All
right.
Hi.
My
name
is
Wendy,
and
I'm
a
technical
lead
working
with
health
and
Life
Sciences
in
the
Midwest.
My
motivation
for
this
talk
comes
from
working
with
JMP
customers
and
prospects
across
a
broad
range
of
industries.
When
I
reflect
on
my
customers' questions
over
the
past
five
years,
the
biggest
opportunity
with
data
analysis
has
been
in
using
all
the
data
that's
being
collected
to
make
decisions.
With
sensors
becoming
more
prevalent
in
measurement
systems
and
manufacturing
equipment,
more
data
is
being
collected
over
time.
It's
cheaper
to
collect
and
store
the
data,
and
as
a
result,
scientists
and
engineers
are
often
swimming
in
data.
They
want
to
use
the
data
to
better
understand
their
processes,
to
make
better
products,
but
they
have
a
knowledge
gap
when
it
comes
to
how
to
process
all
the
data.
This
talk
is
intended
to
be
an
introduction
in
understanding
how
to
work
with
data
collected
over
time.
What
do
I
mean
by
timestamp
data
or
data
collected
over
time?
I
have
some
examples
here.
It
could
just
be
a
time
or
a
date
or
both
a
date
and
a
time.
This
data
can
be
formatted
in
many
different
ways.
When
I
say
timestamp
data,
I'm
referring
to
all
of
these
variations,
and
it
really
is
the
granularity
at
which
your
data
is
stored.
It's
a
pretty
broad
category,
but
this
is
what
we're
talking
about
today.
The
title
of
my
presentation
is,
If
I
only
had
Time
to
Work
with
Timestamp
Data,
I
would
learn
so
much
about
my
process.
I
would
almost
retitle
that
to
say,
If
I
only
had
the
knowledge.
I've
seen
people
struggle
with
working
with
this
type
of
data,
like
wrestling
with
a
pig,
because
they
simply
don't
have
some
of
the
fundamentals
of
understanding
how
JMP
recognizes
this
data
and
how
to
marry
data
together
by
timestamp.
It
doesn't
have
to
be
like
this.
Today,
what
I'm
hoping
you
walk
away
with
are
two
things.
One
is
just
some
basics
about
timestamp
data,
and
then
an
introduction
to
JMP
Query Builder
if
you've
never
used
this
before,
to
join
Timestamp
Data.
All
right,
so
let's
start
with
the
ABCs.
I'm
bringing
open
a
spreadsheet
here,
and
we're
going
to
bring
this
data
into
JMP
to
just
talk
about
some
of
the
basics
when
it
comes
to
timestamp
data.
I've
got
three
columns
here.
We're
first
going
to
focus
on
start
date
and
end
date.
Excel
is
a
very
common
way
that
raw
data
is
stored.
Let's
bring
this
into
JMP.
Okay?
Let's
first
focus
on
the
Start and End date.
I'll
hide
my
special
column
here
just
for
a
second.
Let's
first
focus
on
the
way
that
this
data
came
in.
All
of
these
columns
actually
came
in.
JMP
is
recognizing
them
as
nominal
data.
The
first
thing
you
want
to
know
about
timestamp
data,
if
you
want
to
work
with
it,
is
that
it
should
be
stored
in
JMP
as numeric
and
continuous.
Let's
first
open
up
Start
Date
and
just
take
a
look
here.
JMP
recognized
it
as
character
and
so
therefore
it
made
it
nominal.
We
need
to
communicate
to
JMP
that
this
is
date
data
or
timestamp
data
by
changing
it
to
numeric
and
continuous.
That
is
the
type
of
data,
dates,
times
and
timestamp
data
need
to
be
coded
in
JMP.
Now
I'll
keep
this
open
and
I'm
going
to
click
apply.
You
can
see
that
the
values
change
.
So
this
is
correct
.
