Hi.
I'm
here
to
talk
today
about
the
crucial
Step
0
of
Design
of
Experiments.
Really
the
idea
of
this
is
to
take
full
advantage
of
the
wealth
of
different
tools
that
are
under
the
DOE
platform
in
JMP.
I'll
walk
through
what
we
should
be
thinking
about
in
those
early
stages
of
an
experiment.
If
you
look
at
the
DOE
platform
listing,
what
you'll
see
is
that
there's
a
lot
of
different
choices.
Within
each
choice,
there's
many
more
choices.
Within
some
of
those,
there's
nested
possibilities.
If
you're
an
expert
in
design
of
experiments,
this
wealth
of
possibilities
really
feels
like
such
a
wonderful
set
of
tools.
I
love
all
of
the
options
that
are
available
in
JMP
that
allow
me
to
create
the
design
that
I
really
want
for
a
particular
experiment.
But
if
you're
just
getting
started,
then
I
think
this
set
of
possibilities
can
feel
a
little
bit
intimidating
and
sometimes
a
bit
overwhelming.
It
may
be
a
little
bit
like
going
to
a
new
kind
of
restaurant
that
you've
never
been
to
before.
Someone
who's
a
seasoned
visitor
to
those
kinds
of
restaurants,
loves
all
the
possibilities
and
the
wealth
of
options
on
a
big
menu.
But
if
you're
there
for
the
first
time,
it
would
be
nice
if
someone
guided
you
to
the
right
set
choices
so
that
you
could
make
a
good
decision
for
that
first
visit
and
have
it
be
successful.
Here's
what
I'm
planning
on
talking
about
today.
First,
I
think
the
key
to
a
good
experimental
outcome
is
to
really
have
a
clear
sense
of
what
the
goal
of
the
experiment
is.
I'll
talk
through
some
different
possibilities
of
common
goals
for
experiments
that
really
help
us
hone
in
on
what
we're
trying
to
accomplish
and
what
will
indicate
a
success
for
that
experiment.
Then
I'll
do
a
quick
walk-
through
of
some
of
the
more
common
choices
of
design
of
experiment
choices
in
JMP,
and
then
I'll
return
to
how
do
we
interact
with
those
dialog
boxes
that
we
get
when
we've
chosen
a
design
for
what
factors
to
choose,
the
responses,
and
the
relationship
between
the
inputs
and
the
outputs.
That's
where
we're
headed
through
all
of
this,
and
I
will
say
that
the
first
and
the
third
steps
really
need
a
tremendous
amount
of
subject
matter
expertise.
If
you're
going
to
be
successful
designing
an
experiment,
you
really
need
to
know
as
much
as
possible
about
the
framework
under
which
you're
doing
that
design.
We
want
to in
fact
incorporate
subject
matter
expertise
wherever
possible
to
make
sure
that
we're
in
fact
setting
up
the
experiment
to
the
best
of
our
ability.
What
are
we
trying
to
do?
I've
listed
here
six
common
experimental
objectives.
I
think
that
sort
of
gives
you,
a
checklist
if
you
like
of
different
options
of
things
that
you
might
be
thinking
of
accomplishing
with
your
experiment.
We
might
start
with
a
pilot
study,
so
we're
just
interested
in
making
sure
that
we're
going
to
get
data
of
sufficient
quality
for
the
experiments
and
answering
the
questions
that
we
want
to
have.
We
might
be
interested
in
exploration
or
screening.
We
have
a
long
list
of
factors,
and
we
want to
figure
out
which
one
seem
to
make
a
difference
for
our
responses
of
interest,
and
which
ones
don't
seem
particularly
important.
We
also
might
want
to
do
some
modeling.
Actually
formalizing
that
relationship
that
we're
seeing
between
inputs
and
responses,
and
capturing
it
in
a
functional
form.
Sometimes
we
don't
get
the
level
of
precision
that
we
need,
and
so
we
need
to
do
model
refinement,
and
so
that
might
be
a
second
experiment.
