Hello
,
my
name
is
Don
Lifke
,
and
I'm with
Sandia
National
Labs
,
I'll
be
presenting
on
Reliability
Platform
in
JMP
.
The
example
I'll be
using
has
to
do
with
jumping
into
retirement
.
Sit
back
and
enjoy
.
And
let's
see
where
this
leads
us
.
A
little
bit
about
Sandia.
We
are
,
federally
funded
research
government
owned,
contractor
operated
facility
.
I
've
been
at
Sandia
since
about
2005
,
roughly.
I'll
tell
you
a
little
bit
about
what
we
do at Sandia.
We
work
primarily
on
nuclear
deterrence
.
Six
major
programs
that
we're
working
on
right
now,
I
won't
go
into
the
details
of
these
,
but
all
of
these
programs
that
you
see
on
my
screen
are
programs
that
I
worked
on
and
have
actually
applied
JMP to
all
of
these
as
well
.
The
reason
I'm
presenting
data
on
retirement
is
because
a
lot
of
the
stuff
that
I
do
is
just
too
sensitive
and
I
can't
present
it
in
this
particular
environment
.
So,
we're
going
to
use
some
fun
data
that
everybody
can
relate
to
.
Naturally
,
this
is
a
recording
,
so
you
hold
your
questions
to
the
end
.
But
this
is
the
same
presentation
I'll
be
giving
live
at
the
conference
. L
et's
talk
about
retirement
.
Why
is
it something
that
is
so
certain
but
has
so
much
uncertainty
in
it
that
makes
it
a
little
bit
hard
to
plan
?
I'm
not
sure
I
would
want
to
be
without
that
uncertainty
.
I
prefer
a
little
uncertainty
in
that
.
It
does
make
planning
for
retirement
a
little
bit
difficult
.
But
I
guess
on
the
bright
side
,
if
I
were
a
cat
,
it
would
be
even
more
complicated.
By
the
way
,
that
the
GATO
here
is
a
repairable
system
,
and
that's
a
whole
new
topic
in
general
.
We're
just
going
to
be
doing
systems
that
are
not
repairable
,
like
cats
.
What
we
can
do
is
we
can
use
the
Reliability
and
Survival
tool
in JMP.
Some
of
the
screenshots
that
you
see
maybe
are from
an
older
version
of
JMP.
This
was
created
pre-COVID
.
Now
that
we
are
on
newer
versions
of
JMP there
might
be
some
slight
differences
in
what
you
see
and
what
I've
read
from
the
screenshots
,
but
should
be
fairly
similar
anyway
.
But
we're
going
to
be using
the
Reliability
and
Survival
and
the
Life
Distribution
and
Fit
Life
by X
in
JMP
to
do
some
analysis
of
some data. I'm not
sure
about
where data
is
coming
from
.
At
Sandia
,
we
have
a
Lab
News
that
comes
out,
every
couple
of
weeks
and
in
that
Lab
News
they
like
to
post
retiree
deaths
of
the
people
that
we
worked
with
for
a
long
time,
just
to
let
us
know
that
they've
moved
on
and
gone
to
a
big
R&D
facility
in
the
sky
.
So,
I
thought
I
could
use
that
data
to
help
me
plan
for
my
retirement
.
I
grabbed
some
of
that
data
and
I
took
data
from
four
different
periods
,
of Lab
News
archives
.
I
pulled
some
data
from
2001
,
2007
,
2013
and
2018
.
The
number
of
data
points
that
I
grabbed
from
each
of
those
is
here
.
We'll
look
at
some
of
these
data
and
see
how
things
are
changing
through
time
as
well
.
But
anyway
,
the
number
of
data
points
is
pretty
significant
.
This
little
clip
,
this
little
picture
on
the
right
is
a
little
trivia
there
.
If
you
look
real closely
at
the
violinist
on
the
left
,
some
of
you
might
recognize
him
or
let's
leave
that
up
to
you
to
figure
out
.
Maybe
I'll
sing
it
.
It's
actually
my
partner
,
Claire
.
She's
a
pharmacist
.
But
also
a
beautiful
opera
singer
.
T
he
nice
thing
about
using
the
retiree
death data
for
me
is
that
that
population
is
a
better
representation
of
my
lifestyle
.
They
tend
to
take
less
risks
,
lead
little
more
conservative
lifestyle
and
a
similar
income
and
education
level
,
of
course
,
where
the
best
and
the
brightest
are left at
the
probably
at
the
bottom
of
that
list
,
but
at
least
I'm
in
that
group
.
It's
an
honor
to
be
working
with
the
folks
at
Sandia
.
Another
nice
thing
about
using the
retiree death data
is,
it
only
includes
those
who
actually
made
it
to
retirement
.
