Hello,
my
name
is
Andrea
Coombs,
and
I'm
joined
by
my
colleague,
Olivia
Lipp
incott.
Olivia
and
I
have
given
a
presentation
before
called
What
Model
When.
If
you
want
to
take
a
closer
look
at
that
presentation,
you
can
take
a
look
at
the
link
in
the
community
post.
But
today,
we
want
to
talk
about
something
a
little
bit
more.
Yeah,
today
we
want
to
think
about
modeling
type
and
how
modeling
type
impacts
the analysis
for
each
of
the
four
model
goals
that
we
talked
about
previously.
Right,
and
we're
actually
going
to
use
the
same
data.
This
is
data
that
we
pulled
from
Redfin
that
represents
the
housing
market
in
the
Cincinnati
area.
Here
we're
trying
to
look
at
the
price
of
homes
relative
to
their
square
footage,
the
number
of
beds,
the
number
of
baths,
and
so
on
and
so
forth.
Previously,
we've
answered
the
question,
what
model
when?
It
really
depends
on
what
model
you're
going
to
choose
based
on
your
goal
for
the
analysis.
For
segment,
we're
trying
to
examine
relationships
where
there's
no
intended
response;
explain,
we're
trying
to
explain
a
relationship
and
look
at
the
underlying
factors
and
how
those
affect
the
response;
predict,
we're
trying
to
predict
future
outcomes
or
the
response
in
new
situations;
and identify,
we're
trying
to
find
important
variables.
Right.
Now
let's
bring
the
modeling
type
into
the
picture.
Both
your
responses
and
your
factors
can
have
different
modeling
types.
In
JMP,
there
are
three
main
modeling
types:
continuous,
nominal,
and
ordinal.
Continuous
modeling
type
is
represented
by
this
blue
triangle
icon
here,
and
this
refers
to
numeric
data
only.
The
nominal
modeling
type
is
represented
by
this
red
icon,
and
this
is
numeric
or
character
data
where
values
belong
to
categories,
but
the
order
is
not
important.
For
the
ordinal
modeling
type,
it
is
represented
by
this
green
icon,
and
this
can
be
either
numeric
or
character
data
as
well.
But
in
this
case,
values
belong
to
ordered
categories.
When
you're
doing
an
analysis
in
JMP,
you
want
to
make
sure
you
set
up
the
correct
modeling
type,
because
JMP
will
do
the
correct
model
for
you,
will
do
the
correct
analysis
depending
on
modeling
type.
Andrea,
I
have
a
game
for
us
to
play.
It's
called
Name
That
Analysis.
Do
you
want
to
play?
Absolutely.
I
love
games.
Awesome.
Here's
your
first
question.
We
want
to
identify
which
features
of
a
home
are
most
important
to
determining
the
price.
For
example,
square
footage
and
number
of
bathrooms
can
explain
a
large
amount
of
the
variation
in
price,
but
other
features
are
less
important.
All
right,
Olivia.
I
think
you're
making
this
first
question
easy
for
me.
Is
the
answer
identify?
Let's
see.
Yeah,
you're
right.
I
did
make
that
one
a
little
bit
easy
to
get
us
going,
but
that
is
identify
to
find
important
variables
within
there.
There's
a
couple
of
different
places
in
JMP
where
we
can
use
tools
to
identify
if
that's
our
modeling
goal.
Under
the
Analyze
menu
under
Screening,
Predictive
Modeling
and
Fit
Model,
using
tools
like
Predictor
Screening,
Bootstrap
Forest,
Generalized
Regression
and
Stepwise
Selection.
For
modeling
type,
when
we're
looking
at
the
goal
of
identify,
it's
not
going
to
affect
things
much.
JMP
is
going
to
do
the
correct
analysis
as
long
as
your
modeling
types
are
set
appropriately.
We
took
a
look
at
this
and
we
took
both
the
response
and
the
factors
and
changed
them
from
continuous
to
nominal
and
looked
at
how
which
factors
came
up
as
most
important.
While
the
order
of
the
factors
varied,
the
dominant
factors
stayed
the
same.
All
right.
It
looks
like
if
our
goal
is
to
identify
important
factors,
really,
the
exact
modeling
type
we're
using
isn't
impacting
things
that
much,
it
looks
like,
Olivia.
Right.
Our
conclusions
on
which
variables
are
important
aren't
going
to
change
much
based
on
the
modeling
type.
All right.
Well,
that
is
good
to
know.
I
have
a
question
for
you.
Are
you
ready?
I'm
ready.
All
right,
here
is
your
question.
Let's
say
we
want
to
build
a
model
to
predict
house
prices.
This
model
will
be
based
on
many
important
predictor
variables
we
have
in
our
data.
For
example,
we
want
to
predict
the
price
of
a
house
that
we
want
to
put
on
the
market.
Which
goal
do
you
think
we're
working
with
here?
