do
that.
This
is
a
kinetics
graph
for
showing
by
phase
what
the
ethanol
and
sugar
are
doing
over
time.
We've
got
those
that
we
can
play
with
and
I'll
show
you
how
to
make
one
of
those
in
a
minute
or
so.
Then
here's
another
instance
where
we're
looking
at
by
batch
and
by
day.
We've
got
ethanol
and
sugar.
We've
got
our
sample
age
up
on
the
upper
grouping
axis
here,
and
then
we
have
batch
number
down
here
on
the
lower
x-
axis.
One
other
tool
that
I
want
to
talk
about
before
I
get
into
JMP
itself
is
the
advanced
control
charting,
which
we
call
model
driven
multivariate
control
charts.
This
is
especially
useful
when
you're
looking
for
processes
that
appear
to
be
in
control,
but
they
still
have
batches
that
are
failing.
You
might
have
all
of
your
individual
control
charts
are
showing
that
everything
is
in
control,
but
you're
still
getting
batches
that
fail.
This
model
driven
multivariate
control
chart
will
help
you
better
understand
that.
You
can
see
this
is
just
some
of
the
output
that
you'll
get
there.
With
that,
I
am
actually
going
to
step
out
of
PowerPoint
and
do
this.
All
right.
I
should
still
be
sharing.
I've
got
my
JMP
window
up.
L
et's
go
ahead
and
start
off
with
making
these
graphs.
I've
got
the
data
in
a
stack
format
for
right
now.
This
is
that
graph
that
I
showed
you
before
.
This
is
one
of
them
and
I'm
going
to
show
you
how
to
make
it.
It's
real
quick.
Let's
just
go
to
Gaph
Bilder
and
I'm
going
to
pull
this
one
out
of
the
way
so
it
doesn't
get
in
our
vision
here.
We're
going
to
pull
in
phase
and
you
can
see
things
light
up,
these
drop
zones
light
up
as
I
pulled
the
data
in
or
the
column
in,
but
I
want
to
group
that
.
Then
I
want
to
go
ethanol
here
and
sugar
here
.
We've
got
the
two
and
that's
actually
not
the
way
I
want
that
to
show
up,
so we're
going
to
redo
that.
I'm
going
to
do
an
Undo
here
and
start
over.
Let's
do
sugar
and
ethanol
first.
We'll
pull
those
in
as
one.
There
we
go.
Then
we're
going
to
pull
in
sample
age
and
now
we'll
pull
in
that
phase
and
now
we've
got
that
and
we're
going
to
add
a
line
to
this.
We're
going
to
pull
in
the
smoother,
and
that
adds
those,
and
we'll
hit
Done.
That's
that
graph.
That's
basically
the
kinetics
as
ethanol
and
sugar
go.
Ethanol
goes
up
and
sugar
goes
down
over
time.
We
can
see
that
there
might
be
some
slight
differences
in
the
two
baseline
and
trial,
but
we'd
have
to
dig
deeper
to
really
get
more
into
that.
That's
that
one
graph.
I
think
I'll
just
move
on
to
the
model
driven
multivariate
control
chart.
That's
a
good
idea
on
how
to
build
that,
use
that
Graph
Builder.
But
let's
look
at
the
model
driven
multivariate
control
chart.
Again,
I
have
these
things
pulled
up.
Let's
go
to
Analyze
and
let's
go
to
Multivariate
Method
or Q
uality
and
Process
Model D
riven M
ultivariate
Control
Charts.
Actually,
this
isn't
the
right
table,
so
let
me
pull
up
my
other
table.
There
we
go.
Click
this
off.
We'll
go
to
Analyze,
Quality
and
Process,
Model D
riven M
ultivariate
Control
Charts.
We're
going
to
just
pull
in
a
group
of
different
processes.
I'm
just
going
to
pull
all
these
in.
We're
going
to
try
and
see
where
we
see
differences.
We
could
put
a
time
ID
in
here,
but
we're
just
going
to
go
ahead
and
say O
kay.
Now
we're
seeing
that
we're
getting
some
batches.
We're
looking
at
all
those
components
together,
all
those
process
steps
together.
Now
we're
showing
that
we've
got
some
things
that
are
out
of
control,
so I'm
going
to
highlight
these
out
of
control
batches.
This
is
all
done
with
principal
components.
It's
saying
it
takes
eight
principal
components
to
explain
at
least
85%
of
the
variation
that
we're
seeing.
Let's
highlight
those,
show
their
contribution
plots.
Now
we
can
see
that
we're
getting
an
idea
of
what
are
the
issues
here.
Let's
sort
the
bars
so
that
we
have
ones
where
we're
getting
a
lot
more
variation
seen
in
some
of
the
samples
versus
others.
