Hi
,
I
'm
Annie
Dudley
,
and
I
am
a
Control
Chart
Developer
for
JMP
.
I
am
here
today
to
talk
with
you
about
the
new
in
JMP
version
17
Laney
P
'
and
U
'
control
charts
.
Let
's
review.
The
control
charts
are
intended
to
show
the
stability
of
your
process
.
If
your
process
is
not
stable
,
then
you
can
't
reliably
make
the
same
size
or
the
same
parts
,
and
customers
will
get
upset
and
not
want
to
purchase
from
you
again
.
In
this
case
,
we
're
talking
specifically
about
attribute
charts
,
which
are
based
either
on
the
Binomial
or
the
Poisson
distribution
,
and
they
assume
a
constant
variance,
a
constant
variance
because
both
the
Binomial
and
the
Poisson
are
a
one
parameter
distribution
.
You
got
one
parameter
for
the
mean
and
then
manipulations
on
that
for
the
variance
.
But
what
happens
if
your
variance
is
not
constant
over
time
?
When
you
have
a
non
-constant
variance
,
in
other
words
,
if
you
have
more
variance
in
your
model
or
in
some
cases
,
less
,
more
variance
in
your
model
than
you
're
currently
describing
,
that
's
referred
to
as
overdispersion
.
One
parameter
model
cannot
model
overdispersion
.
David
Laney
proposed
that
we
normalize
the
data
and
we
account
for
varying
subgroup
sizes
,
we
compute
a
moving
range
,
and
average
that
,
and
then
we
insert
that
into
our
model
,
into
our
limits
for
the
control
charts
.
Let
's
take
a
look
at
this
in
JMP
.
Let
's
keep
in
mind
this
assumption
is
that
for
the
regular
P
and
U
charts
,
we
have
this
assumption
of
the
probability
of
nonconformity
is
the
same
for
each
sample
.
In
other
words
,
we
have
assumption
that
our
variance
is
constant
.
Let
's
look
briefly
at
our
limits
formulas
.
For
the
P
chart
,
it
's
the
average
plus
or
minus
3
times
our
standard
error
and
the
same
with
the
U
chart
.
Again
,
remember
,
we
have
this
one
parameter
.
There
's
not
much
that
we
can
do
if
our
limits
are
not
constant
here
or
if
our
variance
is
not
constant
.
What
do
we
do
if
we
have
overdispersion
?
This
is
the
big
question
.
What
Laney
proposed
was
basically
we
standardize
our
data
,
we
take
the
moving
range
,
we
compute
an
average
moving
range
,
and
then
we
adjust
that
and
form
a
sigma
sub
Z
.
These
limits
that
Laney
is
proposing
look
very
similar
to
the
other
limits
.
We
were
just
inserting
the
sigma
sub
Z
into
the
formula
right
before
the
standard
error
for
both
the
P
charts
and
the
U
charts
.
Let
's
take
a
look
at
an
example
.
I
'm
going
to
start
with
a
data
set
from
the
JMP
sample
data
folder
.
We
have
lot
sizes
that
are
not
terribly
large
.
F
or
our
first
example
,
let
's
look
at
this
one
.
I
'm
going
to
go
through
the
interface
for
this
first
one
.
We
'll
use
dialogs
for
future
ones
.
We
have
the
number
of
defective
washers
that
we
're
going
to
model
.
I
'm
going
to
change
the
chart
type
to
attribute
.
Before
we
go
to
Poisson
,
I
have
my
lot
as
my i
dentifier
for
our
X
-axis
,
and
we
're
just
going
to
use
the
constant
lot
size
of
400
.
Drop
that
in
the
n Trials
drop
zone
.
Now
,
Laney
's
charts
are
only
available
when
our
statistic
is
proportioned
,
so
I
have
to
change
that
back
to
proportion
.
When
the
statistic
is
proportion
,
then
we
have
four
choices
for
our
sigma
value
.
I
'm
going
to
choose
Laney
P
'.
But
first
,
let
's
take
a
look
here
.
We
see
we
have
two
points
out
of
control
on
this
P
chart,
which
is
indicating
to
us
that
this
is
not
a
stable
process
.
We
can
certainly
turn
on
the
limits
,
and
that
just
affirms
what
we
spotted
.
If
we
change
the
Laney
P
'
chart
,
that
sigma
sub
Z
was
clearly
greater
than
1
,
because
now
our
limits
have
jumped
up
considerably
.
While
the
points
being
plotted
are
the
same
as
they
were
on
the
P
chart
,
the
upper
limit
in
particular
has
gotten
larger
.
There
's
a
fairly
simple
example
.
Let
's
move
on
to
another
example
.