Okay,
so
it's
not
like
question
one
where
we're
trying
to
see
which
factors
are
most
important
to
predict
housing
prices.
We're
just
really
trying
to
get
that
final
housing
price
prediction.
I'm
going
to
go
with
predict.
All
right,
let's
see
if
you're
right.
Yes,
you
are
right.
The
goal
of
this
analysis
is
predict.
There's
lots
of
different
platforms
in
JMP
where
you
can
build
models
for
prediction.
Within
each
of
those
platforms
in
JMP
where
you
can
build
the
prediction
models,
JMP
will
do
the
correct
analysis
for
you,
depending
on
the
modeling
type
of
your
response.
Here
we
have
a
table
of
different
modeling
types
for
our
responses:
continuous,
nominal,
and
ordinal.
For
a
continuous
response,
this
is
the
typical
one
that
we
were
talking
about,
right?
We
want
to
predict
the
price
of
a
home
that
we're
going
to
put
on
the
market.
Now,
when
we're
building
this
type
of
model
with
a
continuous
response,
well,
we
want
to
know
how
powerful
that
model
is.
What's
the
predictive
power
of
that
model?
We
can
use
RSquared
and
the
Root
Average
Squared
Error
to
diagnose
that
model.
Now,
for
a
nominal
and
ordinal
model,
it's
a
little
bit
different.
For
a
model
with
a
nominal
response,
we
have
categories
as
the
response.
In
this
example,
we're
looking
at
whether
or
not
the
price
will
be
over
or
below
$1
million.
That's
what
we
want
to
predict.
For
the
ordinal
response,
here
we
have
an
ordered
category.
We
want
to
predict
whether
the
price
of
the
house
is
going
to
be
low,
medium,
and
high.
For
the
nominal
and
ordinal
examples,
again,
we
can
look
at
RSquared
and
Root
Average
Squared
Error
to
evaluate
those
models.
But
there's
other
things
that
we
can
use
to
evaluate
those
models,
like
the
misclassification
rate
and
the
area
under
the
ROC
curve.
Of
course,
our
favorite
tool
in
JMP
to
take
a
look
at
our
prediction
model
is
the
Prediction
Profiler.
Let's
take
a
look
at
the
difference
between
the
Prediction
Profiler
for
the
modeling
types
of
our
responses.
For
the
continuous
response,
we
can
see
that
on
the
Y-axis,
we
have
the
mean
prediction
plus
or
minus
the
confidence
interval
given
the
value
of
the
model
factors
here
on
each
of
the
X
-axes.
For
the
nominal
and
ordinal
logistic
models,
what
we
see
on
the
Y
-axis
is
the
probability
of
the
response
being
in
a
certain
category.
For
the
nominal
logistic
model,
we
have
the
probability
that
the
house
is
either
going
to
be
above
or
below
a
million
dollars.
For
this
ordinal
logistic
model,
we
can
see
the
probability
of
having
a
low,
medium,
or
high
price.
Okay,
so
it
sounds
like
the
goal
of
what
we
want
to
predict
is
also
important
when
we're
talking
about
that
prediction
goal,
whether
we
want
to
treat
price
as
continuous
and
get
the
predictions
of
the
exact
prices
out
of
there,
or
if
we
want
to
treat
it
as
a
category.
Right.
You
just
need
to
get
that
response
variable
set
up
and
your
data
set
the
correct
way,
and
then,
of
course,
assign
the
correct
modeling
type,
and
JMP
is
going
to
build
the
correct
model
for
you.
All
right,
Andrea.
Are you
ready
for
your
next
question?
I'm
ready.
Let's
go.
Okay.
We
want
to
quantify
the
effect
on
home
prices
from
additional
bedrooms.
For
example,
on
average,
every
additional
bedroom
adds
about $
97,000
to
the
total
home
cost.
Adding
a
bedroom
adds
$97,000?
Man,
Cincinnati
is
a
tough
housing
market.
That's
crazy.
All
right,
well,
so
let's
see.
What
are
we
trying
to
do
here?
We're
trying
to
quantify
the
effect
here.
I
think
what
we're
trying
to
do
is
explain
that
effect
that
bedrooms
has
on
the
price
of
a
house.
I'm
going
to
say
explain.
You're
correct.
Yeah,
we're
trying
to
describe
the
relationships.
In
explain,
we
use
the
parameter
estimates
taken
from
the
model
equation
to
quantify
those
relationships
between
the
factors
and
the
responses.
Typically,
we
use
in
JMP
under
the
Fit
Model
menu
location
tools
like
Standard Least
Squares,
Logistic
and
Ordinal
Regression,
and
Generalized
Regression.
Modeling
type
can
really
impact
how
our
factored
relationship
with
the
response
variable
is
interpreted.
We
took
a
look,
and
we
were
looking
at
how
does
the
number
of
beds
affect
the
housing
price?
We
changed
beds
from
continuous,
to
nominal,
to
ordinal,
and
see
what
that
relationship
was.
