Live
from
100
SAS
Campus
Drive, your
one
and
only
SAS
world
headquarters
in
Cary,
North
Carolina.
It's
The
Sampling
Plan
Show
starring
Stan
Koprowski.
Now
here,
he
is
the
host
of
The
Sampling
Plan
Show
,
Stan
Koprowski.
Thank
you.
Thank
you
very
much.
You're
too
kind.
Thank
you.
We
love
you.
No, that's enough.
Please.
All
right,
we
have
a
great
show
for
you
today.
Before
we
get
started,
just
a
little
background
here.
I
wasn't
very
good
at
statistics.
In
fact,
I
got
a
paper
cut
from
my
statistics
homework.
What
are
the
odds?
Enough
of
that.
Today
we're
going
to
talk
about
acceptance
sampling
and
sampling
plans.
We're
going
to
learn
about
the
OC
curve
and
what
it
is.
I'll
be
honest,
this
is
one
of
my
all
time
favorite
ANSI
standards.
We're
going
to
hear
about
the
ANSI
standards.
We'll
make
some
predictions
for
the
big
sampling
plan,
and
then
finally,
we'll
show
you
some
fantastic
highlights
using
the
JMP
sampling
plan
added
with
one
of
my
all
time
favorite
industrial
statisticians,
Dr.
José Ramirez.
I'm
glad
to
be
here.
Proud
of this
show.
I
think
it's
going
to
be
fun.
S
orry
to
hear
about
statistics
being
hazardous
to
your
health
with
a
paper
cut.
Man.
It
was
a
rough
journey
there,
but
we'll
get
through
it.
As
you
see,
here's
the
title
of
our
talk,
and
then
I
will
play
some
other
slides
here
for
you.
Your
book,
I
like
this
book.
We
use
this
a
lot
in
the
division.
The
Statistical
Quality
Control
book:
The
JMP
Companion
.
This
is
the
companion
book
to
Doug
Montgomery's
book.
I
think
you're
going
to
mention
Doug's
book
later
in
the
talk
as
well.
That's
true.
Then
I
have
your
other
book
up
here
too,
Analyzing
and
Interpreting
Continuous
Data
Using
JMP
.
I
know
you've
been
a
long
time
user
of
JMP,
probably
back
since
JMP
two.
I
think
you
were
probably
one
of
the
first
support
cases
that
came
into
tech
support
with
their
own
director
of
customer
enablement,
Jeff
Perkinson.
Jeff,
as
we
all
know,
started
out
in
tech
support
and
I'll
have
to
look
back
through
the
cases.
But
what
I
understand,
you
were
a
long
time
user
of
JMP.
Welcome
to
our
show.
I'm
glad
you
could
be
with
us
today.
Super
excited
to
have
you.
We're
going
to
talk
about
some
options
here
for
the
folks
to
call
in.
If
you
want
to
call
into
the
show
or
message, you can
message us
at
JMP L
ot
Plan.
The
phone
number,
if
you
have
a
rotary
dial
phone,
you
can
call
us
up
there,
or
if
you
want
to
reach
us
on
Instagram
at
JMP
Lot
Acceptance
Sampling
Plan.
Let's
go
ahead
and
see.
I
think
I
do
hear
a
call
again.
Wait
a
minute. Let me
see
who
that
is.
Yes,
this
is
Dr.
Julian
Parris
calling
in.
I'm
the
Director
of
JMP User
Acquisition
and
I
have
a
question
for
Dr.
Ramirez.
Go
ahead,
Julian.
Dr.
Ramirez,
can
you
explain
the
difference
between
lot
acceptance
sampling
plans
and
variables
acceptance
sampling
plans?
I
don't
really
believe
that
was
Julian.
That
was
probably
Julio
from
down
by
the
school
yard. Julio,
do
you
have
a
question?
Yes,
he
did.
He
was
asking
if
you
could
give
us
a
little
introduction
to
sampling
plans.
Sampling,
okay
.
He's
doing
some
sampling
by
the
schoolyard.
I
wonder
what
he's
doing
there.
Julio,
since
you're
in
a
school,
you're
by
the
schoolyard,
let's
go
back
to
the
dictionary
and
look
at
sampling,
what
the
dictionary
says.
There
are
a
few
definitions
here
in
the
dictionary,
and
one
thing
I
like
about
the
first
one
it
talks
about
a
suitable
sample.
For
statisticians
that
has
the
meaning,
part
of
that
is
there,
the
sample
has
to
be
representative.
