Welcome
everybody
to
Pictures
from
the
Gallery
7.
So
my
name
is
Scott
Wise.
I'm
a
Senior
Systems
Engineer
in
the
US
West
Coast
and
I'm
joined
with
my
daughter
Samantha.
So
Sammy,
I
wanted
to
ask
you,
as
a
16
year
old
growing
up
during
a
pandemic
and
still
going
to
school
and
trying
to
find
your
path
in
the
world,
what
are
you
most
concerned
about
with
the
future?
Well,
to start, I'm
pretty
worried
about
how
we're
affecting
the
environment,
like
deforestation,
soil
depletion,
climate
change.
Additionally,
I'm
kind
of
worried
about
sexism
in
the
workplace,
and
gender
gap
wages,
and
things
like
that.
Okay,
well,
that's
a
lot
to
think
about.
So
you
got
me
thinking
as
well
about
what
we
can
all
do
to
help
make
this
a
better
place.
So
I
thought
I'd
dedicate
my
presentation
to
also
emphasizing
what
we
can
do
to
save
the
planet.
You
used
to
say
to
me,
"Be
curious...", right?
"and
do
something
about
it",
right?
So
we
can
use
our
curiosity,
our
time,
and
our
skills.
So
I'm
going
to
challenge
everybody
that's
seeing
this
video
and
myself
as
well,
that
we
share
meaningful
data.
So
a
good
place
to
do
that
is
with
the
Data
for
Green
J
MP website.
It's
a
good
place
to
see
what
kind
of
sources
of
data
are
out
there
for
us
to
understand
things
about
our
environment,
as
well
as
to
share
any
meaningful
data
that
we
have
or
any
meaningful
graphs
or
reports
that
we're
able
to
generate.
So
please
do
check
that
out.
I'll
leave
the
link
in
my Journal
for
you,
as
well
as
use
your
time.
So
here's
a
cool
use
of
your
spare
time.
Instead
of
maybe
playing
your
cell
phone
games
is
you
could
go
out
to
this
II
ASA website
and
on
this
website,
they
give
you
images
of
the
rainforest.
They
take
it
from
the
satellite
and
they
ask
your
help
in
identifying
where
there's
roads
and
structures,
but
also
where
there's
untouched
portions
of
the
rainforest.
And
this
feeds
their
artificial
intelligence
models
to
help
them
do
a
better
estimate
of
the
rate
of
deforestation
in
the
rainforest.
So
use
your
time
and
then
definitely
use
your
skills.
And
if
you're
here,
you've
picked
up
some
good
JMP
skills
to
analyze,
graph,
and
explore
your
data
and
get
inspired
from
our
friends
like at
WildTrack,
where
Sky
and
Zoe
are
doing
a
great
job.
Not
only
helping
protect
our
endangered
species
with
their
non-
invasive
wildlife
tracking
methods,
but
also
using
analytics,
using
JMP
in
very
novel
and
creative
ways.
So
definitely
read
up
on
the
stories
to
see
how
we
can
do
better
and
we
can
inspire
ourselves
to
also
use
our
skills
to
do
better.
So
thank
you,
Sammy,
for
all
that
help.
And
I'm
going
to
continue
on
and
to
show
the
pictures
from
the
Gallery.
And
instead
of
just
showing
it
on
fun
data,
I'm
going
to
show
our
advanced
pictures
on
data
that
reflects
some
data
for
good,
Data
for
Green
topics.
So
what
we
got
keyed
up,
number
one
is
this
equality.
So
some
equality
data
on
the
gender
wage
gap,
which
is
we're
going
to
do
the
interval
charts,
also
called
a
dumbbell
chart.
That
was
rated
number
one
in
terms
of
selections
this
year.
Number
two
would
be
this
pandemic
data,
which
we
are
looking
at
how
teachers
are
affected
by
teaching
in
the
pandemic.
And
we're
using
a
word
cloud.
B
et you
didn't
know
you
could
do
word
clouds
within
Graph
Build.
Third
one
is
going
to
be
looking
at
tree
cover
loss
over
our
planet
from
this
new
feature,
which
is
a
smoother
line
which
uses
moving
averages.
Last
is
going
to
be
some
safety
data,
or
next
to
last,
which
is
the
points
cumulative
sum
chart.
And
so
this
is
a
new
way
of
quickly
looking
at
cumulative
figures
within
your
graph,
even
when
you're
just
looking
at
the
points
element.