This
is
again
,
another
data
set
out
of
this
JMP
sample
data
.
In
this
case
,
we
're
looking
at the
number
of
defects
out
of
...
We
have
several
units
that
are
being
tested
,
several
braces
that
are
being
tested
in
each
unit
.
Each
unit
isn
't
the
same
size
.
This
is
another
scenario
where
this
problem
with
the
non
-constant
variance
pops
up.
We
can
count
the
number
of
defects
.
We
're
not
counting
the
number
of
defectives.
We
're
counting
the
number
of
defects
per
unit
.
In
this
case
,
our
unit
size
is
varying
.
Let
's
choose
a
U
chart
under
the
Control
Chart
menu.
We
have
our
number
of
defects
,
and
we
have
our
date
as
our
subgroup
identifier
,
and
we
have
our
unit
size
as
our
n Trials
.
We
talked
about
having
that
non
-constant
unit
size
,
so
we
have
varying
limits.
We
do
see
some
of
the
points
are
out
of
control
here
.
Again
,
this
appears
to
be
a
non
-stable
process,
which
is
cause
for
panic
or
having
to
readjust
everything
.
Now
,
if
we
show
the
control
panel
,
since
we
have
proportion
as
a
statistic
,
we
can
change
this
from
a
Poisson
or
CNU
to
a
Laney
.
Again
,
the
limits
have
now
increased
.
We
now
realize
we
have
a
stable
process
.
We
don
't
have
any
points
out
of
control
.
This
is
better
characterizing
our
scenario
,
our
data
here
.
Now
,
let
me
show
you
a
third
example
,
which
is
a
little
bit
more
complex
.
The
picture
in
my
background
here
is
actually
a
picture
I
took
from
Acadia
National
Park
.
A
couple
of
summers
ago
,
I
was
working
at
the
Schoodic
Point
and
volunteering
to
study
microplastics
in
the
water
up
there
.
We
were
taking
samples
of
one
liter
of
water
per
week
at
different
locations
.
We
would
take
that
water
,
and
we
would
pour
it
through
a
filter
,
and
then
we
take
the
filter
and
look
under
a
microscope
and
physically
count
the
number
of
micro
beads
and
microfibers
that
were
appearing
on
the
filter
that
we
poured
the
water
through
.
Let
's
analyze
this
as
a
control
chart
.
The
principal
investigator
for
this
particular
study
is
trying
to
form
a
nice
,
complex
model
over
time
to
find
out
whether
or
not
the
water
plastics
were
increasing
.
But
in
order
to
do
that
,
you
really
want
to
know
that
you
're
modeling
your
data
correctly
and
whether
or
not
we
have
actual
stable
data
.
This
is
an
example
where we
could
run
a
P
chart
,
so
let
's
try
that
.
Being
the
control
chart
developer
,
I
'm
always
looking
for
more
opportunities
to
model
control
charts
.
We
want
to
look
at
the
total
plastics.
For
our
subgroup
,
we
have
week
,
and
for
our
n Trials
,
we
have
total
volume
,
and
then
we
have
different
site
IDs
as
a
phase
.
Here
we
see
we
have
three
different
points
that
we
were
taking
the
measurements
from
.
Some
were
better
than
others
,
but
they
all
seemed
to
be
really
out
of
control
.
He
's
going
back
to
the
drawing
board
and
like
,
"What
should
I
do
?
Should
I
take
more
data
?
How
do
I
..."
I
thought
,
"Hmm
,
let
's
see
if
this
really
is
characterizing
the
situation
,
the
process
here
very
well.
"
Let
's
run
this
again
through
a
dialog
,
and
you
see
there
are
two
new
entries
on
the
menu
,
one
for
Laney
P
'
and
one
for
Laney
U
control
chart
.
Again
,
let
's
look
at
our
total
plastic
as
the
Y
,
and
the
total
volume
is
our
n Trials
.
Our
week
is
our
subgroup
,
and
the
site
ID
is
the
phase
.
We
've
got
the
same
points
again
,
but
suddenly,
our
limits
are
much
wider
.
We
can
conclude
from
this
that
,
well
,
we
actually
have
a
pretty
stable
process
.
This
is
good
data
.
He
can
go
ahead
and
start
to
form
his
larger
model
on
this
data
.
While
it
's
a
calming
thing
,
also,
it
makes
a
lot
more
sense
.
In
closing
,
I
would
like
to
recommend
that
everyone
use
the
Laney
P
'
and
U
'
control
charts
when
monitoring
your
proportion
of
non
-conforming
or
defects
,
especially
when
you
see
a
difference
between
the
P
and
the
P
'
chart
.
Thank
you
very
much
.