For
those
of
you
familiar
with
stats,
the
other
piece
that
we
also
include
in
a
definition
of
suitable
sample
is
random.
But
what
you're
talking
about,
about
these
sampling
plans
is
the
second
definition,
and
they
got
it
right
because
a
sampling
plan
is
essentially
deciding
on a
small
portion
of
a
lot
or
a
population.
The
population
can
be
either
infinite
or
it
can
be
a
finite
number
like
10,000
items.
We
want
to
take
a
small
sample
from
that
so
we
can
do
some
inspection.
By
inspection,
we
mean
that
we
are
going
to
decide
the
fate
of
that
population
or
the
fate
of
that
lot.
Okay,
I
understand
a
little
bit
of
what
you're
saying
there.
You're
going
to
take
a
sample
from
a
population
and
then
what's
the
difference
between
the
sample
types?
It
looks
like
you
were
talking
here
about
an
inspection.
Is
it
always
an
inspection
or
are
there
other
types
of
sampling
that
you
can
do?
Well,
in
general,
when
people
think
about
lot
acceptance
sampling
plans,
there's
some
type
of
inspection,
some
type
of
checking
that
goes
on.
T
he
way
we're
going
to
do
this
sampling
is
we're
going
to
apply
some
statistical
principles
[crosstalk 00:06:22].
Oh, gosh, that's scary.
But
actually
that's
what
the
agencies
want.
For
example,
if
you
look
at
some
of
the
documents
from
the
FDA,
if
you're
required
to
do
some
type
of
sampling,
they
want
you
to
do
it
in
a
statistical
way.
How
are
we
going
to
do
that?
That's
part
of
this
show
that
we're
having.
But
in
the
old
days,
and
we're
going
to
talk
about
those
old
days,
people
used
standards,
and
those
are
the
standards
that
are
used
right
now.
The
ASQ/ANSI
Z1.4
and
Z
1.9.
That
sounds
even
scarier,
but
I'm
a
bit
confused.
I
thought
when
I
was
doing
my
research
before
I
brought
you
on
as
a
guest,
that
there
were
some
military
standards.
I
thought
the
sampling
plans
were
based
on
military
standards
and
now
you
just
told
me
there's
some
other
standards
in
play
here.
W
hat's
the
story
with
this?
Well,
yeah,
that's
true.
These
sampling
plans
have
a
long
history.
They
go
back
to
probably
the
1930s,
1940s,
and
there
were
two
military
standards,
the
105
E,
which
is
the
one
that
corresponds
to
the
Z
1.4,
and
that's
what
you're
doing
using
a
technical
term,
sampling
by
attribute,
and
then
there
is
the
military
standard,
414,
which
is
the
Z
1.9
that
corresponds
to
sampling
by
variables.
You
can
think
of
discrete
versus
continuous.
What
happens
is
that
those
standards
were
taken
over
now
by
the
American
Society
for
Quality,
that's ASQ,
and
the
American
National
Standard
Institute,
the
ANSI,
and
they
rebranded
those,
and
now
they're
called
Z
1.4
and
Z
1.9.
But
they're
still
the
same
standard,
the
same
table
that
people
use
to
generate
sampling
plan.
L
et's
talk
about
how
we
do
that.
The
way
to
understand
this
is
that
every
type
of
lot
acceptance
sampling
plan,
and
that
is
the
L-A-S-P
or
LASP,
has
components
and
risks.
T
he
components
of
the
plan
essentially
is
how
many
samples
do
I
need
to
take
from
a
population
of
10,000
or
infinity
that
I'm
going
to
test?
I'm
going
to
do
some
inspection,
and
I
want
to
know
if
in
that
sample,
there
is
a
pre-
specified
percent
defective.
We're
going
to
define
quality
as
a
percent
defective
in
the
population.
What
we're
doing
is
taking
a
sample
to
see
if
that
sample
contains
that
or
less.
In
order
to
determine
if
we're
going
to
pass
or
fail
that
lot,
we
also
relied
on
the
acceptance
number,
which
tells
us
how
many
defective
parts
out
of
the
sample
we
can
accept
in
the
plan.
Anything
that
is
greater
than
that,
it's
going
to
say
we're
going
to
reject
or
fail
the
lot.
That's
why,
as
I
said
before,
we're
determining
the
fate
of
the
lot
with
this
[inaudible 00:10:03]
.
Here
we
go.
I
had
my
fate
a
long
time
ago
with
that
paper
cut.