So
another
thing
you
can
do
in
Graph Builder
is
now
do
some,
I
think,
better
looking
and
more
advanced
box
plots.
And
so
we're
going
to
do
that
on
climate
change
and
looking
at
some
projections
for
city
risk
going
out
to
2050.
And
then
if
we
have
time,
I
will
show
you
a
little
bit
about
some
things
we
can
do
with
summarizing
vector
data,
such
as
wind
direction
and
speed,
which
comes
into
play
a
lot
when
we
got
a
lot
of
adverse
weather
due
to
climate
change.
And
a
wind
rose
chart
is
what
we're
going
to
feature
there.
So
these
are
the
pictures
from
the
Gallery
I
will
go
through
each
of
these
individually
as
time
allows
here.
Now,
just
so
you
know,
this
is
a
Journal
I'm
giving
to
you.
It
is
already
out
there
on
the
link.
You
can
download
it.
You
will
have
full
pictures
of
the
graph.
You
will
have
tips
and
tricks.
You
will
have,
as
well,
full
steps
on
how
to
create
that
graph.
And
I
will
leave
you
with
the
data.
And
you
will
not
only
have
the
data,
but
you
will
have
scripts
in
the
data
to
regenerate
the
graphs
so
you
can
build
your
own
and
you
can
compare
and
see
if
your
graphs
look
like
the
ones
that
we
created.
All
right,
so
let's
get
going.
So
our
first
chart
here
is
gender
wage
gap,
and
it's
a
dumbbell
chart.
And
it's
because
we
have
these
interval
charts.
And
with
these
interval
charts,
you
might
see
that
we
kind
of
have
large
points
on
the
end
of
the
interval.
So
you're
trying
to
make
a
comparison
between
two
points,
and
you
can
see
the
distance
in
between
them
as
a
bar.
And
some
people
thought
these
looked
a
lot
like
weight
lifting.
You
go
to
the
gym
and
you're
lifting
some
free
weights
and
you're
going
to
pick
up
a
barbell
or
a
dumbbell,
and
it's
going
to
have
weights
on
the
end
with
the
bar
in
the
middle
that
you
grasp.
That's
kind
of
what
they
think
it
looks
like
some
people
also
call this
an interval
chart.
So
you
can
see
it's
easy
to
see
that
males
make
more
than
females
in
France.
And
there
seems
to
be
a
large
wage
gap
that
doesn't
seem
to
be
closing
as
you
go
through
the
years.
So
let's
see
how
to
create
this.
So
I'm
going
to
go
back
to
the
data
table
we
just
opened
up
from
our
Journal.
And
the
secret
to
making
this
is
you
want
to
have
two
columns
of
information
to
compare.
You
want
them
to
be
over
the
same
scale.
And
so
female
and
male
monthly
nominal
salaries
is
what
we're
comparing
here.
They
normalize
all
the
countries'
currencies
in
the
US
dollars.
And
so
I
should
be
able
to
use
those
in
our
graph.
So
I'm
going
to
open
up
my
Graph Builder.
Start
with
the
blank
Graph Builder.
I'm
going
to
take
female
monthly
and
male
monthly
and
put
them
both
on
the
X
axis.
I'm
going
to
put
year
on
the
Y
axis.
Now
I'm
going
to
copy
both
female
and
male
monthly,
and
I'm
going
to
move
those
into
the
interval.
Now
it's
actually
doing
the
job,
but
it's
hard
to
see
because
we
have
so
many
countries
represented
for
each
year
and
their
intervals
are
all
on
top
of
each
other.
So
let's
go
under
the
red
triangle
where
it
says
Graph Builder.
And
let's
go
ahead
and
do
a
local
data
filter.
And
let's
go
ahead
and
just
look
at
a
certain
country
and
we
are
going
to
look
at
France.
Now
I've
got
France
here
and
it's
starting
to
look
like
my
interval
chart.
I'll
say
done
here
in
the
Graph Builder
to
close
my
little
control
panels.
Most
of
the
things
I'm
going
to
have
to
change
here
are
going
to
be
directly
on
the
graph,
such
as
I'd
like
to
get
the
ends
of
these
intervals,
the
points
to
be
bigger.
And so to
do
that,
I'm
just
going
to
click
right
on
the
legend,
right
click,
you
go
marker
size.
I'm
going
to
go
other,
I'm
going
to
make
it
a
big
ten.
So
you
see
how
much
bigger
it
looks
now.
I'm
going
to
do
the
same
thing
with
male
monthly
with
marker
size
I'll
go
other
and
I'll
go
10.