Then
once
we
have
a
model,
we
want
to
use
that
to
actually
optimize.
How
do
we
get
our
system
to
perform
to
the
best
of
its
capability
for
our
needs.
Then
lastly,
there's
a
confirmation
experiment
where
we
make
that
transition
from
the
controlled
design
of
experiments
environment
that
we're
often
doing
our
preliminary
data
collection
in
to
production,
and
making
sure
we
can
translate
what
we've
seen
in
that first
experiment
into
a
production
setting.
You
can
see
from
this
progression
that
I've
outlined
here,
that
we
may
actually
have
a
series
of
small
experiments
that
we
want to
connect.
We
may
start
off
with
a
pilot
study
to
get
the
data
quality
right,
then
we'll
figure
out
which
factors
are
important,
then
we'll
want to
model
those,
then
we'll
want to
use
that
model
to
optimize,
and
then
lastly
translate
those
results
into
the
final
implementation
in
production.
We
can
think
of
this
sequentially
or
for
an
individual
experiment
just
tackling
one
of
these
objectives.
Now
we
have
some
framework
for
what
the
goals
of
the
experiment
are
and
how
to
think
about
that,
we'll
now
transition
to
looking
at
what
some
of
the
common
choices
are
in
JMP
and
how
they
connect
with
different
goals.
I'll
open
up
the
DOE
tab
in JMP,
and
you
can
see
that
we've
got
the
list
of
possibilities
here
where
we've
got
the
nested
options
tucked
underneath
some
of
the
main
menu
items
that
we
have
here.
The
talk
is
only
half
hour,
and
so
I
won't
be
able
to
cover
all
of
the
tabs.
I've
given
a
brief
description
of
some
of
the
tabs
that
I
won't
have
time
to
talk
about.
Design
Diagnostics
is
all
about
having
a
design
or
maybe
several
designs
and
comparing
and
understanding
the
performance.
Sample
Size
Explorer
is
all
about
how
big
should
the
experiment
be
and
some
tools
to
evaluate
that.
Consumer
Studies
and
Reliability
Designs
are
really
kind
of
specialized
ones.
I'm
setting
those
aside
for
you
to
do
a
little
research
on
your
own
about
that.
In
Consumer
Studies,
we're
usually
asking
questions
of
consumers,
about
what
their
priorities
are,
what
features
they
like.
That
tends
to
be
a
comparison
between
two
options
and
how
they
value
those
choices.
Reliability
is
all
about
how
long
our
product
will
last.
That's
a
little
bit
different
than
things
that
I'll
talk
about
in
the
rest
of
the
talk.
I'll
start
off
with
some
of
the C
lassical
Designs
or
the
general
designs
that
we
have
that
have
been
developed.
Then
I'll
finish
with
some
of
the
JMP
specific
tools
that
are
much
more
flexible
and
adaptable
to
a
broader
range
of
situations.
I'll
start
with
that
bottom
portion
of
the
tab.
Here
we
are
in
JMP
in
the
DOE
tab,
and
I'm
going to
start
with
Classical.
You'll
see
that
I'm
tackling
this
in
a
little
bit
different
order
than
the
list
is
presented
by
JMP.
I
think
those
ones
are
presented
by
JMP
in
their
order
of
popularity,
and
I'm
choosing
to
tackle
them
more
from
principles
about
how
they
were
developed.
In
Classical
Designs,
a
Full
Factorial
design
is
looking
at
all
combinations
of
all
factors
at
all
levels.
That
works
nicely
if
we
have
a
small-ish
number
of
factors,
but
it
can
in
fact
get
a
little
bit
out
of
control
if
we
have
a
large
number
of
factors,
but
it's
exploring
the
entire
set
of
possibilities
very
extensively.