I'm
a
little
anxious
here.
What
kind
of
fate
are
we
talking
about?
Yeah,
we're
going
to
decide
if
we
are
going
to
pass
the
lot
and
make
it
available
for
whatever
purpose
that
lot
is
being
manufactured,
or
we're
going
to
put
it
in
quarantine
[inaudible 00:10:31]
and
maybe
do
some
more
inspections
or
trying
to
understand
why
the
fraction
defective
is
larger
than
the
prespecified
one.
You
mentioned
that
there's
some
risk
involved.
What
kind
of
risk?
Is
it
out
of
my
control
or
is
it
within
my
control?
Whose
risk
is
this?
Yeah,
there's
risk,
and
that's
the
beauty
about
my
profession.
In
statistics,
you
don't
have
to
be
certain
about
anything.
I
can
95%
confident.
I
can
even
go
up
to
99%
confident,
I
don't
have
to
be
certain.
Again,
for
those
of
you
familiar
with
statistics,
you
know
what
we're
talking
about.
There's
the
chance
that
there's
a
perfectly
good
lot
that
we're
going
to
reject
because,
again,
we're
taking
a
small
sample,
so
we're
not
sampling
the
whole
population.
There's
a
risk
associated
that
this
sample
may
determine
that
a
good
lot
is
not
good
or
there's
a
chance
that
a
bad
lot
may
be
released.
Y
ou
can
think
in
terms
of
false
positive.
Y
ou
think
in
terms
of
a
medical
test.
That's
similar
to
like
if
you
were
to
take
the
COVID-19
test.
If
you
actually
had
it
and
it
came
back
negative
,
is
that
a
false
positive?
Yeah,
it's
like
that,
a
false
positive.
We
may
have
a
false
positive
that
means
this
lot
is
bad,
but
the
sampling
plan
that
it
was
good.
Or
vice
versa,
we
have
a
good
lot
and
we're
going
to
reject
it
because
the
sampling
[crosstalk 00:12:19]
.
That's
why
it's
important
to
use
a
statistical
principle
in
designing
these
sampling
plans.
That's
part
of
why
people
use
standards.
Those
were
derived
in
a
way
that
when
you
select
a
plan
from
those
standards,
these
risks
are
going
to
be
balanced.
That's
what
we
talk
about
the
generation
of
the
lot
acceptance
sampling
plan.
We
have
these
two
risks.
Again,
we
can
say
that
a
good
lot
is
going
to
be
rejected
and
there's
also
a
chance
that
a
bad
lot
may
be
released
like
the
false
positive.
We're
going
to
assign
some
probabilities
to
this
risk.
One
thing
we
want
to
make
sure
is
that
if
it's
good,
we
want
to
accept
that
most
of
the
time.
How
do
we
define
that?
Well,
standard
practice
is
used
95%.
A
gain
that
sounds
like
95%
confidence
when
you
use
some
type
of
statistical
test.
What
we're
saying
is
we're going
to
pre
define
a
fraction
defective
for
that
lot
and
we
are
going
to
select
a
plan
that
guarantees
that
or
almost
guarantees
that
95%
of
the
time,
a
good
lot
is
going
to
be
accepted. A
good
lot
is
going
to
pass.
On
the
other
hand,
we're
going
to
also
say
that
we're
going
to
have
a
high
chance
of
rejecting
a
bad
lot.
Or
if
you
flip
that,
you
can
say
there's
a
small
chance
of
passing
a
bad
lot.
90%
here
transforms
itself
to
a
10%.
We're
saying
there's
a
95%
of
accepting
a
good
lot,
but
only
a
10%
chance
of
accepting
a
bad
lot.
Those
are
standard
numbers
in
sampling
plans,
in
the
standards
and
the
way
people
use
those
sampling
plans.
You
were
talking
about
the
user.
The
user
has
the
option
of
changing
those
numbers.
Rather
than
use
95%,
we
can
use
99%.
Rather
than
using
10%,
we
can
use
5%.
However,
if
we
get
too
greedy,
then
as
some
of
you
may
know,
the
sample
size
is
going
to
increase
and
that's
part
of
that
balance.
We
want
to
find
a
sample
size
that
is
small
enough
that
it's
going
to
guarantee
these
probabilities.
Should
we
do
some
examples?
Are
there
things
we
could
do
to
explain
some
of
the
balance
that
you're
talking
about
between
these
two
competing
risks?
Yes.
