Hi,
I'm
Annie
Dudley
Zengi,
the
developer
for
control
charts
in
JMP
.
In
version
17,
I
added
the
Laney
P'
Prime
and
U'
Prime
Control
Charts.
Just
a
little
reminder
to
everybody,
the
control
charts
are
intended
to
show
the
stability
of
your
process,
how
well
you
can
reliably
make
the
same
part
of
the
same
size
over
and
over
again
over
time.
Let's
think
about,
in
order
to
do
that,
you
need
to
have
a
constant
variance.
An
attribute
chart,
either
Binomial
or
Poisson,
assume
a
constant
variance.
But
what
happens
if
your
variance
is
not
constant?
What
happens
if
the
temperature
changes
or
the
humidity
level
changes,
or
the
voltage
changes,
or
all
kinds
of
little
things
change
in
your
process,
and
you
can't
rely
on
the
variance
being
constant
over
time?
This
is
called
over
dispersion,
or
in
rare
instances,
under
dispersion.
A
one
parameter
distribution,
the
Binomial
and/
or
the
Poisson
can
model
over
dispersion
or
under
dispersion.
Now,
Laney
recognizes
this
and
proposed
that
we
normalize
the
data,
account
for
varying
subgroup
sizes,
and
then
compute
a
moving
range.
One
thing
to
keep
in
mind
with
Laney
control
charts
is
the
over
dispersion
and
under
dispersion
happen
typically
when
you
have
either
large
lot
sizes
or
you
have
unequal
lot
sizes.
It
causes
the
traditional
attribute
charts
to
incorrectly
characterize
your
process.
Let's
take
a
look
at
a
sample
data
that
has,
although
these
lot
sizes
are
not
terribly
large,
it
does
have
some
that
are
unequal
in
size.
We'll
open
up
washers
and
go
to
Control
Chart
Builder.
We're
going
to
model
our
number
of
parts
defective.
I
will
change
my
chart
type
to
attribute
because
this
is
only
available
in
the
attribute
control
charts.
I'll
pull
in
my
lot
size
and
the
lot.
Then
recall
that
lot
size
2
is
the
varying
lot
size.
I'm
going
to
pull
this
down
into
the
lot
size
grouping
here.
Now,
we
notice
we
have
varying
control
limits
and
we
have
a
couple
of
points
out
of
control.
I'm
going
to
turn
on
our
test
beyond
limits.
We
see
we
have
two
points
out
of
control.
Now,
we're
looking
at
a
Poisson
chart.
Also
understand
that
Laney's
P'
Prime
and
U'
Prime
control
charts
only
apply
when
the
statistic
is
a
proportion
.
Next,
we
need
to
change
our
statistic
to
a
proportion.
Again,
we
notice
that
those
same
two
points
are
beyond
the
limits.
Now,
if
we
change
our
Sigma
so
that
we
can
choose
a
Laney
P'
Prime,
we
notice
that
now
that
we
change
it
to
proportion.
We
have
four
options
here.
Not
only
just
the
regular
binomial
and
Poisson,
we
also
have
the
Laney
P'
Prime
and
Laney
U'
Prime.
Now,
we're
looking
at
a
U'
chart
right
now
of
number
of
defective, so
I'm
going
to
change
it
to
a
Laney
U' Prime
just
for
consistency
.
You
notice
now
those
two
points
that
were
out
of
control
are
now
in
control.
It's
not
indicating
that
our
process
is
unstable
at
this
point.
We'll
take
a
little
look
at
the
formulas.
There's
our
standardized
values.
We
compute
a
moving
range
of
those
standardized
values.
We
sum
them
up
and
then
we
compute
an
average
.
That
Sigma
sub
Z
is
what
is
inserted
into
the
formula
for
both
the
P' Prime
limits
and
the
U' Prime
limits.
These
look
just
like
the
formulas
for
the
U'
and
the
P'
charts,
only
with
that
Sigma
sub
Z
in
there
.
That
Sigma
sub
Z
approaches
one
as
over
dispersion
goes
away
.
Keep
in
mind
that
U' Prime
and
P' Prime
charts
will
match
your
U'
and
P'
charts,
respectively,
if
there
is
no
over
dispersion,
and
that
this
is
available
when
the
chart
type
is
attribute
chart
and
the
statistic
is
proportion.
Thank
you
very
much
for
your
time.
I
hope
you
found
this
talk
helpful,
and
feel
free
to
reach
out
to
me
with
any
questions.
Thank
you.