I
don't
really
care
about
the
data
for
not
making
it to
retirement
.
And
I
apologize
to
my
kids
.
I'm
in
Albuquerque
,
New
Mexico
right
now
.
For
those
of
you
who
love
Breaking
Bad
and
know
that
Breaking
Bad
was
primarily
filmed
here
in
Albuquerque
,
and
so
I'm
a
big
fan
of
the
show
,
so
I've
got
to
put
a
little
bit
of
fun
stuff
in
the
presentation
.
That's why I
threw
a
little
bit
of
Albuquerque
reference
in
there
for
those
of
you
who
are
Breaking Bad
fans
as
well
.
And
some
Better
Call
Saul
stuff
to
.
Let's
go
right
into
JMP
and
start
doing
some
of
the
analysis
.
I'm
going
to
bailout
of that
and
I'm
going
to
open
up
my
data
file
.
Let
me
see
some
screens
around
here
.
It'll
take a
second
to
load
on
your
screen
.
You
should
be
seeing
my
data
file
.
What
you
see
are
the
columns
of
age
in
the
Lab
News
.
That's
the
newspaper
that
I
got
the
data
from.
The
bi-
weekly
date
and
I
broke
that
down
to
year
.
And
these
other
columns
I'll
talk
about
in
a
little
bit
.
Right
now
,
let's
just
look
at
the
age
and
the
year
.
Let's
just
look
at
the
distribution
of
the
data
and
see
what
it
says
.
If
we
analyze
the
distribution
of
age
,
I'm
just
going
to
analyze
distribution
of
age
in
general
.
If
we
look
at
that
,
this
is
what
the
distribution
looks
like
.
You
can
see
a
sort
of
a
skewed
left
distribution
and
we
can
really
see
a
little
more
detail
on
this
if
we
go
into
some
of
the
display
options
in
JMP. This
data
actually
best
fits
Weibull
distribution
,
a
two
parameter
Weibull
distribution
,
which
I'll
show
on
some
of
the
future
screens
.
But
what
we
want
to
do
is
take
this
data
and
use
the
Life
Distribution
to
look
at
it
.
I'm
going
to
drag
over
my
PowerPoint
here
and
show
you
that
I
set
these
distributions
in
the
interest
of
time
rather
than
just
actually
doing
it
.
L
ooking
at
the
distributions
of
the
four
years
,
the
four
categories
of
years
,
which
are
,
2001
,
2007
and
2013
,
2018
,
all
of
these
fit
a
Weibull
distribution
fairly
well
.
I
wanted
to
check
my
assumption
when
I
start
analyzing
the
data
in
Life
Distribution
.
Then
I fit
them
to
a
Weibull
distribution
.
Let
me
open
up
...
Let
me
give
you
a
little
bit
of
background
on
the
Weibull
distribution
,
that
will
help
you
understand
some
of
the
data
that I'm
going
to
show
.
This
is
actually
a
Weibull
distribution...
Weibull
distributions
generated
using
JMP's
formulas
,
actually
.
I've
taken
distributions
with
different
health
plans
and
different
pay DOEs
just
to
show
you
what
the
alpha betas
do
with
the
Weibull
distribution
.
Let
me
run
my
script
on
this
and
show
you
what
these
look
like
.
Basically,
I'm
just
doing
a
Fit
Life
by
X
in JMP
,
and I
save
the
script
to
the
data
table
.
This
is
primarily
just
to
show
you
what
happens
with
Weibull
distributions
when
you
turn
on
a
local
data
filter
here
and
show
you
what
the
parameters
do
with
different
alphas
and
different
betas
.
If
you
have
Weibull
distributions
where...
I'm
going
to
choose
the
three
that
have
a
beta
of
one.
The
beta
Weibull
distribution,
essentially
determines
this
is
the
spread
of
the
distribution
you
think
of
in
terms
of
standard
deviation
.
When
you
plot
the
data
on
a
Weibull
curve
,
you
will
see
that
they
all
are
parallel
lines
and
they're
basically
just
scooting
across
the
X
axis
here
.
The
beta
is
the
slope
of
this
line
,
which
is
the
spread
of
the
data
.
If
I
look
at
three
curves
that
have
the
same
alpha
but
different
betas
,
you'll
notice
that
they're
all
centered
at
about
the
same
point
.
What
the
beta
is
doing
is
changing
the
spread
.
Alpha
is
changing
the
location
of
this.
They
all
across
this
point
,
which
is
actually
at
0.632
.
I'll
talk
about
that
here in
a
little
bit
.
But
though
the
characteristic
lifetime
is
the
alpha
,
and
that's
the
point
where
your
line
crosses
63.2%
,
which
comes
out
to
be
one
minus
one
over
80
if
you
want
to
get
into
the
proof
of
it
.