It
sounds
like
one
might
be
on
the
consumer
side,
and
maybe
one
of
the
risks
is
on
the
producer's
side.
Exactly.
The
first
chance
of
...
If
you
look
at
the
95%
chance,
that
means
there's
a
5%
chance
that
a
good
lot
is
going
to
be
rejected.
That's
on
the
producer
side.
Because
as
a
producer,
you
don't
want
your
good
lot
to
be
rejected.
The
loss
of
material
or
good
product.
On
the
other
hand,
the
10%
chance
of
accepting
a
bad
lot,
that's
on
the
consumers
because
we
don't
want
the
consumers
to
receive
something
that
is
bad.
Now,
in
order
to
do
this,
there's
this
tool
that
we
use
in
sampling
plans
which
is
called
the
operating
characteristic
curve
or
OC
curve.
Some
people
may
be
familiar
with
that.
Those
curves,
I'm
going
to
show
you
some
examples,
are
the
ones
that
are
used
to
find
that
balance
between
this
risk
and
probability.
Risk
and
probability.
What
is
an
OC
curve?
An OC
curve
is
essentially
a
plot
that
shows
you
the
probability
of
accepting
a
lot
as
a
function
of
that
fraction
defective
in
the
population.
You
look
at
these
two
curves,
the
blue
line
here
on
the
Y-
axis,
we
have
the
probability
of
accepting
the
lot,
and
the
X-
axis,
we
have
the
proportion
defective
in
the
population.
As
you
can
see,
when
the
proportion
defective
is
very
small,
there's
a
high
probability
of
accepting
the
lot.
A
s
soon
as
that
probability
starts
increasing,
then
the
probability
of
accepting
that
lot
decreases,
and
that's
the
shape
that
we
want
to
see
in
an
OC
curve.
Now,
remember
we
have
two
probabilities
and
two
risks.
We
want
a
95%
chance
of
accepting
a
good
lot
and
a
10%
chance
of
accepting
a
bad
lot.
Now,
the
definition
of
good
and
bad
is
in
terms
of
that
proportion
defective
in
the
population.
We
as
users
of
sampling
plans,
we
have
to
define
what
is
a
good
fraction
defective.
Of
course,
ideally,
the
good
fraction
defective
should
be
zero,
but
that
will
throw
a
[inaudible 00:17:22]
in
the
math.
If
you
know
that
zero
divide
by
zero,
you
get
infinity,
meaning
you
have
to
sample
everything
if
you
want
perfection.
What
we
do
is
define
a
small
number,
and
that
is
called
the
AQL,
the
acceptable
quality
level.
On
the
other
side,
we
define
the
RQL
or
rejectable
quality
level.
Granted,
there
are
many
terms
that
people
use.
Sometimes
you
may
see
the
LTPD
instead
of
RQL.
That's
the
lot
tolerance
percent
defective,
the
maximum
percent
defective
that
you
can
accept
in
your
population.
With
the
probabilities
and
those
fraction
defectives,
you
define
two
points
in
this
curve.
H
ere,
the
AQL
is
1.8%,
and
we
have
a
95%
probability
of
accepting
that.
What
that
means
is,
as
long
as
the
fraction
defective
in
the
lot
is
less
than
2%,
let's
say,
then
there's
a
high
chance
of
accepting
that
lot.
But
as
long
as
it
goes
higher,
like
2%,
then
we're
going
to
accept
that
lot
very
infrequently.
These
two
points
are
the
ones
that
you
look
at
in
the
OC
curve,
and
those
are
the
ones
that
are
going
to
determine
the
sample
size
and
the
acceptance
criteria.
Got
it.
I
think
it
will
help
Julio
if
we
run
some
example.
I
think
Julio
is
texting
and
he
said
that
would
be
great
if
we
could
do
an
example.
Okay,
let's
do
that.
Why
did
you
develop
or
why
did
we
produce
a
JMP
add-in?
Yes,
Julio,
what
we're
going
to
do
is
we're
going
to
show
you
an
app
to
do
this.
People
sometimes
ask
us,
"Why
did
you
do
this?
Why
do
you
spend
a
lot
of
time
for
writing
code
and
package
it
in
an
add-
in?"
Well,
to
tell
you
the
truth,
[crosstalk 00:19:30]
.
That
is
really
tiny.
I'm
going
to
have
to
get
a
new
set
of
readers
or
a
giant
magnifying
glass.
Is
that
really
how
people
do
this?