Pretty
cool.
Now,
what
about
the
bar?
I'm
going
to
right
click,
I'm
going
to
go
customize,
and
it's
the
second
error bar
you're
going
to
see
is
the
one
that's
going
to
show
up
on
top
here.
And
the
line
style
is
fine
with
me.
I'm
just
going
to
make
it
gray
and
I'm
going
to
increase
the
line
width
to
a
two
or
three.
I'll
do
this
one
on
a
two.
And
now
I
get
the
view
that
I
like.
Now,
a
couple
of
things
I'm
going
to
do
in
the
X
axis.
I'm
going
to
right
click
on
the
axis
settings,
and
I'm
going
to
put
a
pre
average
salary
of
$3,500
monthly.
I'll
say
okay.
And
now
I
have
that
line
driven.
So
I
can
kind
of
see
if
things
are
increasing
or
decreasing
over
the
years.
But
it
looks
like
my
years
are
going
and
kind
of
from
bottom
up.
I
like
them
to
go
from
top
down.
So
I'm
going
to
right
click
under
axis
settings.
And
I'm
just
going
to
reverse
the
order
on
the
scale.
And
you
can
see
now
I'm
going
for
2010
down
to
2019.
I
click
on
that.
And
now
I
can
truly
judge
what's
going
on.
Now,
one
other
thing
I
thought
was
pretty
cool.
If
I
switched
the
colors
here,
if
I
switch
them,
if
I
make
the
female
the
red
and
I
make
the
male
the
blue,
then
I
can
bring
in
a
picture.
And
I've
got
this
kind
of
great
picture
you
can
see
a
small
little
icon
of
it
that
I
think
will
look
cool
in
the
background
so
I
click
on
it.
You
can't
really
see
what's
going
on.
It's
there.
All
I
have
to
do
is
right
click,
go
to
images,
size
and
scale,
build
graph.
And
there
it
is.
I'm
going
to
right
click
again
and
go
back
to
images.
And
I'm
going
to
go
transparency,
and
I'm
going
to
make
that
a point
three.
And
now
you
can
see
it's
kind
of
more
muted
in
the
background.
So
you
don't
want
to
bring
in
background
pictures
if
you're
going
to
create
a
complicated
chart
or
you're
going
to
switch
between
a
lot
of
different
maybe
filter
options
or
have
many
different
panes
open
because
it
can
get
tedious
to
have
to
keep
resizing
the
picture.
But
it's
great
for
stand
alone
chart.
So
here
we
go.
We
got
our
stand
alone
chart.
I
am
going
to
close
this
one
down
and
show
you
we
have
them
available
to
you
directly
as
a
link
in
your
data
table.
And
the
one
I
was
looking
at
was
without
the
picture.
But
I
did
a
couple
side
by
side.
And
this
kind
of
let
me
look
at
France
versus
Germany
versus
Sweden.
And
I
can
see
in
France,
the
gap
is
not
as
necessary,
as
bad
as
I
thought
it
was,
there's
a
bigger
wage
gap
in
Germany,
but
in
Germany,
everybody
seems
to
be
making
more
money.
But
if
you
go
to
Sweden,
it's
the
females
that
make
more
than
the
males.
And
so
I
thought
that
was
very
interesting.
So
here's
some
good
information
and
data
to
play
with.
This
data
came
from
the
International
Labor
Organization.
And
I
will
put
all
the
links
into
the
data
table
so
you
can
find
them
where
they
are
housed
publicly.
All
right,
that
was
our
most
popular
view.
Let's
take
a
look
at
our
second
most
popular
view.
That
would
be
how
do
you
get
a
word
cloud
out
of
Graph Builder
a
lot
of
people
asked
about
this.
And
all
you
have
to
do
is
we're
going
to
have
to
create
some
ordering
columns
that's
going
to
help
us
figure
out,
let
JMP
figure
out
how
to
display
my
words
within
a
cloud-
like
shape.
So
I'll
show
you
what's
going
on.
So
I'll
open
up
this
educator
for
top
five
COVID
words.
So
they
went
out...
An
education
article
went
out
in
the
state
of
Kentucky
and
they
just
said,
"What
are
the
top
five
words
you
think
about
when
you
hear
COVID?"
to
school
teachers, right?
School
teachers
have
been
by
far
adversely
affected
by
the
pandemic,
having
to
try
to
teach
the
schools
as
they
close,
as
they
reopen,
as
they
do
it
with
mixed media
you
have
to
do
some
classes
live,
some
classes
on
site,
mixed
classrooms.
