Hello,
I'm
Chris
Gotwalt with
JMP,
and
my
co-
presenter,
Giuseppe
De
Martino
from
Topsoe
,
and
I
are
giving
a
presentation
that
tells
the
story
of
a
data
analysis
project
that
showcases
the
new
wavelet
analysis
in
the
Functional
Data
Explorer,
one
of
the
most
exciting
new
capabilities
in
JMP
17.
The
case
study
begins
with
a
product
formulation
problem
where
Tops
oe
wanted
to
design
a
catalyst
that
optimizes
two
responses,
but
the
responses
are
in
conflict
with
one
another
in
that
improving
one
response
often
comes
at
the
expense
of
the
other.
A
candidate
for
the
optimal
tradeoff
was
found
with
models
fit
by
the
generalized
regression
platform,
and
the
optimal
factor
settings
were
found
using
the
desirability
functions
in
the
profiler.
This
was
a
fairly
standard
DoE
analysis.
But
in
addition
to
the
measured
responses,
NIR
spectra
were
taken
from
some,
but
not
all
of
the
sample
batches.
This
is
information
that
can
be
used
to
give
a
clue
to
the
R&D
team
about
what
the
chemical
structure
of
the
ideal
formulation
should
look
like.
In
addition
to
the
GenReg
model
of
the
responses,
we
also
used
wavelet
models
as
the
basis
function
of
a
functional
DoE
analysis
of
the
spectra
using
the
DoE
factors
as
inputs.
We
were
then
able
to
get
a
synthetic
spectra
of
the
optimal
formulation
by
plugging
in
the
optimal
factor
settings
found
in
the
initial
analysis
of
the
two
critical
responses.
Before
going
into
the
presentation,
I
want
to
point
out
that
at
the
beginning
of
the
project,
Giuseppe
was
very
new
to
JMP
and
didn't
have
a
background
in
this
type
of
statistical
analysis.
Giuseppe
learned
all
he
needed
to
do
the
analysis
on
his
own
after
a
couple
of
web
meetings
with
me.
This
obviously
shows
he's
a
clever
guy,
but
also
that
JMP
makes
learning
how
to
do
some
very
sophisticated
data
analysis
projects
quick
and
easy.
Now
I'm
going
to
hand
the
show
over
to
Giuseppe.
Thank
you,
Chris.
Here
is
some
background
about
our
project.
It's
a
catalyst
development
project.
Therefore,
we
are
developing
many
different
recipes.
Each
recipe
has
a
unique
set
of
production
parameters,
and
once
the
sample
is
prepared,
we
characterize
it
in
our
analysis
lab
in
Topsoe.
And
finally,
we
do
a
performance
test.
During
the
performance
test,
we
look
for
two
values
that
here
we
call
Response
1
and
Response
2.
In
this
specific
case,
we
are
trying
to
minimize
response
1 while
maximizing
response
2.
That
will
lead
us
to
this
ideal
space
in
the
top
left
corner
of
the
graph.
But
as
you
can
see,
the
55
samples
that
we've
tested
get
stuck
in
the
middle
of
the
graph.
That
is
because
Response 1
and
Response 2
are
inter-
correlated,
meaning
that
improving
one
comes
with
the
expenses
of
the
other.
Therefore,
we
move
to
JMP,
and
we
try
to
look
at
our
response
data
and
our
characterization
data,
try
to
see
if
we
can
move
away
from
this
line
in
the
middle
of
the
graph.
We
identified
two
targets
areas
that
we
want
to
reach,
and
together
with
Chris,
we
thought
about
using
JMP
to
create
a
model
that
would
connect
the
production
parameters
to
the
response
values. T
hen
we
further
looked
into
our
infrared
spectroscopy
data
to
try
to
validate
our
model
and
to
get
some
extra
information
about
the
target
samples.
Here
is
an
overview
of
the
data
set.
We
have
produced
112
samples.
Each
sample
has
a
unique
set
of
production
parameters.
We
have
analyzed
all
the
samples
using
infrared
spectroscopy.
We
have
one
spectrum
for
each
sample.
Then
we
have
used
many
other
characterization
techniques
that
we
have
in-
house
that
accounts
for
21
more
columns
of
data.
Finally,
we
have
tested
half
of
the
samples
and
that
accounts
for
the
last
two
columns
that
we
called
response.
At
the
beginning
of
our
project,
we
actually
wanted
to
include
the
infrared
spectroscopic
data
in
our
larger
data
set,
and
that's
why
we
wanted
to
use
JMP,
because
now
we
have
this
new
wavelet
model
possibility.