Now
it may
seem
weird,
but
still,
I
believe
in
some
industry,
people
may
use
a
standard.
Granted,
it may
not
be
the
old
book
that
they
use,
maybe
a
pdf,
but
sometimes
they
still
use
this.
But
these
are
very
tedious
and
they're
discreet
in
the
sense
that
they're
just
approximations
to
the
plans.
T
here's
a
process
to
do
that.
Of course, there
are
multiple
tables
that
you
have
to
go
through
in
order
to
find
the
appropriate
sample
size.
W
hat
we
wanted
to
do
is
make
our
life
easier,
actually,
because
to
be
truthful
here,
we
use
sampling
plans.
We're
also
tired
of
using
these
tables.
So
we
wanted
to
automate
the
generation
of
the
LASPs.
Okay,
got
it.
What
else
do
we
need
to
know
before
we
do
some
examples?
This
is
JMP,
which
is
one
of
the
greatest
pieces
of
software
out
there
for
doing
statistics
and
getting
insights
out
of
your
data.
A
nother
thing
that
we
did
is
that
not
only
we're
automating
the
generation
of
the
plans,
making
life
easier,
but
we're
using
all
the
visualization
tools
in
JMP,
like
the
profilers,
to
understand
these
OC
curves.
Remember,
we
just
showed
you
that
the
way
we
determine
or
generate
the
plans
is
via
an
OC curve.
The
OC curve
is
also
very
important
to
evaluate
plan.
How
do
we
know?
If
someone
gives
us
a
sampling
plan,
how
do
we
know
if
that's
good?
T
hat's
part
of
this.
Let's
look
at
an
example.
T
his
comes
from
Professor
Montgomery's
book.
A
gain,
here a
shameless
PR
for
us.
We
wrote
a
companion
book.
You
saw
that
book
at
the
beginning
for
this.
It's
called
JMP
Base
.
I
f
you
look
at
this
figure,
this
is
in
chapter
15, P
rofessor
Montgomery
also
shows
another
approximation.
It's
another
way
of
generating
sampling
plan,
which
is
using
a
nomo
graph.
A
nomo
graph
is
this
figure
that
you
see
here.
Here,
you
have
to
figure
out
what
your
AQL
and
RQL
are,
the
probabilities,
and
that
you
have
to
go
in
there
and
approximate
that.
Here,
they
give
you
an
example
where
they
say
the
acceptable
quality
level
is
2%
or 0.02
,
the
rejectable
quality
level
is
8%
or 0.08
.
The
probability
of
accepting
a
lot
that
has
an
AQL
of
2%
of
less
is
0.
95.
As
we
say,
that's
high.
T
he
probability
of
accepting
a
lot
that
has
RQL
of
8%
or
more
is
only
10%.
A
gain,
these
0.
95
and
0.1
are
our
standard
value
in
industry.
Y
ou
notice
this
diagram
here,
they
put
the
0.02,
they
draw
a
line
here
to
intersect
the
0.
95.
The
0.08
intersects
at
that,
and
then
you
intersect
those
two
lines
and
you
guess
at
that.
Okay,
that's
a
93
plan.
Meaning,
out
of
an
infinite
population,
you
take
90
samples,
you
inspect
them
all,
and
you
can
accept
up
to
three
defectives.
If
you
see
more
than
three
defectives,
you
reject
the
lot.
If
you
see
three
or
less,
then
you
accept
the
lot.
T
hat's
sampling
plan.
A
little
cumbersome
too
.
Yeah,
it
looks
a
little
difficult
to
line
up
exactly.
I
hope
we
could
get
away
from
doing
that
with
the
add-in.
Well,
let's
show
Julio
how
we
can
do
that
with
JMP.
Okay,
sounds
like
a
good
idea.
All
right,
Julio,
let's
jump
over
to
some
examples
here.
H
ere
is JMP
and
to
get
to
the
add-in,
you
just
go
to
your
add-in,
JMP
Sampling
Plans,
and
let's
look
at
attribute
sampling
plans.
T
hen
this
particular
one,
we're
going
to
do
just
a
single
lot
acceptance
sampling
plan.
W
e
have
the
menu.
After
we
make
that
choice,
it
comes
up
and
it
gives
us
three
options.
We
can
evaluate
an
attributes
plan,
we
can
generate
or
create
an
attributes
plan,
or
we
can
compare
plans.
The
add-in
gives
us
the
ability
to
compare
up
to
five
different
plans.