So
it's
been
very
difficult.
And
you
can
see
some
of
the
words
that
have
popped
out
get
captured
here
in
this
column
and
you
can
see
how
many
respondents
said
those
words
in
their
top
five.
Like
20
of
them
basically
said...
The
word
anxious
showed
up
to
20
of
the
respondees.
As
a
top
five
word
associated
with
the
pandemic.
So
I
have
that
information.
So
I
created
two
columns.
The
first
column
is
a
random
column
and
it's
just
random
normal
information
being
assigned
to
the
rows.
And
I
got
it
just
from
creating
a
column,
right
clicking, going into
the
column
info
initializing
some
random
data
versus
initialized
data.
You
can
do
random.
You
can
pick
a
random
normal.
I
figure
if
you
have
a
mound
shape,
right,
a
bell
shape
distribution,
it's
kind
of
like
a
cloud.
Most
of
the
stuff
is
going
to
be
in
the
middle
and
less
is
going
to
be
out
toward
the
end
toward
the tails.
So
I
did
that
and
that's
how
I
came
up
with
this
random
data.
I'll
go
ahead
and
delete
that
one.
For
the
order
column,
it
was
pretty
easy.
I
just
sorted
by
weight
and
you
can
see
so
anxious
was
20,
then
17,
then
17.
So
basically
the
order
is
just
a
row
sorted
order.
So
anxious
is
number
one
because
it
was
biggest
and
that's
going
to
help
me
later
make
the
word
cloud.
So
let's
go
ahead,
click
on
our...
Graph Builder.
Let's go to...
Before
we
do
anything
with
the
word,
let's
actually
go
to
the
random.
And
let's
put
random
on
the
Y
and
it's
doing
what
we
expected.
It's
distributing
the
rows
out
randomly
on
the
Y
axis,
just
kind
of
separating
them
out.
What
if
we
swapped
those
points
for
the
word
itself?
So
if
you
go
to
points
under
the
red
triangle
and
you
click
on
it,
let's
go
ahead
and
select
set
shape
column.
When
I
click
on
that
one,
I'll
go
ahead
and
click
on
the
words
and
there's
the
words.
I'm
going
to
take
the
weight
of
the
words
and
I'm
going
to
size
it.
Oh
that's
starting
to
look
like
a
word
cloud,
right?
Now
I'm
going
to
take
that
weight
again
and
maybe
give
it some sort
of
scale
coloring
there.
So
I
guess
the
more
red,
the
more
frequency
we
had
in
that
word,
which
is
kind
of
cool.
Now
what's
going
on
is
it's
doing
a
jittering
and
it's
doing
centering
grid
jittering.
That's
actually
the
automatic
default
is
the
center
grid
so
that
looks
pretty
good
and
look
when
you're
done
as
you
move
it
around, it will...
it will try
to
adjust
the
words
within
that
shape
and
try
to
hold
that
type
of
scale.
That's
really
easy
to
do.
I'm
going
to
right
click
under
the
red
triangle
for
Graph Builder
and
under
legend
position
I'm
going
to
go
inside
bottom
right.
It
may
be
inside
bottom
left.
I
think
I
like
that
one
better.
I'll
put
it
right
to
the
inside
bottom
left
and
I'm
going
to
go
to
legend
settings
and
I'm
just
going
to
keep
the
bit
that
talks
about
the
color
so
that's
pretty
cool
and
that's
how
you
would
do
the
random
word
cloud.
How
I
would
do
the
ordered
one
is
I'll
go
back
to
the
red
triangle
under
the
Graph Builder
I'll
say
show
control
panel.
I
will
swap
out
random
for
order.
Now
all
the
big
ones
are
on
the
bottom
and
the
smaller
ones
are
at
the
top.
That
makes
sense.
Maybe
I'll
right
click
on
this
axis
setting.
You
saw
me
do
this
earlier.
Go
back
and
reverse
the
order
so
it's
going
from
0-30,
with
the
ones
and
the
twos
and
the
threes
being
more
on
the
top.
There
we
go.
That's
exactly
what
I
wanted
to
see
and
now
I
have
an
ordered
one.
Now
with
ordered
low,
a
lot
of
times
it
might
make
sense.
Here
if
I
really
wanted
to
have
it
in
order
ed order,
I need to use
a
certain
jittering
that's
going
to
kind
of
justify
the
ordering.
So
I'm
going
to
show
back
the
control
panel.