That
these
others
and
then
we've
got
one
that
says
it's
out
of
control.
We
can
look
at
that
individually
and
say,
"Oh,
wow,
yeah,
we've
got
a
point.
At
least
one
point
that's
out
of
control
for
that
individual
batch
there,
or
that
sample."
Sample
23.
One
other
thing
you
can
do
here,
too,
is,
let's
add
a
monitored
process,
let's
go
to
the
score
plot.
Now
we
can
see
that
down
here,
we've
got
the
batches
that
we're
looking
at
and
then
we've
got
the
individual
points.
Again,
we
can
look
at
their
individual
plots,
but
let's
make
that
group
A.
Then
we're
going
to
go
back
up
here,
take
those
off
and
highlight
this
other
grouping
here.
Make
that
group
B.
I
don't
know
if
you
can
see
in
the
background
there
that's
highlighting
in
the
data
table.
That's
a
nice
way
of
selecting
the
data
for
you
to
show
that.
Then
we've
got
the
bars
here.
Now
we've
got
an
idea
that
this
prop
send
cell
count
is
one
of
the
bigger
drivers
as
to
why
we're
seeing
differences
between
batches
that
are
out
of
control
versus
in
control.
That's
just
a
nice
tool
to
try
and
work
with
there.
I'm
going
to
get
rid
of
this
for
now
and
I'm
going
to
go
back
to
the
PowerPoint.
Get
that
right.
Let's
step
up
again.
Part
of
another
tool
within
JMP,
and
this
is
getting
deeper
into
the
power
of
JMP,
is
something
called
Functional
Data
Explorer
in
JMP.
Let
me
get
rid
of
this.
Then
this
allows
you
to
analyze
data
that
is
captured
over
time.
You
can
compare
curves
to
a
standard
or
golden
curve.
With
chemometric
data,
you
can
preprocess
and
analyze
that
data,
and
then
you
can
use
this
Functional
Data
Explorer
to
do
a
qualitative
review
of
the
curve
data.
One
of
the
things
that
might
be
used
in
the
ethanol
industry,
one
of
the
tools
is
something
called
near-
infrared
spectroscopy,
and
that's
used
to
measure
inbound
corn
composition.
This
Functional
Data
Explorer
will
allow
you
to
do
that.
Some
of
the
other
things
you
might
want
to
look
at
are
like
spectral
data,
HPLC,
or
Chromatographic
data,
mass
spec
data.
But
the
JMP
Pro
features
in
Functional
Data
Explorer
allow
you
to
do
the
preprocessing
and
analyzing
of
spectral
data
or
functional
data.
It's
pretty
straightforward.
Then,
just
so
everybody
knows
what
I'm
talking
about
here,
functional
data
is
any
data
that
unfolds
or
develops
over
some
sort
of
continuum.
These
continuums
are
listed
down
here
below,
and
time
is
one
of
those
continuums.
This
is
an
example
of
visualizing
the
functional
data
that
we're
talking
about.
This
is
some
HPLC
data,
this
is
NMR
data,
and
then
this
is
near-
IR
spectral
data.
How
this
is
used
for,
or
how
this
is
used
in
the
ethanol
industry
is
to
do
something
like
this.
We
looked
at
that
sugar
and
ethanol
data
before,
but
we
really
couldn't
compare
batch
to
batch,
right?
Not
that
well.
We
couldn't
get
any
quantitative
view
of
that.
Well,
with
Functional
Data
Explorer,
this
allows
you
to
look
at
these
by
batch,
and
you
can
fit
a
model
to
them
and
see
where
things
fall
out.
We'll
show
you
that
in
just
a
second.
Then
this
is
just
an
output
from
some
near-
IR
data
where
we've
looked
at
60
batches
of
gasoline
looking
for
the
octane
rating
and
then
the
output
that
we
get
out
of
that.
Now
I'm
going
to
step
out
here
and
go
back
to
our
data
sets
here
and
get
rid
of
this
one
and
pull
back
up
our
stack
data.
Let's
go
to
Analyze.
This
is
Specialized
Modeling.
Functional
Data
Explorer.
We're
going
to
do
this
in
the
stack
format,
and
we're
going
to
look
at
ethanol
and
sugar.
That's
our
Y
output.
We're
going
to
use
a
time
this
time,
but
we're
going
to
use
continuous
time.
We're
not
going
to
use
the
categorical
time.
We
need
the
ID
function.
Then
we
can
put
Phase
and
let's
say,
Amylase
Type
in
there
as
some S
upplemental
variables.
Say
Okay.
Now
we're
going
to
fit
this
data.
I'm
going
to
do
a
JMP
tip
here.