There's
an
option
to
keep
the
dialogue
open
in
case
you
want
to
look
at
more
than
one
plan.
L
et's
go
ahead
and
generate
that
plan
that
you
were
just
sharing
here
from
Dr.
Montgomery's book.
I'll
give
you
the
numbers.
Let's
see.
All
right.
This
is
the
interface. [inaudible 00:24:51] AQL.
Right. Y
ou
have
the
couple
of
different
sections
in
the
interface.
You
get
to
put
in
your
quality
levels,
your
probabilities,
and
then
you
have
the
optional
area
about
the
type
of
lot
sampling
you're
going
to
do.
T
hat
lot
sampling
is
based
on
a
distribution.
S
o
we
could
either
do
a
hypergeometric
distribution
or
a
binomial
distribution.
Lets put Montgomery's
numbers
there.
The
AQL in
Professor
Montgomery's
book
is
0.02
or
2%.
The
RQL
he
has
is
0.08.
All
right.
Oh,
they're
pre-
populated.
Yes,
those
are
the
standard
values, 0.95.
The
standard
values.
So
we
keep
the
default
there.
I
think
you
told
me
there
was
no
theoretical
lot
size
here.
So
we're
going
to
do
type
B.
Actually,
in
Montgomery's book,
it
says
that
he's
using
a
binomial
nomograph,
so
it's
meaning
he's
using
the
binomial
distribution.
So
yes,
that's
the
right
choice.
Then
all
we
have
to
do
is
hit
okay,
and
then
what
happens?
JMP
gives
us
this
curve
here
and
an
output
window
and
a
report.
T
here's
three
sections
to
the
report.
You
get
a
little
at
the
top
of
summary,
you
get
a
summary
of
the
plan.
I
t
shows
you
your
input
parameters
and
then
what
the
plan
generated
as
a
sample
size
and
acceptance
number.
T
hen
it
gives
you
some
information
on
how
to
interpret
the
plan.
That was helpful.
O
ut
of
the
plan
recommendation
here,
of
the
98
samples,
it
says
you
can
accept
the
lot
here
as
long
as
the
number
of
defectives
is
less
than
or
equal
to
four
out
of
that
98
sampled.
O
therwise,
you
reject
it
if
it's
greater
than
that.
T
hen
it
tells
you
some
additional
information
there.
I
f
we
look
at
that
OC
curve,
I
think
this
is
what
you
were
showing
us
earlier.
Yes.
So
we
have
a
probability
at
95%
and
the
quality
level
of
2%.
But
if
I
recall,
you
told
me
in
the
literature
that
this
was
a
plan
of
90 and 3.
So
I'm
confused
again
here
or, should I say, Julio texted
me
and
said
he's
confused.
How
is
it
different
here? Or
why
is
this
different?
We're
getting
98
and
4.
Professor
Montgomery,
in
his
book,
he's
showing
the
nomo
graph
and
he's
getting
90 and 3.
Let
me
go
back
there
just
1
second.
R
emember,
what
we're
using
is
this
graph
here
where
you
have
all
these
approximations.
You
have
a
line
that
goes
200,
300,
you
go
between
70 and 100.
Got
it.
There's
not
really
a
93
or
94,
anything
like
that.
A
s
I
say,
you
had
to
approximate
these.
T
his
nomog
raph
is
just
an
approximation.
What
we're
actually
trying
to
do
is
solve
these
equations
for
those
of
you
mathematically
inclined.
The
one
minus
alpha
is
the 0.
95,
the
beta
is
the 0.10
,
p1
is
the
AQL
and
p2
is
the
RQL.
I
f
we
put
all
those
four
things
in
here
and
we
solve
these
equations
to
try
to
find
the
minimum
n
that
satisfied
those
four
things. W
hen
you
do
that,
actually, software
does
that
for
us,
the
code
that
we
wrote,
we
get
the
98
and
4.
I
get
that.
The
moral
of
the
story
here
is
that
the
nomograph
gives
approximate
lot
acceptance
sampling
plan
again,
because
it's
just
an
approximation
game.
T
his
is
again
one
of
the
advantages
of
using
the
add-in,
because
you
get-
Sampling plan add-in,
of
course.
-more
exact
sampling
plan.
Y
ou
show
that
we
can
evaluate
a
sampling
plan.
L
et's
do
that.
Why
don't
we
evaluate
this
plan,
the
93?
Show us ho
w
to
do
that.
Since
I
left
that
window
open,
we
don't
have
to
go
back
to
the
menu
again.