This
looks
nonsensical
to
us,
but
this
is
the
other
thing
you
need
to
know
about
timestamp
data
is
the
way
it's
stored
in
JMP
is
the
number
of
seconds
from
a
reference
date.
It's
specifically
the
number
of
seconds
from
January
1st
1904
.
You
don't
need
to
remember
the
January
1st
1904
.
You
just
need
to
remember
that
dates,
times
and
timestamps
are
stored
as
the
number
of
seconds.
The
way
that
we
make
it
look
sensible
to
us
is
by
changing
the
format.
I
almost
think
of
the
format
as
a
mask.
This
is
correct.
The
software
understands
this,
but
I
want
to
be
able
to
read
it
easily.
Now
let's
work
with
the
format.
I'll
go
to
this
drop
down
and
I
am
just
going
to
call
out
these
three
menus.
There's
a
date
menu,
a
time
menu,
and
then
a
duration.
If
you're
working
with
timestamp
data,
these
are
the
three
menus
that
are
going
to
be
relevant
to
you
in
order
to
format
the
data.
We'll
talk
about
a
special
case
in
a
second.
Let's
format
this
in
the
same
way
that
it
appears.
Because
this
is
formatted
as
a
date,
sorry,
day
and
then
a
month
and
then
a
year,
let's
make
the
selection
that
preserves
that.
I'm
going
to
select
this
particular
option,
click
'Apply'
to
confirm
that
that's
what
I
want
to
see.
Now
I'm
good
to
go.
Notice
again
in
the
columns
area.
Now
start
date
is
numeric
and
continuous
and
now
JMP
knows.
January
26
is
five
days
after
the
21st.
It
doesn't
know
that
right
now
for
end
date.
Now
let's
do
the
same
thing
with
end
date.
This
is
going
to
allow
us
to
subtract
the
two.
To
do
date
time
math.
I'm
going
to
come
in
here
again,
numeric
and
continuous.
We'll
click
'Apply'
.
That's
the
number
of
seconds
from
that
reference
date.
Now
I'm
going
to
change
the
format.
Okay,
so
now
I
have
numbers
that
I
can
perform
math
with
.
I
can't
perform
math
on
character
data.
If
you
want
to
do
something
like
calculate
a
date
difference,
now
we
can
do
that
now
that
our
data
is
being
stored
and
JMP
properly.
Let
me
show
you
one
way
that
you
can
work.
Do,
let's
say,
column
math
with
dates.
I'm
going
to
select
these
two
columns.
I'm
going
to
right
click,
and
I'm
popping
into
this
new
formula
column
menu.
I
can
ask
JMP
to
help
me
write
this
formula.
Because
I
have
two
columns
selected,
I'm
going
to
go
to
this
combined
menu
and
tell
JMP
I
want
to
take
the
difference.
I'll
take
the
difference
in
reverse
order
because
I
want
end
date,
minus
start
date.
There
you
go.
Again,
I'm
not
alarmed
because
I
remember
that
JMP
stores
this
type
of
data
in
seconds.
To
convert
this
to
another
unit,
let's
say
days,
we
just
need
to
work
with
this
formula
a
little
bit
more.
Let's
do
that,
I'm
going
to
hit
the
plus
sign.
Now
I'm
in
the
Formula
editor
.
We
can
convert
this
to
days
by
dividing
this
by
60
seconds
are
in
a
minute,
60
minutes
are
in
an
hour,
and
then
there's
24
hours
in
a
day.
I'll
click 'Okay'
.
Now
we
can
confirm,
right,
that
this
is
the
difference
between
the
two.
You
can
just
do
some
mental
math
here.
It's
not
too
challenging.
That
looks
good.
I
want
to
present
you
another
way
to
do
date
time
math
or
timestamp
math.
We
could
use
the
Formula
editor.
Let's
go
ahead
and
create
a
new
column.
I
will
right
click
and
go
to
the
full
Formula
Editor.