The
next
one
that
I'll
talk
about
is
a
Two-Level Screening
design,
and
essentially,
what
that's
doing
is
it's
choosing
a
subset
of
the
two
factorial
possibilities,
and
it's
a
strategic
subset
that
allows
us
to
explore
the
space,
but
keep
the
design
size
more
manageable.
You'll
notice
that
those
first
two
possibilities
I've
shown
at
two
levels,
and
that's
typical
for
screening
designs.
Usually,
we
just
want to
get
a
simple
picture
of
what's
happening
between
the
input
and
the
responses.
When
we
want to
start
modeling,
then
a
Response
Surface
Design
typically
allows
for
exploring
curvature.
When
we're
modeling,
three levels
or
sometimes
more
than
three
levels
can
be
a
good
way
to
understand
curvature
and
also
understand
interactions
between
the
factors
and
how
they
impact
the
response.
Alright. T
hat's
three
of
the
items
under
the
Classic
tab.
The
other
ones
are
Mixture
Design.
Typically
in
all
the
other
possibilities,
what
we
have
is
that
we
can
vary
the
individual
factors
separately
from
each
other.
But
in
a
Mixture
Design
where
we're
talking
about
the
composition
or
the
proportion
of
the
ingredients,
they're
interdependent.
If
I
increase
the
amount
of
one
ingredient,
it
probably
reduces
the
proportion
of
the
other
ingredients
that
are
in
that
overall
mixture.
A bit
of
a
specialized
one
when
we're
looking
at
putting
together
ingredients
into
an
overall
mixture.
Taguchi Arrays,
I've
listed
here
as
a
kind
of
optimization,
and
the
optimization
that
they're
interested
in
is
making
our
process
robust.
Typically
when
we're
in
a
production
environment,
we
might
have
noise
factors.
These
are
in
fact,
factors
that
we
can
control
in
our
experiment
but
when
we
get
to
production,
we're
not
able
to
control
them.
Then
we
have
a
set
of
factors
that
we
can
control
both
in
the
experiment
and
in
production.
The
goal
of
Taguchi Arrays
is
to
look
for
a
combination
of
the
controllable
factors
that
gets
us
nice
stable
predictable
performance
across
the
range
of
the
noise
factors.
You
can
see
C1
here
has
a
pretty
horizontal
line
which
means
it
doesn't
matter
which
level
we
are
at
for
the
noise
factor,
we'll
get
a
pretty
consistent
response.
Those
are
the
classical
options.
The
next
of
the
items
on
this
JMP
design
tab
that
I'll
talk
about
are
Definitive
Screening
Designs.
These
are
specialized
designs
that
were
developed
at
JMP,
and
they
are
a
blend
of
an
exploration
or
screening
design,
so
a
focus
on
a
lot
of
two-
level
factor
levels,
and
modeling.
You
can
see
with
the
blue
dots,
we
have
some
third
levels,
so
a
middle
value
for
the
factors
that
allows
us
to
get
some
curvature
estimated
as
well.
It's
a
nice
compact
design
that's
primarily
about
exploration
and
screening,
but
it
does
give
us
the
option
for
an
all
in
one
chance
to
do
some
modeling
as
well.
That's
very
popular
in
a
lot
of
different
design
scenarios.
The
next
tab
is
Special
Purpose,
and
you
can
see
there's
quite
a
long
list
of
possibilities
there,
and
I'll
hit
some
of
the
more
popular
ones
that
I
think
show
up
in
a
lot
of
specialized
situations.
A Covering
Array
is
often
used
when
we're
trying
to
do
testing
of
software.
A
lot
of
times
what
causes
problems
in
software
is
when
we
have
the
combinations
of
factors.
This
is
a
pretty
small
design
that's
typical
for
Covering
Arrays,
so
13
runs,
and
we're
trying
to
understand
things
about
10
different
factors.
What's
nice
about
these
Covering
Arrays
is
that
it
gives
us
a
way
to
see
all
possibilities
of,
in
this
case,
three
different
factors.