I'm
going
to
go
to
the
jitter
style
instead
of
center
grid,
I
do
positive
grid
and
now
it
really
is
ordered,
going
from
left
to
the
right,
top
to
bottom
with
the
number
one
thing,
then
the
number
two,
then
the
number
three
and
then
the
next
line,
the
number
four,
and
so
on.
And
sometimes
people
prefer this
kind
of
word
cloud, because...
it
makes
it
easier
to
judge
which
one's
bigger
than
the
other
if
the
size
and
the
color
are
similar.
Now
you
can
make
a
judgment.
Now
it's
looking
good
from
what
I
want
it
to
do,
but
it's
not
looking
good
on
the
graph.
What
are
we
going
to
do
about
this
one?
So
I
really
would
like
to
move
this
whole
thing
over
to
the
right.
So
to
do
that
one,
I'll
take
my
control
panel
back
open.
I'll
go
under
this
X axis.
And
even
though
there's
nothing
there,
I'll
right
click.
I'll
say
access
settings,
and
I
will
do
a
negative
point three
for
the
minimum.
And
now
it's
moved
over.
And
now
this
looks
a
little
more
like
an
ordered
word
cloud.
And
of
course,
you
can
bring
in
pictures
as
well.
And
I
did
that
on
this
one.
I
brought
in
a
nice
apple
with
a
little
picture
with
a
little
bit
of
transparency
on
it
and
make
the
words
pop
out
on
top
of
it.
All
right,
so
instructions
are
there
if
you
want
to
try
this
one.
But
I
can
see
a
lot
of
people
having,
again,
a
bunch
of
words,
a
bunch
of
categories,
a
bunch
of
phrases.
And
if
you
got
a
weight
on
them,
got
to
count
on
them,
you
can
make
a
work
cloud.
All
right.
So
let's
see
here
where
we
are.
We
are
ready
to
go
to
the
moving
average
smoother
chart.
And
this
one's
pretty
cool.
It's
a
new
smoothing
line
option
that
lets
you
kind
of
look
for
trends.
And
a
popular
way
to
look
for
trends
is
a
moving
average.
When
there's
a
lot
of
noise in...
the
things
that
you're
plotting
over
time,
then
sometimes
you
want
to
smooth
it.
You
see
that
blue
line
here
is
smoothed
in
between
the
ups
and
downs
of
those
points
that
are
being
collected
over
the
years.
The
other
thing
it's
doing
here,
as
you
can
see
I don't
have
a
legend.
I've
actually
labeled
the
lines.
I'm
going
to
show
you
how
you
can
do
that
well
as
well.
All
right,
so
here
I'm
going
to
click
on
this
tree
loss.
What
we're
looking
at
here
is
reasons
for
loss
of
tree
cover
or
deforestations.
This
came
from
the
Global
Forest
Watch,
and
they
have
Tree
Loss
in hectares.
So
we'll
go
ahead
and
show
that
one.
Here's
tree
loss in hectares
and
I'm
going
to
throw
my
year
down
here.
And
I've
got
both
points
and
smoothers
automatically
showing.
So
all
I
really
do
is
just
got
to
take
the
drivers
and
overlay
by
the
drivers.
That's
pretty
cool.
Now
I've
got
just
the
s
pline method
and
I'm not
as
big
on
that
one
format showing
a
trend.
So
if
I
click
on
the
options,
here's
where
you
have
more
options
in
JMP
16
and
I
can
do
the
moving
average.
I'm
going
to
do
the
moving
average.
I'm
going
to
move
back
this
little
local
width
toggle
so
it
fills
out
the
graph.
And
I'm
even
going
to
include
this
confidence
fit.
So
this
is
looking
pretty
good.
So
I'm
going
to
say
done
here.
Now
I'm
going
to
go
under
the
red
hotspot
for
Graph Builder.
I'm
going
to
go
to
the
local
data
filter.
I'm
going
to
add
drivers
and
I'm
just
going
to
look
at
the
first
three
drivers.
Okay,
so
these
are
looking
pretty
good.
Now
I've
got
this
legend
way
over
here
and
it's
not
really
adding
to
the
graph.
I
would
like
to
do
something
better.
Well,
I'm
going
to
right
click
right
on
the
legend
line
for
in
this
case
the
agricultural
shift.
And
I'm
going
to
go
to
label,
and this
is
brand
new
in
16.
See,
I
can
do
min
maximum
values,
first
values.
But
I
can
also
do
a
name.
Look
where
it
puts
it.
It
looks
it
to
the
right
in
the
graph.