There
is
a
more
comprehensive
list
of
date
time
functions
in
the
full
Formula
Editor
than
you're
getting
in
the
data
table
when
you
right
click.
This
date
difference,
if
I
hover
over
it,
you
can
get
a
peek
of
what
it
offers
you.
It
allows
you
to
specify
an
interval
name.
I'll
double
click
on
that
to
bring
it
into
the
view.
Now
let's
tell
JMP
that
we
want
to
take
that
date
difference
with
the
two
dates,
and
then
we
can
specify
the
units
that
we
want
.
We
don't
have
to
do
the
60
X
60
X
24.
I'll
put
the
end
date
first,
start
date,
and
then
my
interval
name
does
need
to
be
in
quotations.
This
little
hover
help
does
let
you
know
that
that's
the
structure.
Let's
click
'Okay'.
Now
I
can
see
I
went
backwards
on
that.
Let's
double
click
in
here.
We
just
need
to
swap
those
two.
Start
date
should
be
the
first
one.
There
you
go.
These
are
the
same
values
we
got
when
we
performed
that
division.
Two
different
ways
to
perform
these
calculations.
But
the
big
takeaway
is
to
familiarize
yourself
with
some
date
time
formats.
Not
formats,
but
calculations.
You
can
do
that
by
exploring
these
quick
formulas.
It's
via
a
right
click
in
the
data
table.
There's
a
date
time
menu
here
or
in
the
full
Formula
Editor
that's
going
to
be
this
very
large
menu
of
various
date
time
formulas.
Okay,
so
now
let's
wrap
up
the
ABCs
by
looking
at
this
special
date
time
column
that
I
hid.
I'll
unhide
it
so
that
we
can
focus
on
it.
Now
this
is
a
full
week
day
of
the
week
name.
You
see
the
date
and
the
time.
Let's
try
formatting
this,
or
I
should
say
communicating
to
JMP
how
this
data
should
be
stored.
I'm
going
to
right
click
and
go
into
column
info
just
like
we
did
with
Start and End date.
I'm
going
to
go
to
numeric
and
continuous
.
Because
that's
how
this
type
of
data
needs
to
be
stored
in
JMP.
I'm
going
to
click
apply.
Now
I'm
disappointed
because
JMP
has
basically
deleted
all
those
values.
It's
telling
me
that
it
does
not
recognize
that
information
as
being
a
date
.
It worked,
we
got
seconds
when
we
did
that
with
Start
and
End
Date,
but
we're
not
getting
that
here.
This
tells
us
that
we
need
to
work
a
little
bit
harder
because
this
is
a
special
format
to
tell
JMP
that
this
is
time
data
or
date
time
data.
We're
going
to
leverage
and
undo
here
to
get
our
data
back.
Now
I'll
right
click
go
back
into
column
info
and
here
let's
first
do
numeric
continuous.
Here
we're
going
to
go
to
the
format
menu
and
I'm
going
to
introduce
format
pattern.
We
talked
about
dates,
time
durations.
Here's
where
you
want
to
go
if
you
have
a
special
situation
like
we
have
here.
Now
I'm
going
to
say
set
format
pattern.
Now
the
process
is
about
communicating
to
JMP.
Each
element
in
that
it
is
a
date,
time
duration
or
other.
See
this
other
menu.
That's
what
we're
going
to
do
is
each
piece
we're
going
to
tell
JMP
what
it
is.
Wednesday,
I'm
kind
of
focusing
on
this
first
line.
What
is
that?
Make
this
a
little
bit
bigger.
Well,
that's
the
day
of
the
week
.
It's
the
full
name.
I'm
going
to
make
that
selection.
Then
I
do
have
to
tell
JMP.
"Okay,
well,
I
have
a
comma
next,
and
a
space".
And
now
I
have
the
full
month
next.
I'm
just
scrolling
and
finding
that
in
the
menu.
Here
is
month
long
name.
I'm
going
to
make
that
selection.