I'm
going
to
hold
down
my
Ctrl
key.
I'm
going
to
go
to
the
red
hotspot
and
go
to
Models
and
say
Wavelengths.
This
will
fit
both
models
at
the
same
time.
One
of
the
things
you're
going
to
find
out
is
that
wavelengths
need
a
grid
and
a
needling
space
grid.
We're
going
to
say
Okay
there,
and
say
Okay
for
the
next
one.
Now
we
have
models
fit
for
both
the
ethanol
and
the
sugar.
This
is
ethanol,
this
is
the
sugar
data.
If
we
come
down
here
a
little
bit,
we
can
see
that
each
one
of
the
batches
that
we
have
are
using
these
wavelength
type
functions.
We
can
go
a
little
deeper,
we
can
look
at
the
fit
here
where
we
have
multiple
shape
functions
and
we
can
say,
"Okay,
why
are
these
different?"
If
we
hover
over
them,
we
can
look
at
that
and
we
can
plot
that
out.
But
we
can
also
come
down
here
and
play
around
with
a
function
here
where
we
can
look
at
the
functional
principal
components
and
say,
"Okay,
how
did
these
things
change
over
time?"
This
gives
us
a
pretty
good
indication
that
as
we
increase
these
functional
principal
components,
but
we
don't
know
what
those
parameters
are,
things
are
going
to
change
with
the
output
or
the
consumption
of
the
sugar.
We
get
a
qualitative
view
of
what's
going
on
there.
Then
last
but
not
least,
with
all
these
things,
you're
going
to
want
to
share
this
data.
I'm
going
to
go
back
to
PowerPoint
one
more
time
here.
This
is
an
image
of
a
JMP
Live
output
where
we've
looked
at
a
couple
of
the
graphs
where
we've
got
ethanol
and
sugar
comparing
over
time
by
batch
number
and
then
by
sample
age
here,
comparing
both
the
baseline
and
trials
that
way.
That's
a
nice
way
to
look
at
that.
Let
me
show
you
this,
in
a
live
JMP
Live
image,
take
me
a
second
to
get
that.
Pull
that
in.
This
is
a
live
JMP
Live
image
of
those
graphs.
This
allows
anyone
that
has
JMP
Live
to
investigate
this
data.
This
is
especially
important
when
you're
trying
to
share
data
with
your
management
or
your
team.
If
you
have
upsets
in
your
analyzes
or
in
the
process,
this
is
a
great
way
to
share
that.
You'll
get
warnings
about
things
that
are
out
of
control,
if
you're
looking
at
control
charts.
I've
got
other
dashboards
or
other
images
that
I
can
look
at.
We
can
go
over
here
and
look
at,
let's
see,
chemical
production.
Then
I
can
look
at
the
data
this
way
where
it's
a
model
that
we
can
say,
"Okay,
how
are
things
reacting
over
time?"
Then
this
is
something
called
a
prediction
profiler
where
we
can
say,
"Okay,
what
happens
to
the
model
when
I
change
reactors?"
Or
in
this
case,
trials
or
whatever
it
happens
to
be
in
the
ethanol
world?
With
that,
I
believe
this
is
all
we've
got.
We'll
say
thank
you,
and
we'll
go
from
there.
Nick,
any
last
words?
Yeah,
I
will
just
say
again,
Bill,
thank
you
to
you
and
the
JMP
team.
For
those
users
out
there,
if
you're
an
ethanol
producer
or
not
an
Ethanol
producer,
you
probably
have
your
2
or
3
or 4
analysis,
what
I
would
call
the
bread
and
butter
of
what
it
is
you're
doing.
You
do
it
over,
and
over,
and
over
again.
Don't
be
afraid
to
reach
out.
Learn
something
new.
Learn
a
new
feature
about
those
things
that
you
use
often.
But
also,
don't
be
afraid
to
grow
a
little
bit
on
the
depth
of
your
understanding
of
what
JMP
can
do.
We
are
so
data-
rich
and
information
poor.
JMP
can
help
you
take
that
data,
turn
it
into
real
actionable
information,
and
it
tells
a
great
story,
and
it
can
really
help
an
organization
out.
Reach
out,
find
Bill,
find
somebody
at
JMP,
find
myself.
Always
happy
to help.
Thanks
again.
Thanks,
Nick.
Just
as
a
word,
Nick
and
I
had
done
a
presentation
earlier
this
year
based
on
this
same
data
set.
If
you're
interested,
reach
out
to
us.
We
can
get
those
links
for
you
so
you
can
watch
those
presentations.
It
was
a
couple
of
days.
We
did
it
over
a
couple
of
days.
A
little
more
in
depth
than
what
you're
seeing
today,
but
thank
you.