We
can
see
under
the
continuous,
that's
where
we've
got
that
every
additional
bedroom
adds
$97,000
about
to
the
total
home
price.
That
prediction
profiler
shows
a
linear
relationship
when
we
treat
beds
as
continuous.
But
when
we
treat
beds
as
nominal
or
ordinal,
there's
not
that
straight
linear
relationship
going
on.
We
see
a
spike
in
price
for
4-5
bedrooms
compared
to
going
from
2-3
bedrooms.
Right.
I
see
with
nominal
and
ordinal,
the
prediction
profiler
looks
almost
exactly
the
same,
so
it
must
be
the
same
model.
However,
I'm
seeing
with
the
parameter
estimates,
they
look
a
little
bit
different
between
nominal
and
ordinal.
What's
going
on
there?
Yeah,
so
the
nominal
and
ordinal
modeling
type,
and
when we
use
that
within a
regression,
is
treating...
They're
coded
differently
within
the
regression,
so
the
parameter
estimates
are
different.
For
nominal,
that
intercept,
we
think
of
that
as
the
mean
house
price
across
all
the
different
bedrooms,
and
each
of
those
parameter
estimates
are
how
much
that
number
of
beds
increases
or
decreases
that
mean
house
price.
But
for
ord
inal,
because
we're
looking
at
order
matters,
we
think
of
the
intercept
as
if
there
are
zero
bedrooms
and
each
of
those
parameter
estimates
is
the
effect
of
adding
an
additional
bedroom
onto
the
price.
All
right.
Modeling
type
is
really
going
to
affect
my
parameter
estimates.
I
really
need
to
think
about
exactly
what
do
I
want
to
explain
as
a
part
of
this
model
when
I'm
doing
this
analysis.
Yes.
All
right.
Are
you
ready
for
the
final
question,
Olivia?
Yeah,
bring
it
on.
All
right,
here's
the
question.
Let's
say
we
want
to
identify
groups
of
homes
that
are
similar
based
on
a
list
of
possible
characteristics.
In
other
words,
we
want
to
identify
market
segments
based
on
things
like
square
footage,
location,
number
of
bedrooms,
et
cetera.
Which
goal
do
you
think
this
is?
I
think
you're
trying
to
trick
me
with
that
identify,
and
I'm
not
going
to
fall
for
it.
Okay.
But
there
are
no
responses
within
this
question.
I
think
we're
looking
at
clustering.
I'm
going
to
say
segment.
Okay.
Well,
you're
right,
Olivia.
I
did
try
and
trick
you
a
little
bit
because
I
really
wanted
to
win.
But
you're
right,
that's
the
key
thing
here,
is
that
there
are
no
responses
here
in
this
analysis.
We
are
definitely
looking
at
segment.
When
our
goal
is
segment,
we
can
use
a
couple
of
different
clustering
tools.
We
can
do
Hierarchical
Clustering,
K-Means C
lustering,
or
Latent
Class
Analysis.
It's
important
to
keep
in
mind
that
with
Hierarchical
Clustering,
you
can
only
include...
Sorry,
you
can
include
all
of
the
modeling
types:
continuous,
nominal,
and
ordinal.
But
for
K-Means
Clustering,
you
can
only
include
variables
that
are
continuous.
For
Latent
Class
Analysis,
you
can
only
include
nominal
or
ordinal
variables.
In
our
case
here,
when
we're
looking
at
the
number
of
bedrooms,
lot
size,
year
built,
and
square
feet,
we
have
a
combination
of
continuous
and
nominal
variables.
Hierarchical
Clustering
may
be
the
best
clustering
tool
to
use
in
this
scenario.
It
looks
like
with
that
parallel
plot
with
Hierarchical
Clustering,
maybe
we
could
call
Cluster
6
Amazing
Location.
Yes.
If
you
think
a
large
lot
size
is an
amazing
location,
yeah,
we
can
definitely
call
that
segment
Amazing
Location
Homes.
Well,
all
right,
Olivia,
despite
me
giving
you
a
trick
in
that
last
question,
it
looks
like
we
ended
up
with
a
tie
here
again.
We'll
have
to
rematch
again
soon.
Absolutely.
We
talked
about
what
model
when,
and that
really,
what
model
you
choose
depends
on
your
goal
for
the
analysis,
whether
it's
segment,
explain,
predict,
or
identify.
Yeah,
in
terms
of
modeling
type,
again,
JMP
is
going
to
do
the
correct
analysis
for
you,
especially
with
your
responses.
If
you're
setting
them
up
with
the
correct
modeling
type,
JMP
is
going
to
do
the
correct
analysis
for
you.
If
your
goal
is
explain,
you
might
need
to
think
a
little
bit
about
which
modeling
type
to
use,
depending
on
how
you
want
to
explain
the
effect
of
something
like
the
number
of
bedrooms.
Thank
you,
Olivia.
This
is
so
much
fun.
Let's
do
it
again
next
year.