Again,
I
have
a
space
and
now
I
have
a
two
digit
day
and
then
I
have
a
comma
and
then
I
have
a
year.
It's
a
four
digit
year
.
You
can
see
it
being
a
little
bit
of
a
preview
of
what
I'm
specifying
here.
That's
really
the
process.
Just
to
kind
of
shorten
this
up,
I'm
going
to
do
my
little
cheat
thing
and
I'm
just
going
to
paste
in
the
full
thing
.
We
built
it
up
to
year,
but
the
process
would
be
the
same
for
telling
JMP
about
the
hour,
minute
and
AM, PM.
This
is
a
game
changer
for
those
of
you
who
have
special
formatting.
I
do
see
this
more
and
more
often
with
the
various
pieces
of
equipment
that
are
collecting
data
over
time.
All
right,
so
now
let's
look
to
see,
now
JMP
does
recognize
this
as
date
time
data.
We
have
numeric
and
continuous,
and
we
can
see
via
the
formatting
that
this
data
is
correct.
Okay,
let's
do
some
review,
and
then
we'll
push
on
to
part
2
here.
What
have
we
talked
about
so
far?
JMP
recognizes,
let's
call
it
timestamp
data
broadly
as
the
number
of
seconds.
You
don't
need
to
remember
the
January
1st 1904
.
Just
know
that
it's
the
number
of
seconds.
The
data
should
be
stored
as
numeric
and
continuous,
and
the
format
is
the
way
that
you
make
it
legible
or
understandable
to
you.
JMP
understands
it
as
the
number
of
seconds.
We
need
to
view
it
as
a
specific
type
of
format
so
that
we
can
understand
it.
The
last
thing
I'll
say
is
develop
a
familiarity
with
date
formulas.
We
had
a
chance
to
do
that
both
in
the
data
table
with
those
quick
formulas
and
then
in
the
full
formula
editor.
Oh,
yes,
I
said
that
was
the
last
thing.
But
that
special
case
we
addressed
with
the
special
date
times,
if
that's
your
situation,
you
can
deal
with
it
using
set
format
pattern
so
that
you
can
ultimately
work
with
that
special
formatting
of
timestamp
data.
Okay,
now
we're
going
to
go
to
part
two,
and
this
is
where
we
are
going
to
do
something
that
I
have
seen
lots
of
customers
struggle
with,
which
is,
they
want
to
align
data
by
timestamps,
but
they
can't
do
it
because
of
a
lack
of
awareness
of
tools.
That
tool
is
going
to
be
JMP
Query Builder.
We're
going
to
just
dive
into
an
example
that
I
think
will
illustrate
this
particular
challenge.
All
right,
so
here's
a
case
study.
We
are
making
this
product
called
Forever
Young
Elixir,
who
doesn't
want
that
product.
To
make
Forever
Young
Elixir,
here's
a
little
bit
of
a
sketch
of
that
process.
We
start
with
raw
materials.
Then
these
raw
materials
go
into
a
pressure
step.
This
is
very
abstract.
Then
they
go
into
a
temperature
step,
and
then
we
get
our
finished
product.
What
we're
most
concerned
about
with
respect
to
this
finished
product
is
potency
.
We
want
high
potency
in
our
finished
product.
Every
finished
batch,
we
take
a
sample
and
we
measure
potency.
So
here
you
can
see
this
is
a
sample
of
the
data
that's
being
collected.
This
first
batch
finished
at
10:00
AM
on
6:28,
and
it
had
a
potency
of
95.41%,
etc
.
I
have
one
row
per
batch
because
I
get
one
potency
measure
for
each
batch.
I
also
collect
temperature
data
over
time.
There's
a
sensor
on
this
temperature
step
that
continuously
measures
the
temperature
that
let's
call
it
my
product
in
process
is
going
through.
I
have
the
same
thing
for
my
pressure
step.