If
I
take
two
levels
of
each
factor,
a
zero
and
a
one,
there's
eight
different
combinations
for
how
I
can
combine
those
three
factors.
All
zeros,
all
ones,
and
then
a
mixture
of
zeros
and
ones.
I've
highlighted
those
with eight
different
underlined,
what's
really
nice
about
these
Covering
Arrays
is
whichever
three
factors
I
choose,
I
will
be
able
to
find
all
eight
of
those
combinations.
There's
10
choose
three
different
combinations
of
those
3
factors
that
I
might
be
interested
in,
and
all
of
them
have
all
of
those
possibilities
represented.
That's
a
very
small
design
that
allows
us
not
so
much
estimation,
but
to
check
possibilities
for
problems
that
we
might
encounter
particularly
in
software.
Next,
a
very
important
category
of
Space
Filling
Designs.
Compared
to
the
other options
that
I've
talked
about,
which
are
model-
based,
this
one
just
says,
I
maybe
don't
know
what
to
expect
in
my
input
space.
Let
me
give
even
coverage
throughout
the
space
that
I've
declared
and
just
see
what
happens.
You
can
see
that
I
have
many
more
levels
of
each
of
the
factors.
There's
a
lot
of
specialized
choices
in
here,
but
they
all
have
this
same
feel
of
nice,
even
coverage
throughout
the
inputs
face.
I
think
these
are
often
used
in
computer
experiments
or
in
physical
experiments
where
we're
just
not
sure
what
the
response
will
look
like.
I'll
talk
a
little
bit
more
about
that
when
we
get
to
the
decision
making
portion
in
Step
3
of
the
talk.
Next
is
MSA
Design
or
a
Measurement S
ystem
Analysis,
and
this
typically
is
associated
with
the
Pilot
Study.
Before
I
dive
in
and
really
start
to
model
things
or
do
some
screening,
it's
helpful
to
understand
some
basics
about
the
process
and
the
quality
of
the
data
that
I'm
getting.
Here,
I
can
sort
of
divide
the
variability
that
I'm
seeing
in
the
responses
and
attribute
it
to
the
operator,
the
measurement
device,
or
the
gage,
and
the
parts
themselves.
Sort
of
understand
the
breakdown
of
what's
contributing
to
what
I'm
seeing.
That's
very
helpful
before
I
launch
into
a
more
detailed
study.
Finally,
G
roup
Orthogonal
Super
saturated
Designs
are
in
fact,
really
compact
designs.
In
this
example,
where
you
have
six
runs,
and
we're
trying
to
understand
what's
happening
with
seven
different
factors.
That
may
seem
a
little
bit
magical,
but
it's
a
very
aggressive
screening
tool
that
allows
us
to
understand
what's
happening
with
a
lot
of
factors
in
a
very
small
experiment.
It's
important
with
these
designs
to
not
have
a
lot
of
factors.
If
all
seven
factors
are
doing
something,
and
I
only
have
six
runs,
I'll
end
up
quite
confused
at
the
end.
But
if
I
think
two
or
three
of
them
may
be
active,
this
may
be
a
very
efficient
way
to
explore
what's
going
on
without
spending
too
many
resources.
Those
are
the
start
here
ones
that
I've
talked
through
a
little
bit.
Now
I'm
going to
finish
with
these
wonderful
tools
in
JMP
that
are
more
general
and
more
flexible
for
different
scenarios.
Custom
Design,
I
think
is
just
an
amazing
tool
for
its
flexibility.
What's
really
nice
in
Custom
Design
is
that
I
have
this
wealth
of
different
possibilities
for
the
kinds
of
factors
that
I
can
include.
Continuous
factors,
maybe
I'll
add
in,
Discrete
Numeric
ones,
and
then
also
Categorical
Factors.