I'm
going
to
do
the
same
thing
with
each
of
them.
Each
of
the
ones
I
have
open.
Now
I'm
going
to
go
under
the
red
triangle
for
the
Graph Builder
and
go
to
show
and
turn
off
the
legend.
I
don't
need
it
anymore
and
I
don't
want
them
on
the
side.
Look
what
happens
when
I
start
to
move
it
back.
You
can
see
I
can
move
it
into
the
body
of
the
graph.
And
I'm
going
to
stretch
this
out
a
little
bit
on
our
screen
and
you
can
see
agricultural
shift.
If
you
put
it
close
to
the
line,
it
tries
to
hug...
the
slope
of
the
line
which
is
really
cool.
I'm
going
to
put
agricultural
shift
there.
Maybe
I'll
put
commodity
driven
just
over
here
and
maybe
forest
driven
right
in
this
area
here.
And
now
you
don't
need
the
legend.
Now
you
can
just
see
what's
going
on
with
the
light
and
kind
of
let
people's
eyes
go
where
they
have
interest
in
the
agricultural
shift.
Here
was
the
real
story,
how
this
was
more
of
an
assignable
cause
of
loss
of
tree
cover,
but
it
has
gone
down
a
little
bit
here
in
recent
times.
All
right,
so
pretty
cool
graph
as
well.
So
smooth
line
moving
average.
Points
cumulative
summary
chart.
So
this
was
cool
data.
This
was
actually
data
on
driving
safety.
And
kind
of
the
idea
was
if
we
could
point
out
over
the
years
times
when
there's
been
major
releases
in
car
safety,
are
we
getting
safer?
Right?
As
more
vehicles
are
on
the
road,
we
expect
there
to
be
some
more
crashes
just
from
the
volume
increasing.
But
are
we
actually
having
less
injuries
as
our
cars
helping
us
with
airbags
and
anti
lock
brakes
and
these
things?
And
we
did
this
with
a
points
chart.
And
we
really
benefited
from
using
cumulative
sum
options
that
are
new
in
JMP
16
right
off
the
points
chart.
You
can
always
create
a
cumulative
sum
column
very
easily
in
JMP.
But
we
think
this
is
better.
I'll
show
you
why.
So
if
I
go
to
this
motor
vehicle
safety
and
this
came
from
the
US
Bureau of
Transportation
Statistics,
I
have...
crash
rates
and
I
have
injury
rates.
And
the
rates
basically
have
taken
the
total
divided
by
the
vehicle-
miles
in millions.
So
you
kind
of
get
an
index.
So
I'm
going
to
go
open
up
Graph Builder.
I'm
going
to
take
crash
rate
and injury
rate
and
put
it
on
the
Y
axis.
I'm
going
to
put
year
on
the
X
axis.
I'm
going
to
pull
off
the
smoother
line
and
just
leave
the
points.
And
you
can
see
why
this
is
not
so
great,
because
what's
going
on
right
now
is
I
see
a
trend.
I
see
a
trend
of
crash
rates
kind
of
going
down
over
time,
which
is
good
news.
But
is
it
different
than
the
slope
of
the
rate
of
change
going
on
by
injury
rate?
Scale
can
make
it
hard
for
us
to
make
that
judgment.
And
you're
just
trying
to
compare
patterns
here
and
they're
not
next
to
each
other.
They're
ones
above
the
other
one.
It's
kind
of
hard
to
see.
So
with
one
selection
here
under
summary
statistic,
there's
a
new
cumulative
sum.
Now
tell
me
which
one
you
think
has
a
steeper
slope.
And
the
crash
rate
is
going
up
at
a
pretty
good
cumulative
growth
rate.
But
you
can
see
the
injury
rate
is
kind
of
leveling
out
a
little
bit.
It
is
going
up,
but
not
as
steeply.
So
I
could
summarize
that
the
vances
are
definitely...
We're
still
getting
into
a
lot
of
crashes
because
there's
more
cars
in
the
road.
But
it
seems
like
we
are...
saving
ourselves
some
injuries
here
and
that's
a
good
thing.
And
so
the
fun
thing
I
was
able
to
do,
you
can
see
back
in
the
data,
I
was
able
to
put
in
some...
safety
innovation.
So
like
in
1996,
we
had
side
impact
testing.
We
had
dual
front
airbags
in
1998.
So
we
can
go
in
and
go
to
our
a
xis setting.
