Hello
.
I'm
Ryan Lekivetz
,
Manager
of
the
DOE
and
Reliability
team
at
JMP
.
And
I'm
all
Rory Lekivetz.
We're
here
today
to
talk
to
you
about
Easy DOE
.
The
question
is
it
easy
enough
for
a
seven-
year-
old? N
ow
that you're eight
years
older
you're a lot
wiser
to
answer
that
question.
For
those
of
you
who
don't
know
about
Easy DOE, so
it's
a
new
platform
and
JMP
17
.
Now
,
the
idea
with
Easy DOE
is
it's
going
to
be
a
new
file
type
that
encompasses
the
design
through
the
analysis
of
a
designed
experiment
.
No
more
do
you
need
to
worry
about
splitting
up
,
going
from
the
DOE
platform
to
a
data
table
and
then
running
the
analysis
separately
.
Now
,
the
idea
with
Easy DOE
is
that
we're
trying
to
aid
novice
users
through
that
entire
workflow
.
There's
going
to
be
a
guided
mode
where
we've
tried
to
add
hints
and
useful
defaults
to
guide
those
users
while
at
the
same
time
having
a
flexible
mode
for
those
who
are
more
comfortable
with
Easy DOE
.
Now
before
we
started
doing
this
idea
with
Easy DOE
and
running
our
experiment,
I
did
talk
to
Rory
about
the
daily
workflow
.
I
f
you
open
up
the
DOE
documentation
,
we
outline
this
idea
of
a
Easy DOE
workflow
which
goes
through
the
described
phase
,
which
is
where
we
identify
the
goal
and
the
responses
and
the
different
factors,
specify
where
we're
looking
at
our
model
.
We
create
the
design
,
collect
the
data
,
fit
a
model
to
that
data
,
and
then
use
that
model
to
predict
.
Right
now
,
if
you
think
about
the
way
traditionally
we've
done
this
in
JMP,
at
that
design
phase
is
where
we
create
the
data
table
.
Using
that
data
table
the
experimental
go
and
collect
the
data
and
then
perform
the
remaining
steps ending it.
N
ow
what
you'll
see
in
Easy DOE
,
there's
the
tabbed
interface
where
each
tab
represents
one
of
these
steps in
the DOE
workflow
.
Now
what
was
the
experiment
that
we
did
?
Paper
airplanes
.
Rory
had
found
a
website
that
talked
about
different
ways
to
create
paper
airplanes
.
You
want
to
tell
them
what
was
the
response
?
What
were
you
trying
to
measure
?
We
were
trying
to
measure
the
distance
which
was
inches
.
What
factors
did
you
end
up
deciding
that
we
could
change
?
For
factors
we
decided
on
war
plane
type,
paper
type,
flying force
and
paperclip
.
Yeah
.
Now
you
to
tell
them
about
some
of
these
different
tabs
.
Okay
,
so
let's
start
.
What
was
the
define
tab
?
The
define
tab
was
where
you
got
to
choose
your
factors
and
your
responses
.
That's
right
.
I
should
mention
here
as
well
that
when
we
were
using
Easy DOE
,
I
left
Lory
in
control
of
the
entire
platform
.
She
launched
it
.
She
was
the
one
entering
everything
and
clicking
between
tabs
and
all
of
that
.
I
think
after
the
define
tab
,
we
moved
to
the
next.
What
was
that
next
tab?
Model. F
or
the
model
tab
,
you
had
to
choose w
hich
one
of
these
four
was
the
best
for
your
experiment.
Now
I'll
say
too,
on
this
one
this
is
where
we
had
to
talk
a
little
bit
more
about
what
these
different
model
types
mean
.
Of
course
,
for
a
seven-
year-
old
and
even
an
eight-
year-
old
,
now
that
idea
of
understanding
interactions
can
be
a
difficult
thing
.
Now
,
the
main
effect
versus
the
interaction
.
One
of
the
nice
things
was
the
website
that
we
had
found
about
creating
paper
airplanes
.
It
talked
about
how
some
of
the
different
types
of
paper
airplanes
do
better
when
you
throw
it
hard
versus
light
.
It
already
had
discussed
that
idea
of
interactions,
so
that's
why
ultimately,
I
helped
her
decide
on
picking
that
two-
factor
interaction
model
with
the
main
effects
.
Once
we
had
that
model,
then
what
happened?
Then
was
the
design
.
The
design
shows
you
what
you're
going
to
be
making
.
Since
we
were
doing
paper
airplanes
and
we
entered
the
factors
for
tight
paper
throwing
for
some
paper
clip
,
then
it
sounds
like
different
types
,
different
papers,
different
throwing
forces
like
route
and
paper
clip
or
no paper clip.
Yes
,
I
think
we
made
the
16
different
paper
airplanes
and
so
each
one
was
a
different
one
.