The
beta
is
the
spread
of
the
data,
the
alpha
is
the
location
of
the
data.
Scale
and
location, it's
sometimes
what
they
call
it
.
Just
a
little
background
on
that
.
I'll
swing
this
PowerPoint
back
over
here
and
show
you
and summarize
slide
what
I
mean
.
These
plots
have
the
same
alpha
.
They're
all
centered
at
the
same
place
.
The
three
that
have
the
smaller
rectangle
around
them
are
the
same
beta
.
So,
they
have
the
same
spread
,
but
they're
located
differently
because
they
have
different
alphas
.
Just
a
little
brief
tutorial
on
what
the
beta
distribution
parameters
do
to
the
curve
.
Let's
just
look
at
all
the
data
on
a
close
up.
Example
,
probable
curve
data
,
and
let's
just
look
at
the
age
parameters
.
If
we
analyze
the
Reliability
,
Survival
and
just
look
at
the
Life
distribution
of
age
...
What
we
see
is
linear
scales
on
the
X
and
Y
,
but
we
can
actually
determine
which
distribution
system
fits
here
.
And
what
we
find
,
of
course
,
is
that
the
Weibull
fits
best
.
This
is
what
the
Weibull
distribution
looks
like,
on
all
of
that
data
crammed
together
.
Using
the
distribution
profiler
,
I
can
actually
manually
scoot
this
over
and
say,
I
want
to
be
90%
sure
I
don't
run
out
of
money
.
I
better
plan
on
a
retirement
age
of
roughly
92
or
so
.
About
92
to
be
90%
sure
I
don't
run
out
of
money
every
time
.
That
was
really
the
focus
of
this
day
in
trying
to
help
me
plan
my
retirement age
.
You
know
,
they
always
tell
you
how
long
you
want
to
plan
your
retirement
for
.
Of
course
,
we
don't
know
.
Usually
we
just
take
a
wild
guess
,
but
this
is
a
little
bit
of
data
driven
decision
making
going
on
here
.
What
if
we
look
at
these
through
the
four
different
years
,
we
can
use
the
same
platform
,
the
Reliability
Survival
or
Fit
Life
by
X.
We
can
look
at
the
age
versus
the
actual
year
here
.
Let's
see
how
the
years
look
differently
.
Let
me
turn
on
the
density
curves
here
so
you
can
see
where
those
fall
.
I want this to be a Weibull
distribution
.
We
can
also
turn
on
quantile
lines
to
see
how
things
change
through
time
.
This
is
the
10,
15
,
98%
quantile line
.
It
looks
like
maybe
maybe
from
2001
to
2007
things
have
change
a
lot
,
but
about
2013
,
maybe
the
people
are
living
a
little
bit
longer
based
on
the
2013
and
2018
data
.
We
go
down
here
and
look
at
the
details
of
this
Fit Life by X
.
You
can
see
the
plots
all separately
here
.
These
little
profilers
are
fun
to
mess
with
here
.
You
can
see
the
age
,
the
probability
of
failure
.
Looks
like
I've
got
the
long
run
and
the
probability
of
failure
is...
I'll
just
put
a
90
here
.
But
we
can
see
that
it
looks
like
we
are
getting
a
little
bit
healthier
because
this
Probability
is
going
down
through
time
,
at
least
for
90
years
old
.
There's
a
lot
of
stuff
in
here
that
you
can
tinker
around
with
.
I
don't
have
the
time
to
show
you
all
of
that
,
but
what I
want
to
get
down
to
here
is
...
This
location
scale
.
This
little
test
here
is
telling
me
,
are
my
locations
different
,
assuming
we
don't
have
the
same
data
,
the
same
failure
mechanism
,
which
in
theory
we
do
,
right
?
We
all
have
the
same
failure
,
but
the
physics
of
the
human
like
failures
should
be
constant
.
And
so
our
data
shouldn't
change
much
.
But
assuming
we
have
the
saving
betas
and
the
alphas
changing
,
in
other
words
,
is
the
location
of
these
changing
?
And
the
data
says
,
yeah
,
that
we're
rejecting
the
null
hypothesis
that
the
data
are
not
scooting
across
this
X
axis
through
the
years
.
There's
a
change
.
Now
looking
at
the location
and
scale
,
it
looks
like
that's
marginal
.
I'm
going to talk
more
,
when
we get
down
to
the
Weibull
here,
the
actual
Weibull
data
.
The
Weibull
looking
at
is there
a
difference
in
the
beta
,
in
the
slopes
or
in
the
spread
of
the
data
year
to
year.
It's
right
on
the
edge
of
being
rejecting
the
null
hypothesis
that
they're
located
,
that
they're
actually
the
same
on
the
slopes
.