And
this
is
a
good
place
for
a
reference
line,
1998,
we'll
put
a
dashed
line,
maybe
a
dash
gray
line,
and
we
will
say
air bags.
We'll
add
those
and
there
you
go.
And
now
we
can
see
where
the
air
bags
came
in
and
what
the
performance
was.
Now
the
other
thing
we
can
do
is
instead
of
using
points,
we
can
go
to
the
red
triangle
where
the
points
are.
And
we
can
use
the
shape
column
here
as
well
for
something
that's
not
even
categorical,
for
something
that's
continuous, like year
and
it
will
still
bring
in
those
values.
I'll
say
done.
I
will
make
these
a
little
bigger
with
marker
size.
Maybe...
make
them
pop
out
a
little
bit
more
on
our
screen
and
make
that
a
little
bigger,
maybe
even
tuck
in
on
the
legend
position.
Put
this
on
the
inside
left,
and
by
the
way,
you
see
where
these
highlighted
areas
are
on
my
screen,
you
can
put
them
in
the
corners
or
you
can
put
them
again
out
on
the
side
of
the
graph.
Just
drag
them
there.
But
there
we
go.
And
now
we
can
kind
of
see
how
these
early
advancements
have
really
done
the
job
to
protect
us,
so even
though
there's
more
of
us
and
there's
more
traffic
and
there's
more
cars
and
there's
going
to
be
more
accidents,
just
hopefully
less
serious
ones.
Very
cool.
All
right.
So
hopefully
you're
enjoying
these
like
I
am.
Remember,
I'm
giving
you
this
Journal
so
you
can
go
and
recreate
them
at
any
time.
We
will
show
you
the
fifth
most
popular
chart,
which
is
the
advanced
box
plot.
Now,
on
this
chart,
this
is
using
some
JMP
16
new
features
that
allow
us
to
really
help
the
Graph
Builder
graphics
to
give
us
a
lot
of
ways
to
visualize
the
box
plots.
And
also,
you're
going
to
see that
this
is
a
good
way
we
can
do
some
interactive
labeling
as
well.
So
I
take
this
data
that
I
found
from
Nestpick.
It's climate
change
city
index.
And
it
was
quite
interesting.
They
came
up
with
a
total
climate
change
risk
and
it
went
from
1-100.
And
they
base
that
off
of
climate
shift,
of
temperature
shift,
and
potential
sea
rise, and...
...water
stress, and
that
was
the
one
I
really
hadn't
thought
of,
like,
will
there
be
water
around
your
town
in
30
years?
So
in
2050,
it
said
that
these
were
going
to
be
the
cities
based
on
total
risk
that were the most
going
to
have
the
most
problems.
So
Bangkok
was
number
one,
Marrakesh
was
number
ten
going
on
down.
So
I
turned
on
the
labels
for
these
rows
so
it
will
pop
up
in
our
graph.
So
let's
take
a
look
at
it.
So
I'm
going
to
go
Graph Builder.
I'm
going
to
take
the
total
climate
change
risk
on
the
bottom.
You
can
see
there's
already
starting
to
be
some
things
getting
labeled
from
having
asked
for
labels
in
my
data
on
those
rows
.
I'm
going
to
look
at
it
by
region.
So
I
got
three
areas
here.
It's
looking
at
it
by
region.
And
now
I
can
say,
well,
let's
make
this
a
different
color.
Let's
make
this
red.
I
want
to
right
click
there,
right
on
the
points
on
the
Legends
and
make
it
a
little
bigger
here.
Now
the
points
are
standing
out
a
little
bit.
Now
let's
hold
our
shift
key
down
and
let's
add
in
a
box
plot.
Now
this
is
a
typical
box
plot.
It's
not
that
interesting.
You
might
notice
Amsterdam
showing
up
twice
and
Bangkok is.
That's
because
the
box
plot
is
labeling
the
outliers.
Well, the
points
are
already
represented
so
we
can
turn
that
outlier
box
off.
I
don't
like
this
view
low.
I've
always
kind
of
liked
the
saw
with
views,
but
I've
got
points
showing
up
well
with
this
open
box
plot.
But
if
I
change
the
box
type,
not
the
box
type.
Actually,
the
box
style,
to
solid,
all
of
a
sudden
it's
gray,
it
covers
it
up,
and
I
don't
see
the
fences
of
the
whiskers
kind
of
where
my
normal
spread
of
point
should
be,
I
lose
that.
Well,
not
exactly.
If
I
go
to
box
plots
now
and
I
just
click
on
the
red
triangle,
I
can
do
notched
and
I
can
do
fences.