I
think
we
put
a
number
on
it
.
Is
that
right
?
We
label
it
with
a
number
one
.
Yeah
.
Then
what
happens
after
we
have
that
design,
what
do
we
do
with
that
?
Then
we
do
good
data
entry
.
With
data
entry
is
where
you
enter
in
how
many
inches you want.
Yeah
.
I
think
we
went
outside and
we
took
those
paper
airplanes
and
we
flew
them
anyway
and
then
just
measured
that
.
Yeah
,
that's
right
.
W
hat
happened
after we had
that
data
entry
?
Then
we
go
to
be
analyzed
.
Analyze
is
where
you
figure
out
which
ones
were
the best.
Yeah
,
which
ones
really
were
impacting
that
distance
flown
.
Now
,
I
should
mention
here
,
so
this
is
a
novel
thing in Easy DOE
.
The
confidence
intervals
for
each
of
our
different
effects
are
clickable
.
All
right,
I
had
actually
thought
that
this
was
going
to
be
a
really
difficult
time
to
talk
about
the
analyzed
.
But
as
soon
as
we
got
there,
so
it
starts
with
all
of
the
terms
in
the
model
and
very
quickly
figured
out
that
you
could
click
on
them
.
She
looked
at
the
ones
that
were
close
to
zero
and
just
removed
them
.
On
the
top
was
actually
her
model
.
She
actually
picked
a
much
simpler
model
than
even
what
the
best
model
.
You'll
notice
there
is
a
best
model
one
.
But
one
could
argue
that
I
actually
might
even
prefer
her
model
to
the
one
that
was
picked
by
the
best
model
.
But
again
,
still
a
very
nice
way
to
play
around
with
your
model
and
see
what
happens
if
term
enter o
r
are
removed
just
by
clicking
on
those
confidence
intervals
.
T
hen
after
we
moved
to
the
analysis
,
what
was
that
tab they had?
The
predict
tab
was
where
you
could
see
which
types
or
which t
hings
were the
best
.
The
best
look
like
it
would
probably
be
a
dirt
metal
construction
and
differently
for
us
since
it
was
like,
the
hard in blue light
was
likeâ¦
Did
it
matter or
not
really?
Not
really
.
It
would
be
like
you
could do hard or light
in
your
paper
.
I
should
mention
here,
so
it
was
interesting
to
see
she
hadn't
really
seen
the
prediction
profiler
so
much
before
.
I
mean
,
wasn't
familiar
with
it
.
S
he
did
have
to
be
told
to
click
in
there
to
see
what
happens
.
But
even
for
a
seven-
year-
old
,
it's
interesting
to
see
once
they
have
that
sense
that
they
can
click
within
that
prediction
profiler
,
she
was
really
able
to
get
the
hang
of
it
.
J
ust
some
final
thoughts
from
Easy DOE
.
I
asked
Rory
a
few
questions
ahead
of
time
.
What
would
you
like
to
tell
people
about
Easy
DOE
?
It
was
really
fun
.
Yeah
.
If
you
were
to
do
this
experiment
again
,
would
you
change
or
what
would
you
change
?
The
factors
,
maybe
different
days
for
the
weather
.
You think like
it
might
be
windy
on
some
days
and
not
on
others
.
F
or
my
own
perspective
,
you
know
,
so
she
was
actually
able
to
complete
this
with
minimal
help
from
me
.
I
mean
,
she
was
in
control
the
entire
time of the
Easy DOE
platform
.
A
lot
of
these
different
choices
she
was
making
on
her
own
.
Even
when
it
came
to
the
factors
that
she
picked
.
As
well
she
actually
did
help
us
find
some
usability
issues
.
There
were
pieces
like
in
the
design
tab
that
I
think
we
improve
throughout
because
of
users
trying
this
out
not
just
for
her
,
but
as
well
as
other
users
that
we
had
in
the DOE
program
.
The
model
she
definitely
needed
help
with
,
but
the
analysis
was
easier
than
expected
.
Just
some
references
,
acknowledgments
.
Really
,
I
just
want
to
thank
all
the
members
of
JMP
that
helped
in
the
development
of Easy DOE
.
There's
a
huge
list
that
you'll
actually
see
.
We
have
a
Discovery
America
presentation
there
as
well
,
where
we
talk
about
this
in
a
little
bit
more
detail
.
Again
,
all
the
feedback
from
external
and
internal
users
that
have
seen
this
before
the
release
of
17
and
since
it's
been
released
.
Thank
you
for
your
time
and
joining
us
today
.
We
hope
you'll
join
us
during
Discovery
where
we
can
discuss
this
poster
.
Not
sure
yet
if
you'll
be
able
to
join
us
,
but
I
definitely
will
be
and
hopefully
you as
well
.
Thank
you
.
Thank
you
.