L
et's
evaluate
a
plan
now.
W
e
have
in
that
interface,
again,
we're
going
to
put
in
our
previous
0.02, 2%,
and
I
think
he
told
me
the
RQL
was
8%.
Yes.
This
time,
though,
instead
of
a
sample
size
of
20,
we're
going
to
do
90
and
evaluate
three.
Again,
it's
a
binomial.
Here,
you're
entering
the
four
quantities
that
we
talked
about
in
the
generation,
but
you
also
enter
the
actual
sampling
plan
that
you
want
to
evaluate.
Exactly.
Now
when
I
say,
okay,
it's
going
to
look...
Sorry
about
that. I'll j
ust
redo
that
quick.
[inaudible 00:30:31],
no?
Yes,
sorry.
I
want
to
evaluate
that plan,
you get 90 and 3.
Three, yes.
When
I
rerun
that,
we
get
the
exact
same
style
of
report,
but
the
information
is
slightly
different,
I
see
here.
I
noticed
also
down
in
this
table,
there's
some
color
coding
and
direction
of
the
arrows.
Yeah,
I see some
red
there.
I
see some
red,
yes.
That's
an
issue.
Is
this
an
indication
that
the
specified
quality
level
is
better?
Actually,
no.
I
think
that
the
reason
that
it's
red
is
because
what
happens
here
is
what
this
is
telling
us.
If
I
use
a
sample
size
90
with
an
acceptance
number
of
three,
you
can
see
the
associated
probability
of
acceptance
is
0.
89.
Remember,
we
wanted
that
to
be
0.
95.
So
it's
actually
lower.
It's lower and that's
why
that
is
red.
T
his
add-in
is
giving
you
a
signal
that
your
probability
of
acceptance
is
less
than
the
one
you
specify
and
that's
an
issue.
T
hat's
why
the
98 and 4
is
a
better
plan.
A
lso
you
can
see
at
the
bottom
that
the
probability
of
acceptance
for
defective
lot
is
6%
rather
than
10%.
I
n
that
case,
it's
blue
because
it's
better,
that
probability
is
better.
Y
ou're
going
to
have
a
smaller
consumer's
risk.
You
can
see
that
the
n
is
6.47
versus
the
10.
67.
T
hat
is
what's
happening.
That
producers
risk
is
really
just
the
difference
between
that
one
minus
the
associated
probability
of
acceptance
there?
Exactly.
Got
it.
Exactly.
T
he
producer's
risk,
we
want
it
to
be
5%,
and
in
this
case, it's
10%.
No
w
this
is
something
that
I
haven't
seen
in
any
other
software.
Normally,
what
you
see
is
the
plot
on
the
lot.
You
get
that
for
the
associated
AQL
and
all
that,
and
they
assume
that
the
probabilities
are
the
ones
that
you
specify,
but
they're
not.
W
hat
the
add-in
does
too,
is
it
flips
things
around
and
says,
okay,
if
rather
than
fixing
the
AQL
and
RQL,
I
fix
the
probabilities,
I
want
to
be
95
because
that's
what
it
is,
I'm
neurotic
that
way.
I
want
it
to
be
95
and
0.1.
Then
what
are
the
corresponding
AQL
and
RQL
for
that?
T
hat's
what
that's
telling
me.
Okay.
I
wasn't
really
just
seeing
double.
It's
really
a
different
calculation
on
the
right
hand
side. Got it.
What
that
says
is
if
the
probability
of
acceptance
peaks
at
0.
95,
then
the
AQL
is
not
2%
but
1.53%.
Okay, got it.
It
has
to
be
way
less
than
that.
So it's a... Okay,
go
ahead.
A
lso
at
0.1,
then
it's
not
8%
but
7%.
Okay.
7.3%,
roughly.
That's
why
both
cases,
they
are
blue.
They
are
blue.
A
gain,
just
very
quickly
here
to
show
this.
In
summary,
for
this
one
is
that,
again,
the
nomogram
gives
an
approximate
lot
acceptance
sampling
plan.
The
90
and 3
plan
shows
you
that
we're
not
hitting
that.
Are
you
sharing?
Yes,
I'm
sharing.
Okay.
Hopefully,
people
can
see
that.
Our
producers
risk
is
now
10%
versus
5%.
But
there
is
one
more
thing
that
you
had
there,
which
is
compare.
I'm
curious,
can
we
use
that?
I'll tell
you
tell
can
tell
you
what
I
want
to
do.