There's
a
sensor
here
too,
and
I'm
collecting
pressure
over
time.
I'm
making
this
product
and
I
had
some
ideas
about
how
to
set
the
process.
But
I
want
to
know,
can
I
make
this
better?
Can
I
get
more
batches
that
are
of
higher
potency,
more
consistently
higher
potency
batches?
I
want
to
use
my
sensor
data
to
figure
out
is
there
a
correlation
between
the
temperature
and
potency
and
ultimately
also
pressure
and
potency,
and
where
should
I
try
to
get
that
process
to
sit
at
in
these
steps
to
get
to
maximize
potency
.
That's
what
we're
marching
towards.
I
want
to
identify
temperature
settings
that
are
going
to
give
me
the
highest
potency.
Let's
focus
on
this
graph
here.
I'm
plotting
every
single
batch
here
and
its
potency
value
on
the
y
axis,
and
I
have
the
average
temperature
that
it
was
experiencing
in
that
temperature
step.
We're
going
to
focus
on
temperature,
but
these
steps
would
be
analogous
for
that
pressure
information
as
well.
This
is
what
I'm
marching
towards.
Being
able
to
build
a
graph
like
this
allows
me
to
see,
oh,
I
can
maybe
maximize
potency
by
maintaining
a
temperature
setting
of
between
63
and
maybe
68
.
This
is
where
I'm
getting
the
highest
potency
values.
We
need
to
ultimately
get
a
data
table
that
has
both
potency
values
and
temperature
values
so
that
we
can
perform
this
correlation.
A
little
bit
of
an
asterisk
here
only
because
there
are
certainly
other
ways
to
analyze
this
type
of
data.
We're
going
to
take
the
average,
but
there
are
certainly
other
tools
and
JMP
and
other
approaches
one
could
take
to
analyze
this
type
of
data.
We're
not
going
to
address
that
here.
Just
kind
of
putting
it
out
there
as
another
thing
to
explore.
We're
going
to
stay
focused
on
this
data
preparation
aspect
of
working
with
this
data.
When
we
have
this
data
aligned,
let's
focus
on
batch
1, 2, 3, 4, 5.
We
have
one
potency
value
for
batch
1, 2, 3, 4, 5,
that's
the
95.41,
and
that's
why
you
see
it
across
all
the
rows.
I
only
have
one
value
for
that
batch,
but
I
have
many
values
for
temperature
.
It's
just
sort
of
streaming
and
being
collected.
How
do
I
do
this
alignment?
What
do
you
do,
when
you
don't
have
a
batch
ID?
That's
really
kind
of
the
crux
of
the
issue.
When
we
have
unique
identifiers,
we
can
join
that
data
by
those
unique
identifiers.
When
we
do
not,
how
do
we
address
this
problem?
That's
what
we're
going
to
talk
about
now.
Really
the
first
step
before
even
getting
into
JMP
is
to
think
about
the
process.
With
a
hypothetical
batch
finishing
at
09:10
AM,
when
is
it
inside
of
this
temperature
step?
What
is
the
relevant
time
range
for
a
finished
batch
that
finishes
at
09:10
AM?
That's
not
a
software
question.
This
is
a
let's
understand
the
process
question
.
In
talking
to
the
manufacturing
engineers,
we've
determined
that
a
batch
spends
five
minutes
in
this
temperature
step
before
it
becomes
a
finished
product.
We're
going
to
use
this
five
minutes,
and
we're
going
to
calculate
from
the
finished
product
timestamp
and
determine
that
for
a
9:10
finish,
the
product
or
the
pre
made
product
started
in
this
temperature
step
at 9:05AM.
That
is
the
work
ahead
of
us
in
the
software
is
to
associate
the
sensor
data
using
that
five
minutes
to
that
end
product
.
We've
got
the
finished
time
for
the
Forever
Young
Elixir
batch,
and
we
need
to
collect
the
sensor
time
that's
relevant
for
that
finished
product.