And
that
would
enable
us
to
include
the
principal
components
coming
from
the
wavelet
model
in
our
data
set.
And
we
could
use
that
to
create
models
in
JMP.
But
before
we
start
our
analysis,
we
need
to
have
a
look
at
the
Pro
data
to
find
outliers.
We
do
that
by
clicking
Analyze
and
the
Multivariate
Methods,
Multivariate.
H
ere
we
can
select
our
production
parameters
and
characterization
data
as
Y
columns,
and
we
get
this
scatterplot
matrix.
This
is
an
example
of
just
looking
at
the
production
parameters
at
what
we
would
identify
as
an
outlier.
We
can
see
that
there
is
a
set
of
points
in
production
parameters
too
that
is
far
away
for
all
the
other
points.
Furthermore,
we
have
background
knowledge
about
these
samples
that
we
know
wouldn't
be
optimal
for
our
catalyst
development,
so
we
decided
to
right
click
and
say
"Row,
hide
and
exclude."
We
did
this
also
with
other
points
looking
at
the
scatterplot
metrics
of
all
the
characterization
data.
Now
that
we
have
cleaned
up
the
data,
we
can
fit
a
model.
We
click
Analyze
and
Fit
Model,
and
we
select
the
production
parameters
as
variables
in
our
model.
We
click
Macros
and
response
surface
to
create
a
second
polynomial
combination
of
these
variables.
Then
we
select
our
responses
as
Y
values.
Then
we
decided
to
use
generalized
regression.
H
ere
Chris
can
add
some
more
info
about
why
we
decided
to
use
this
specific
type
of
model.
We
used
a
quadratic
response
surface
model
because
the
design
had
three
or
more
levels
for
each
factor,
so
I
knew
that
we
would
be
able
to
fit
curvature
terms
if
necessary
and
also
be
able
to
fit
quite
a
variety
of
different
interaction
terms.
We
use
the
generalized
regression
platform
because
it
does
model
selection
with
non
Gaussian
distributions
like
the
log
normal.
In
my
opinion,
there
aren't
many
reasons
not
to
use
the
generalized
regression
platform
if
you
have
JMP
Pro
because
it
is
so
easy
to
use
while
in
many
ways
being
so
much
more
powerful
for
DoE
analysis
than
the
other
options
in
JMP
and
JMP
Pro.
After
that,
we
can
select
our
distribution.
We
know
that
the
responses
are
going
to
be
strictly
positive,
so
we
select
the
log
normal
distribution
and
then
we
click
Run,
and
we
say
no.
In
this
slide,
we
can
see
that
we
have
now
created
a
model,
but
we
have
also
the
possibility
of
creating
other
type
of
models
using
different
estimation
methods.
We
decided
to
use
best
subset.
Here,
Chris
can
add
some
more
words
about
it.
Well,
so
here
we
use
best
subset
selection
because
the
full
model
isn't
terribly
large,
so
why
not
try
every
possible
subset
of
that
full
model
and
find
the
one
that
provides
the
absolute
best
tradeoff
between
accuracy
and
model
simplicity?
On
the
other
hand,
had
there
been
eight
or
more
factors,
I
would
have
used
a
faster
algorithm
like
forward
selection
or
pruned
forward
selection
because
with
larger
base
models,
it
would
take
a
very
long
time
to
fit
every
possible
submodel
to
the
data.
We're
going
to
be
using
the
AICc
model
selection
criteria
to
compare
GenReg
models.
The
AICc
allows
you
to
compare
models
with
different
effects
in
them
as
well
as
different
response
distributions.
With
the
AICc,
smaller
values
are
better
and
the
rule
of
thumb
I
use
is
that
if
a
model
has
an
AICc
value
that
is
within
4
of
the
smallest
AICc
value
seen,
then
those
two
models
are
practically
identical
in
quality
of
fit.
If
the
two
models
have
AICc
values
within
10
of
each
other,
then
they
are
statistically
similar
to
one
another.
The
main
point
here
being
that
if
we
have
two
models
and
their
AICc's
differ
by
more
than
10,
then
the
data
are
pretty
strongly
suggesting
that
the
one
with
the
smaller
AICc
is
the
better
model
to
be
working
with.
As
with
any
individual
statistic,
you
should
view
the
A
ICc
as
a
suggestion.
If
your
subject
matter
experience
strongly
suggests
one
model
over
the
other,
you
may
want
to
trust
your
instincts
and
ignore
the
recommendation
of
the
A
ICc.