I
want
to
use
the
add-in
to
compare
the
planning
Professor
Montgomery's
book
of
'93
with
a
plan
that
the
add-in
gave
us,
which
is
98,
4
All
right.
W
e
did
mention
that
we
can
compare
up
to
five
different
plans.
Y
ou
want
me
to
compare
the
two
plans
that
we
just
created.
All
right. Again,
that
8%
RQL.
In
this
case,
we
had
a
90
and
an
acceptance
of
three.
Yeah.
Then
I
think
you
told
me
it
was
98
and
4.
That's
what
they
do.
I
didn't
tell
you.
That's what
the
add-in
gave
us.
Yeah.
Wow,
that,
that
is
pretty
slick.
A
gain,
if
you
wanted
to
do
more
than
two,
you
would
just
check
the
additional
rows
and
then
the
add-in
will
calculate
up
to
five
different
comparisons
here.
I'm
going
to
go
ahead
and
click
OKay.
N
ow
it
looks
a
little
bit
different,
Jose,
here.
Now
I'm
seeing
two
OC
curves.
I'm
getting
a
comparison
of
both
of
my
OC
curves
on
the
same
plot
I
see
here.
Exactly.
H
ere
the
blue
one
is
the
plan
93 and he red line is-
T
he
blue
is
the
93.
Yeah,
exactly.
I
see that.
The
red
line
is
the
98, 4
.
Y
ou
can
see
that
the
red
line or
curve,
is
on
top
of
the
blue
curve.
T
hat's
what
you
want
to
see.
You
want
the
curve
to
be
on
top,
literally.
T
his
shows
you
that
you
have
higher
probabilities
of
acceptance
according
to
the
parameters,
the
IAQL
and
RQL
that
we
prespecified
with
the
plan
98, 4
than
with
the
plan
93.
T
his
is
very
helpful
because
you
may
be
in
situations
where
you
may
get
an
approximate
plan
from
a
book
or
someone
may
suggest,
"Hey,
why
don't
you
use
this
plan?"
With
this
one,
you
can
compare
them
all
and
see
which
one
is
better.
I
t's
easier
to
negotiate
the
sample
size
using
these
tools
than
just
getting
into
an
argument
and
say,
"No,
we
should
use
93
because
that's
in
the
book
or
something
like
that.
"I
think
that's
a
great
feature
to
be
able
to
compare,
and
that
way
you
move
the
discussion
into
some
actual
information
and
rather
than
subjective,
individuals
can
now
compare
directly.
Ag
ain,
if
you
want
the
reference
lines,
it
didn't
make
sense
to
show
all
the
reference
lines
across
five
different
curves.
W
hat
we've
done
is
we
could
just
display
a
single
set
of
reference
lines
by
toggling
the
filter,
and
it
will
update
the
graphs
for
you
as
you
toggle
between
them.
That
way,
if
you
do
want
to
see
that
individually,
you
could
do
that,
and
then
you're
just
focused
in
on
the
table
and
the
graphs
for
that
particular
set
of
observations. That's,
I
think,
a
nice
feature,
Julio.
It
is.
All
right. It's time for one more.
[inaudible 00:38:32].
I
think
that
Julio
is
probably
getting
tired
there.
He
is
very
exhausted.
Julio, there's
some
other
things
that
the
add-in
can
do.
So
maybe
it should
be
a
follow
up
to
this.
Maybe
we
could
do
another
session
for
Discovery
Summit
Japan
or
Discovery
Summit
China.
We'll
continue
showing.
[inaudible 00:39:05].
I
think
I'm
getting
breaking
news
coming
across
here.
Just
into
the
news
center
here
of
the
show.
A
nyone,
it
looks
like,
can
get
that
sampling
plan
add-in.
If
you
just
go
to
the
JMP
user
community
and
search
for
the
sampling
plan
add-in,
you
could
download
it
yourself.
And that's free. No? That's free?
Absolutely
free.
We
would
never
charge
for
that
on
the
community.
G
o
ahead
and
download
that.
If
you
have
feedback
or
you
run
into
issues,
feel
free
to
message
me,
and
we'll
get
those
defects
entered
and
get
a
corrected
version
out
there
as
soon
as
we
can.
Other
things
just
before
we
wrap
up
here,
I
just
want
to
say
thank
you
to
Jose.
Thank
you
for
joining
us
on
the
JMP
sampling
plan
show
today.
It
was
great
to
have
you
here.
Really
great.
T
hank
you
again.