Okay,
so
let's
dive
into
the
software.
Here
is
our
potency
data.
We
have
one
row
per
batch,
100
batches
in
here.
Let's
go
take
a
look
at
the
temperature
sensor
data.
This
is
being
collected
almost
every
second.
We
have
500
rows
in
here,
so
no
batch
ID.
this
is
the
challenge
that
Query Builder
is
going
to
help
us
with.
If
I
scroll
down,
you
can
see,
oh,
some
missing
values,
maybe.
I
don't
know
if
the
sensor
was
down,
that
can
happen.
Our
first
step
really
is
we're
going
to
work
with
this
temperature
data
I'm
sorry,
the
potency
data.
We're
going
to
calculate
a
start
time.
A
start
time
meaning
when
did
it
start
being
in
that
temperature
step.
Let's
do
some
labeling
because
we're
going
to
have
a
lot
of
timestamps
here.
I'm
going
to
call
this
batch
finish
timestamp.
Now
let's
do
some
math.
We're
going
to
subtract
five
minutes
from
the
batch
finish
timestamp.
Let's
go
to
the
formula
editor,
and
I'm
going
to
select
batch
finish
timestamp,
and
I'm
going
to
say
minus
five
now,
because
I
know,
we
all
know
now
that
JMP
likes
to
work
in
seconds.
This
five
won't
work.
I
would
need
to
change
five
to
seconds,
or
I
could
ask
JMP
to
do
that
for
me
.
I
could
change
this
to
5 X
60.
Or
I
can
come
here
and
say
that
five
is
in
minutes.
Now
let's
check
it
out.
Again,
we're
not
alarmed
because
we
know
we
just
need
to
change
the
mask,
right,
the
format.
Let's
change
it
to
a
timestamp.
We'll
make
it
consistent
with
what
we
have.
There
we
go.
We'll
call
this
temperature
start
time.
For
batch
1, 2, 3, 4, 5,
that
finishes
at
10:00 AM
we're
going
to
start
collecting
that
sensor
start
time
at
9:55.
Let's
go
pop
over
to
Potency
data,
sorry,
temperature
data.
If
you're
used
to
using
the
join
function
in
the
Tables
menu,
you
don't
want
to
do
that
for
date
data
or
timestamp
data.
What
you
want
to
use
is
JMP
Query Builder.
That's
what
we're
going
to
use.
I'm
going
to
join
temperature
sensor
data
with
potency
data.
I'm
going
to
double
click
in
here
to
set
the
join
criteria.
This
is
going
to
allow
us
to
set
two
criteria
for
aligning
this
data.
The
first
one
is
I
want
to
collect
temperature
data
that's
greater
than
my
temperature
start
time,
or
I
should
say
greater
than
or
equal
to
my
temperature
start
time.
In
my
potency
data
that
I
calculated.
I
have
a
second
criterion
that
I
have,
which
is
I
want
to
stop
collecting
data
for
a
particular
batch
and
use
the
batch
finish
time.
We
did
greater
than
before
and
now
it's
a
less
than.
I'll
click 'Okay'
.
Now
let's
build
the
query.
We've
told
the
software
how
we
want
to
align
these
rows
and
now
we're
going
to
go
to
the
next
window,
which
is
the
build
query
part.
First
step
is
we
need
to
tell
the
software
which
columns
we
want
in
the
resulting
table.
I'll
just
add
all
of
the
columns
and
you
can
see
you
get
this
preview
down
here,
right,
so
we
can
start
to
see
do
some
sanity
checks
on
the
data.
This
is
the
join
that
we're
about
to
do.
Does
it
look
correct?
I
think
what's
worked
well
for
me
is
to
focus
on
a
single
batch
so
we
can
pick
on
1, 2, 3, 4, 5.
This
first
batch
here,
I
can
see
that
I
have
a
single
potency
value.