Once
we
have
created
this
new
model,
we
can
see
that
the
non-
zero
parameters
have
now
dropped
from
16
to
nine.
If
we
want
to
compare,
if
the
model
has
improved,
we
can
look
at
the
A
ICc
values.
We
can
see
that
there
is
an
improvement
of
more
than
10,
which
is
an
important
difference.
Therefore,
we
decided
to
go
with
the
best
subset.
We
did
the
same
for
Response 2,
and
then
we
moved
on.
Now
we
can
click
on
the
red
arrow
and
say
profiler.
In
the
profiler,
we
can
play
around
with
the
production
parameters
and
see
how
the
model
is
expecting
Response 1
and
Response 2
to
change.
This
is
already
a
great
tool
for
the
scientist
to
understand
how
the
model
is
expecting
the
responses
to
vary,
but
we
can
do
more.
We
can
click
on
optimization
and
desirability
and
desirability
functions.
Since
from
slide
one,
we
know
that
we
have
two
targets
that
we
want
to
reach,
we
can
change
the
desirability
function
to
match
those
targets.
So
we
double
click
on
the
Desirability
function and
we
say
match
target
and
select
the
target
area
that
we
want
to
reach.
Finally,
we
can
click
again
on
optimization
and
desirability
and
say
maximize
desirability.
Here,
the
profiler
will
try
to
reach
the
optimal
points
for
the
production
parameters.
To
summarize,
we
can
say
that
now
we
have
the
first
model,
we
go
from
production
parameters
to
responses
and
we
have
set
two
targets
that
we
want
to
reach.
This
way
we
can
get
ideal
production
parameters
that
we
can
communicate
to
the
development
team
and
they
can
use
to
move
on
in
their
research.
In
the
second
part
of
the
presentation,
I'm
going
to
talk
about
how
we
use
the
IR
Spectra.
Here
we
have
a
file
for
each
spectrum
and
therefore
we
need
to
click
on
file
and
import
multiple
files.
Then
we
need
to
specify
that
we
want
the
file
name
to
be
columned
in
the
data
set
and
then
we
can
use
this
sample
name,
which
is
the
name
of
the
file
as
an
ID
to
connect
it
to
the
other
table
where
we
have
all
the
data.
We
click
on
the
column
and
say
link
reference.
Now
that
the
two
tables
are
connected,
we
can
click
Analyze
and
specialize
modeling
and functional
data
explorer.
In
the
Functional
Data
Explorer,
we
want
to
use
the
intensity
value
as
the
Y
output.
We
can
use
the
sample
name,
matrix
name
as
our
ID
function.
Then
this
is
very
important.
We
use
the
production
parameters
as
supplementary
data.
And
the
weight
number
is,
of
course,
the
X-
axis.
And
we
say,
"Okay,
here
we
can
see
that
the
data
is
already
clean."
We've
imported
all
the
Spectra
and
the
data
looks
clean
because
I've
done
the
preprocessing
outside
of
JMP.
I
used
Python
because
I'm
more
familiar
with
that
and
there
is
a
very
nice
module
that
is
able
to
remove
the
background
and
reduce
the
range
that
we
want
to
look
at.
JMP
was
good
to
work
with
as
an
extra
tool
after
this
reprocessing.
Then
we
decide
to
click
on
models
and
wavelets.
We
move
from
discrete
data
to
continuous
data.
Now
we
can
also
look
at
the
diagnostic
plots.
This
is
for
you,
Chris,
to
take talk
about.
It's
a
good
idea
to
look
at
actual
by
predicted
plots
as
you
proceed
through
a
functional
data
analysis.
These
have
the
actual
observed
values
in
the
data
on
the
Y
axis
and
the
predicted
values
on
the
X
axis.
We
want
the
predicted
values
to
be
as
close
to
the
actual
values
as
possible.
A
plot
like
this
one
that
is
tight
along
the
45
degree
line
indicates
that
we
have
a
good
model.
Now,
some
of
you
may
be
concerned
about
overfit
since
the
predictions
fit
the
data
so
well.
In
my
experience,
I
haven't
found
that
to
be
a
problem
in
the
basis
function
fitting
and
functional
principal
component
steps
of
functional
DoE
analysis.
I'd
also
like
to
point
out
that
in
JMP
17,
we've
added
a
lot
of
new
features
for
spectral
preprocessing
like
standard
normal
variant,
multiplicative
scatter
correction,
and
Savitzky-Golay filters
.