I
have
a
lot
of
different
choices
so
I
can
put
together
the
pieces,
and
if
I'm
not
sure
what
the
design
should
look
like
in
that
bottom
portion
of
the
list,
this
gives
JMP
some
control
to
help
guide
me
to
a
good choice.
On
the
next
page,
I
have
the
option
about
whether
I'm
just
interested
in
Main E
ffects,
whether
I
want to
add
some
two
factor
interactions,
and
whether
I
want
to
build
a
Response S
urface
Model,
so
more
the
modeling
goal
of
the
experiment.
This
is
sort
of
an
easy
way
to
build
a
design,
and
I
have
flexibility
here
to
specify
whatever
design
size
I feel
would
be
helpful
and
is
within
my
budget
to
make
a
design
and
the
expertise
of
the
JMP
design
team
are
going to
guide
me
to
a
sensible
choice.
This
is
a
great
way
if
you're
not
sure
about
how
to
proceed,
but
you're
still
making
some
key
decisions
about
what
the
goal
of
the
experiment
should
look
like.
Next,
the
Augment
tab.
If
you
think
back
to
what
I've
talked
about
for
the
Experimental
Objectives,
you
see
that
there's
this
connection
between
the
stages.
Maybe
I've
done
some
exploring
or
screening,
and
then
I'd
like
to
transition
to
modeling.
Well,
this
allows
me
to
take
an
experiment
that
I've
already
run
and
collected
data
for,
and
then
connect
it
to
the
Augment D
esign,
assign
the
roles
of
what's
a
response
and
what's
the
factor,
and
then
add
in
some
additional
runs.
There's
some
specialized
ones
here,
but
if
I
choose
the
Augment
portion,
that
allows
me
to
specify
a
new
set
of
factors,
perhaps
a
subset
of
what
I
have
or
an
additional
factor
and
then
also
what
model
I
would
now
like
to
design
for.
This
is
a
flexible
tool
for
connecting
several
sets
of
data
together.
Lastly,
Easy
DOE
is
a
great
way
to
get
started
for
your
very
first
experiment.
It
allows
you
to
build
sequentially
and
it
guides
you
through
the
seven
different
steps
of
the
entire
experiment.
It'll
allow
us
to
design
and
define,
and
so
that's
figuring
out
what
the
factors
are,
what
the
levels
are,
their
general
nature,
then
we
can
select
what
kind
of
model
makes
the
most
sense
for
what
we're
trying
to
accomplish,
then
progress
all
the
way
to
actually
running
the
experiment,
entering
the
data,
doing
the
analysis
and
then
generating
results.
This
is
a
wonderful
progression
that
walks
you
all
the
way
through
what
am
I
trying
to
do?
To
having
some
final
results
to
be
able
to
look
at.
What
I
will
say
is
that
this
is
designed
for
a
model-
based
approach.
What
you'll
see
is
that
all
of
these
look
like
they're
going
to
choose
a
polynomial
form
of
the
model.
That
needs
to
make
sense
as
a
starting
point.
But
if
that
does
make
sense
and
it
does
in
a
lot
of
situations,
then
this
is
a
wonderful
option.
Just
to
finish
things
up
here,
what
are
some
of
the
other
key
questions
now
that
I
have
a
goal
I
know
a
particular
choice
that
I
want
to
use
in
JMP,
what
are
some
of
the
other
key
questions
before
I
actually
generate
that
design?
A whole
category
is
about
the
factors.
We
need
to
use
our
subject
matter
expertise
to
figure
out
which
factors
we
should
be
looking
at.
If
we
have
too
long
of
a
laundry
list
of
factors,
then
the
experiment
necessarily
needs
to
be
quite
large
in
order
to
understand
all
of
them.
That's
going to
have
an
impact
on
how
expensive
our
experiment
will
be.
If
we
have
too
few
factors,
then
we
run
the
possibility
of
missing
something
important.
What
type
are
they
going
to
be?
We
need
to
think
about
getting
the
right
subset.