So
the
notch
actually
kind
of
notches
the
figure
right
where
the
median
is.
My
boss
likes
to
call
this
a
torpedo.
Looks
like
a
torpedo.
And
then
I
can
add
the
fences
back
to
see
where
the
ends
of
my
whiskers
are
from
my
box
plot,
which
really
does
help.
Now
I'm
going
to
say
done.
Now
you're
going
to
say,
"Well,
Scott,
it's
still
covering
up
the
points."
When
I
make
this
really
big
on
our
screen,
all
you
have
to
do
is
right
click
on
the
graph,
go
to
points
and
say,
you
know
what,
move
that
forward.
And
there
you
go.
And
now
I
can
see
where
all...
my
top
ten
at
risk
cities
fall
on
this
list.
And
it
was
interesting.
I
heard
some
surprises
that
I
didn't
expect.
Like,
I
thought
that
New
Orleans
here
in
the
US,
because
it's
so
low
and
so
prone
to
floodings,
especially
from
natural
disasters
like
Hurricanes,
it
would
come
out
higher.
2050
maybe
Boston
is
not
a
great
place
to
be.
So
that
was
really
interesting
to
me.
Fun
data
to
play
with.
And
this
is
some
fun
things
to
do
with
points
and
box
plots.
All
right,
so
we
are
nearly
out
of
time.
I
will
just
quickly
show
you
the
last
one
is
this
wind
rose
chart.
And
the
wind
rose
chart
is
a
way
of
looking
at
vector
information.
In
this
case,
you
see
all
these
little
arrows
here.
Those
are
all
measured
wind
speeds
and
directions
that
came
out
of
looking
at
the
Great
Lakes
area
around
Chicago.
And
so
they
wanted
to
summarize
it.
So
they
were
able
to
come
up
with
a
special
type
of
pie
chart.
It's
called
a
coxcomb
chart
that
actually
will
let
you
kind
of
mimic
a
compass.
So
the
compass
rose
is
kind
of
what
you're
used
to
seeing
on
like
compasses
on
the
face,
right?
Yo
u know you
get
North,
South,
East,
West.
Well,
we've
kind
of
doing
the
same
thing
with
the
wind
rose.
So
it's
kind
of
summarized
all
this
data.
So
to
make
it,
it's
pretty
easy,
we
just
go
ahead
and
we've
got
our
positions
for
our
wind.
We
have
the
speed
of
our
wind,
but
we
also
have,
in
this
case...
the
direction
of
the
wind.
And
this
one
went
into
the
230th
degree
of
the
compass
so
that
would
be
a
west
southwest.
So
when
we
go
on
the
graph
it
all
we
have
to
do
is
take
those
sections
that
we've
identified,
pop
them
on
down
here,
ask
for
the
bar
chart,
ask
for
the
coxcomb chart
and
I'm
going
to
take
the
speed
and
I'm
going
to
put
it
in
the
overlay
and
now
it's
easy
to
see
that
a
lot
of
my
data
is
coming
out
of...
I put that
one
right
there.
Now
I
can
see
a
lot
of
my
data
is
coming
out
of
the
western
section,
the
northwest
in
particular,
especially
with
the
larger
orange
segments
where
it's
got
a
higher
count
and
of
course
I
brought
in
a
nifty...
background
map
and
so
you
can
make
it
look
really
cool
and
if
you
want
to
learn
how
to
draw
those
arrows
I've
included
those
as
well.
That
is
something
you
would
do
under
points,
set
shape,
expression
and
I'm
showing
you
how
you
can
put
in
just
a
little of
JSL
scripting
and
you
can
draw
these
wind
directions
and
the
length
of
the
arrow
is
the
strength
of
the
wind.
That's
pretty
cool.
All
right,
so
we
are
right
at
time
and
I'm
going
to
include
in
your
Journal
where
to
learn
more
so
you
can
learn
from
the
other
galleries,
you
can
learn
from
the
other
blogs
and
journals,
the
other
presentations
as
well
as
other
tutorials
just
off
the
JMP
community
so
please
do
learn
more
about
Graph Builder
and
please
do
share
your
data
at
the
JMP Data
for
Green.
All
right
so
please
email
me
or
contact
me
if
you'd
like
to
talk
more
about
Graph
Build
or
see
any
of
these
views
differently.
And
I
thank
you
and
I
hope
you
enjoy
your
discovery
and
please
do
go
help
save
the
planet
and
get
curious
and
share
your
results.