Those
of
you
that
don't
know
Python
have
access
to
these
capabilities
in
JMP
Pro
17.
After
that,
we
can
also
have
a
look
at
the
Functional
PCA
analysis.
Here,
we'll
spend
some
more
words
on
it.
After
the
wavelet
model
is
fit,
JMP
Pro
automatically
does
a
functional
principal
components
analysis
of
the
wavelet
model.
This
decomposes
the
spectra
into
an
overall
mean
spectra and
a
linear
combination
of
shape
components
and
coefficients
that
are
unique
to
each
sample
spectra.
When
we
do
the
functional
DoE
analysis,
GenReg
automatically
fits
models
to
these
coefficients
behind
the
scenes
and
combines
the
resulting
model
with
the
mean
function
and
the
shape
components
to
predict
the
shape
of
the
spectra
at
new
values
of
the
input
parameters.
If
we
look
at
the
principal
component
analysis,
we
can
see
that
the
wavelet
model
has
created
a
mean
function
of
all
the
spectra
that
we've
set
as
input,
and
then
it
has
created
different
shape
functions.
We
decided
to
stop
at
six.
What
this
shape
function
described
is
the
variation
of
the
data
that
we
are
analyzing.
As
you
can
see
from
the
left,
the
first
shape
function
is
accounting
for
72
%
of
the
variation,
while
the
second
is
accounting
for
22
%.
Together,
the
account
called
six
account
for
99.5
%
of
the
variation.
As
an
example,
we
can
look
at
principal
component
2,
and
we
can
see
that
around
3,737,
there
is
a
reduction.
There
is
a
minus.
That
means
that
increasing
the
principal
component
2 will
decrease
the
peak
at
3,737.
This
is
just
an
example
to
say
that
already
from
these
six
shape
functions,
we
can
get
a
lot
of
information.
If
we
have
subject
matter
knowledge,
about
the
infrared
spectroscopy
and
this
catalyst
system.
Already
here,
we
spent
quite
a
lot
of
time
looking
at
the
principal
components,
but
this
is
not
what
we
are
going
to
focus
on
in
the
next
slides.
What
we
want
to
look
at
instead
is
the
functional
DoE
analysis.
Here
we
have
as
well
a
profiler,
but
we
can
now
plug
the
production
parameters
that
we
got
from
the
first
model
that
we
developed.
Therefore,
knowing
the
target
production
parameters
that
we
want
to
use,
we
can
generate
a
fake
spectrum
or
a synthetic
spectrum,
we
can
call
it.
This
is
a
spectrum
of
a
sample
that
was
never
produced.
It
could
be
wrong,
but
it
can
give
some
ideas
to
the
scientists
about
what
you
would
expect
to
get
from
these
new
production
parameters.
To
sum
up,
we
can
say
that
now
we
move
from
production
parameters
to
infrared
spectrum.
We
have
a
second
model
that
uses
the
wavelet
model
to
generate
synthetic
spectrum.
I
imagine
this
to
be
like
when
you're
baking
a
cake,
now
you
have
the
recipe
but
you
got
also
a
snapshot
picture
of
the
final
cake.
It
doesn't
explain
you
how
to
do
it
but
it
adds
information
about
what
you
want
to
achieve.
Finally,
we
can
move
from
model
to
test.
That
means
that
we
can
give
the
R&D
team
a
new
recipe
and
also
the
synthetic
spectrum
of
that recipe.
Together
with
this
information,
they
can
try
to
develop
the
new
catalyst
and
see
if
the
model
is
validated
or
is
wrong.
Another
thing
the
group
can
do
is
look
back
at
the
previous
samples.
Now
we
have
half
of
the
samples
that
were
not
tested.
Those
are
the
black
dots
in
the
slide.
We
can
look
for
outliers.
Is
there
a
sample
that
could
perform
really
good
that
we
haven't
looked
at?
We
actually
have
one.
So
that's
another
test
we
can
do.
Looking
at
future
work,
I
added
this
slide
that
was
at
the
beginning
just
to
say
that
we
focused
on
the
production
parameters
and
the
IR
spectra,
but
we
haven't
really
looked
at
these
21
more
columns
of
characterization
data.
In
the
future,
we
could
spend
some
time
trying
to
identify
the
most
predictive
parameters
in
these
21
columns
and
create
maybe
a
new
model
from
this
characterization
data
to
the
response
and
use
this
model
as
a
screening
model
to
avoid
testing
samples
that
would
not
perform
as
good.
That's
the
end
of
the
presentation.