As
I
showed
you
in
C
ustom
Design,
we
have
quite
a
wide
variety
of
different
types
of
roles
for
the
different
factors
that
we're
looking
at.
That's
another
set
of
choices.
How
much
can
we
manipulate
the
factors?
Are
they
naturally
categorical,
or
are
they
continuous?
Then
we
need
to
think
about
the
ranges
or
the
values
for
each
of
those.
Let's
go
to
DOE
and
Custom
Design.
Then I'll
just
start
off
and
I'll
have
three
different
continuous
factors.
What
you
can
see
is
I
can
give
a
name
to
each
of
the
factors,
but
I
also
get
to
declare
the
range
that
I
want
to
experiment
in
for
each
of
those
factors.
A
s
you
can
imagine,
this
has
a
critical
role
in
the
space
that
I'm
actually
going
to
explore.
I
need
to
hone
in
on
what's
possible
and
what
I'm
interested
in
to
get
those
ranges
right.
If
I
make
the
range
too
big,
then
I
may
actually
have
a
lot
going
on
across
the
range
of
the
input
and
I
may
not
be
able
to
fully
capture
what's
going
on.
If
I
make
the
range
too
small,
then
I
may
miss
the
target
location
and
I
may
get
a
distorted
view
of
the
importance
of
that
factor.
Here,
this
input
actually
has
a
lot
going
on
for
that
response,
but
if
I
sample
in
a
very
narrow
range,
it
looks
like
it's
not
doing
anything.
Lastly,
if
I'm
in
the
wrong
location,
I
may
miss
some
features
and
not
be
able
to
optimize
the
process
for
what
I'm
doing.
Again,
the
choice
of
which
factors
and
the
ranges,
relies
a
lot
on
having
some
fundamental
understanding
about
what
we're
trying
to
do
and
where
we
need
to
explore.
The
next
piece
to
talk
about
is
the
relationship
between
inputs
and
responses.
I
will
say
that
one
of
the
common
mistakes
that
I
often
see
is
that
we
run
an
experiment,
and
then
after
the
fact,
people
realize,
oh,
we
should
have
collected
this.
In
textbooks,
a
lot
of
times,
it
looks
like
there's
a
single
response
that
we're
interested
in
and
we
run
the
experiment
to
just
collect
for
that
response.
In
practice,
I
think
most
experiments
have
multiple
responses
and
so
this
is
a
key
decision,
is
to
make
sure
before
we
collect
that
first
data
point
that
we
actually
include
the
right
set
of
responses
so
that
we
can
answer
all
of
the
questions
from
that
one
experiment.
Then
we
need
to
think
about
what
we
know
about
the
relationship.
Is
it
likely
to
be
smooth?
Is
it
going to
be
continuous
in
the
range
that
we've
selected?
How
complicated
are
we
expecting
it
to
be?
A
ll
of
these
have
an
impact
on
the
design
that
we're
going
to
have.
A
couple
of
common
mistakes
about
the
relationship
is,
one,
being
a
little
too
confident,
so
we
assume
that
we
know
too
much
about
what's
going to
happen,
and
then
don't
build
in
some
protection
against
surprises.
Then
also
if
we
have
multiple
responses,
not
designing
for
the
most
complicated
relationship.
If
one
of
them
were
interested
in
Main E
ffects
and
the
other
one
we
think
there
might
be
curvature,
we
need
to
build
the
design
so
that
it
can
estimate
the
curvature
because
that's
the
more
complicated
relationship.
A
first
key
decision
that
I
think
is
a
little
bit
hidden
in
JMP
is
that
we
have
to
decide
between
model-
based,
and
that's
usually
sensible
if
we're
confident
that
our
responses
will
be
smooth
and
continuous,
and
that
we're
not
investigating
too
big
of
a
region,
or
should
we
do
space
filling?
Space
filling
can
be
a
good
safety
net
if
we're
not
sure
what
to
expect,
if
we're
exploring
a
large
region,
or
if
we
want to
protect
against
surprises.