Then
I
can
also
see
that
I've
collected
temperature
values
that
are
inside
that
window
of
9:55
and
10:00
AM.
This
looks
good.
Now,
because
I
just
have
one
potency
value,
I
do
need
to
summarize
that
temperature
data,
if
I
want
to
create
a
correlation.
This
is
where
we're
going
to
use
an
average.
I
could
run
the
query
and
get
this
raw
data
and
then
summarize
using
table
summary.
But
I
want
to
show
you
how
you
can
do
this
in
Query Builder.
We'll
stay
in
this
window
and
we're
going
to
use
this
aggregation
option
for
temperature.
We
need
to
get
rid
of
some
of
our
columns
here
because
we
don't
actually
want
this
level
of
granularity.
We
want
this
all
at
the
batch
ID
level.
I'm
going
to
get
rid
of
timestamp,
which
is
the
timestamp
for
the
temperature
sensor.
I
can
keep
the
batch
finish
timestamp
because
there's
only
one
value
for
each
batch
ID
and
I
don't
necessarily
need
this
temperature
start
time.
We
can
reorganize
this
in
a
more
logical
way
.
Maybe
we
start
with
batch
ID,
we
preserve
the
finish
time
and
then
we've
got
our
Potency
and
our
temperature.
Now
we're
going
to
go
to
the
temperature
one
and
take
an
average
.
Certainly
you
could
calculate
additional
statistics
as
well.
Maybe
you
want
to
also
look
at,
you
could
look
at
a
min
value.
You
could
look
at
a
max
value
.
There
are
many
other
ways
to
kind
of
look
at
this
data.
We're
just
going
to
stick
to
temperature
so
it
looks
like
we
maybe
had
some
sensor
data
where
we
didn't
have
batch
finished
batch.
Okay,
so
this
looks
good.
Let's
just
do
one
more
sanity
check.
Batch
1, 2, 3, 4, 5,
the
average
temperature
is
56
and
we
have
this
potency
value.
You
could
certainly
do
more
checks.
That's
something
I
would
recommend
is
just
to
go
to
your
raw
data
and
just
confirm.
But
this
looks
good.
We're
ready
to
create
the
table.
At
this
point,
I'm
going
to
say
run
query
and
we're
ready
to
look
at
correlations
.
I've
got
this
blank
row,
I
could
just
delete
this
guy,
but
now
I
have
Potency
and
average
temperature
for
each
batch.
Now
I
can
go
into
graph
builder
and
look
at
that
relationship.
Here's
potency,
here's
temperature
and
maybe
we
add
a
model
to
it
.
Let's
change
this
to
a
line
of
fit.
It
certainly
doesn't
look
like
it's
linear,
probably
better
modeled
as
quadratic.
We're
done.
We
could
add
some
more
statistics
here,
but
this
is
really
a
nice
starting
place
where
we
can
start
to
see,
to
make
statements
like
I'm
seeing
higher P
otency
values
when
temperature
is
maybe
between
64
and
68.
I
should
go
back
to
my
process
and
try
to
see
if
I
can
set
the
temperatures
to
stay
within
that
operating
window.
Okay,
so
with
that,
I'll
conclude
and
again,
just
remember,
when
you're
working
with
timestamp
data
and
you
want
align
rows,
think
of
JMP
Query Builder,
don't
think
of
Tables
Join.
You
really
need
to
set
to
be
able
to
set
two
criterion
for
the
boundaries
in
order
to
align
that
data.
Just
as
a
wrap
up,
I'll
do
some
shameless
promotion.
I've
documented
this
case
study
in
a
blog
post.
If
you'd
like
to
review
it,
you
can
take
a
look
here.
I
even
include
the
data
set
in
here
as
well
so
you
can
recreate
this.
There
are
some
nice
references
as
well
to
some
other
blog
posts
that
others
have
written
on
this
topic
of
working
with
timestamp
data.
All
right,
thank
you
very
much.