I'm
pointing
here
on
the
last
slide.
I
have
more
details
about
that
to
a
paper
that
I
wrote
with
a
colleague,
Dr. Lu Lu
at
the
University
of
South
Florida,
where
we
talk
about
the
implications
of
that
first
fork
in
the
road,
how
do
we
choose
between
model-
based
and
space
filling,
and
what
are
the
repercussions?
Then
lastly,
we
need
to
think
a
little
bit
about
constraints.
Our
input
region,
if
we've
declared
some
ranges
for
the
different
inputs,
that
naturally
seems
like
it's
a
square
or
a
rectangle.
But
in
that
region,
there
may
be
some
portions
where
we
can't
get
a
response
or
we
just
don't
care
about
what
the
responses
look
like.
Imagine
if
I
am
doing
an
experiment
about
baking
and
I'm
varying
the
time
that
the
cookies
are
in
the
oven
and
the
temperature
of
the
oven.
I
might
know
that
the
coolest
temperature
for
the
shortest
amount
of
time
won't
produce
a
baked
cookie.
It'll
still
be
raw,
or
it
might
be
the
hottest
temperature
for
the
longest
time
will
overcook
the
cookies.
I
want
to
sort
of
chop
off
regions
of
that
space
that
aren't
of
interest
or
won't
give
me
a
reasonable
thing.
In
JMP,
there's
easy
ways
to
specify
constraints
to
make
the
shape
of
that
region
match
what
you
want.
The
last
thing
is
all
about
budget,
how
big
should
my
experiment
be,
and
that's
a
function
of
the
time
that
I
have
available
and
the
cost
of
the
experiment.
In
JMP,
we
jump
to
here.
Maybe
I
specify
a
response
surface
model,
you'll
see
that
there's
a
new
feature
called
Design
Explorer,
which
when
I
activate
that,
it
allows
me
with
a
single
click
of
a
button
to
generate
multiple
designs.
I
can
optimize
for
good
estimation,
so
D
or
A-O
ptimality,
or
good
prediction
of
the
responses
with
I-O
ptimality.
I
can
vary
the
size
of
the
experiment
and
center
points
and
replicates.
If
I
click
Generate
All
Designs,
it
will
generate
a
dozen
or
so
designs,
which
then
I
can
compare
and
consider
and
figure
out
which
one
makes
the
most
sense.
I
think
understanding
the
budget,
thinking
of
that
as
a
constraint,
is
an
important
consideration
that
we
need
to
have.
To
wrap
things
up,
just
a
few
helpful
resources.
The
first
one
is
a
JMP
web
page
that
talks
in
a
little
more
detail
about
the
different
kinds
of
designs.
It
fills
in
a
lot
of
the
details
that
I
wasn't
able
to
talk
about
today
about
those
individual
choices
on
the
DOE
tab.
The
Model-B
ased
versus
Space-F
illing
,
that's
the
paper
I
referenced
earlier,
where
we
need
to
understand
the
implications
of
choosing
a
model-
based
design
or
doing
space-
filling,
which
is
a
little
more
general
and
a
little
more protective
if
we
are
expecting
some
surprises.
Then
the
last
two
things
are,
two White
Papers
that
I
wrote,
the
first
one
talks
about
how
you
can
use
Design
Explorer
to
consider
different
design
sizes
and
different
optimality
criteria
and
then
choose
between
the
different
choices
by
looking
at
the
compare
design
option
in
JMP.
Then
lastly,
everything
I've
talked
about
here
is
dependent
on
subject
matter
expertise.
The
why
and
how
of
asking
good
questions,
give
some
strategies
for
how
to
interact
with
our
subject
matter
experts
to
be
able
to
target
those
conversations
and
make
them
as
productive
as
possible.
I
hope
this
has
been
helpful,
and will
help
you
have
a
successful
first
experiment
using
JMP
software.
Thanks.