If
you
want
to
look
at
different
distributions
through
the
years
,
you
can
actually
do
a
statistical
test
to
see
if
things
are
changing
through
time
,
the
distributions
of
your
reliable
data
.
Right
.
So,
what
am
I
forgetting
?
Let
me
slide
back
over
here
to
the
PowerPoint
.
What
I'm
not
doing
,
I'm
not
considering
censored
data
.
I
don't
have
the
data
for
everybody
who's
still
alive
.
The
retirees
are
still
alive
,
but
I
just
don't
have
access
to
it
.
So,
I
thought
,
well
,
what's
that
going
to
do
to
my
analysis
?
Well
,
I
can
play
around
with
the
data
I
have
and
go
back
in
time
.
For
example
,
I
can
go
back
to
2007
and
I
can
treat
the
2013
and
2018
data
as
censored
data
because
I
know
that
those
retirees
are
still
alive
.
Just
to
see
how
it
affects
my
analysis
,
the
fact
that
I'm
not
including
censored
data
.
I
did
do
that
.
I
went
back
and
messed
around
with
that
.
I'll
show
you
a
little
bit
of
it
,
but
I
didn't
really
find
out
much
.
As
our
great
friend
George
Box
that
models
around
the
practical
questions
,
how
wrong
do
they
have
to
be
to
not
be
useful
?
And
I
really
didn't
find
anything
useful
in
doing
that
.
But
I
will
show
you
at
least
what
I
did
.
To
convince
myself
that
not
having
that
the
data
for
the
retiree
still
alive,
is
not
a
problem
.
If
we
look
at
the
...
Let
me
tabulate
my
data
real
quick
.
If
I
look
at
the
age
,
the
year
of
the
data
versus
whether
or
not
I
suspended
the
data
,
what
I
did
is
I
took
the
2018
data
and
suspended
it
and
worked
my
way
back
to
2013
.
We
did
an
analysis
on
2013
using
the
three
years
of
data
and
then
using
the
2018
data
as
suspended
data
.
Of
course
,
I
had
to
take
the
data
from
2018
and
I
had
to
subtract
five
from
it
because
that
would
be
the
age
in
2013
. I
n
order
to
include
that
suspended
data
,
I
had
to
adjust
their
age
accordingly
as
well
.
But
I
also
did
that
for
2007
.
Let
me
show
you
in
tabular
format
,
I
created
a
column
called
Suspend
2007
where
I
took the
data
from
2018
and
2013
and
also
suspended
it
.
I
took
out
my
data
of
known
deaths
and
half
my
data
as
suspended
data
,
people
who
are
still
alive
.
You
can
see
that
...
Let
me
show
you
what
that
did
to
the
data
.
For
the
age on
2013
data
still
alive
,
you
can
see
that
it's
basically
the
2018
data
minus
the
five
years
.
If
I
look
at
the
actual
age
here
.
If
we
look
at
the
mean
age
,
basically
this
is
the
mean
age
for
the
2018
data
.
By
suspending
,
I
basically
just
subtracted
off
the
five
years
here
.
And
treated
out the
suspended
data
.
And
the
same
goes
for
the
2007
data
.
T
here
instead
,
I
believe
,
I simply
just
subtracted
off
nine
years,
and
five
years
for
the
18
to
2013
data
.
When
I
wrote
the
analysis
of
those
,
I'm
just
going
to
show
you
on
the
PowerPoint
slide
what
it
looks
like
because
it
works
out
convenient
for
me
to
jump
back
and
forth
between
these
two
to
show
you
the
difference
.
What
I
found
was
that
this
was
my
original
data
and
then
this
is
the
data
treating
2018
data
suspended
and
doing
the
analysis
of
2013
data
.
You
can
see
that
the
area
of
interest
where
I'm
really
concerned
about
is
where
I'm
crossing
that
maybe
90%
probability
.
It
didn't
change
much
at
all
.
And
then
this
is
throwing
the
2018
and
2013
data
assets
during
the
analysis
based
on
2007
data
and
the
2001
data.
You
can
see
that
it
really
doesn't
change
the
curve
much
in
the
area
that
I
care
about
.
That
gave
me
the
comfort
that
I
really
am
not
missing
much
by
not
censoring
out
the
data
.
I
mean
by
not
having
this
censored
data
.
How
did
I
calculate
my
90%
confidence
intervals
?
I'm
going
to
just
show
you
in
PowerPoint
how
I
did
this
just
in
the
interest
of
time
.
I
can
use
the
quantile
profiler
and
look
at
the
5%
95%
probability
and
calculate
my
90%
confidence
intervals
.
Typically
,
what
you'll
see
for
data
and
in
this
case
,
it's
66
years
old,
96
years
old
.
But
really
what
I
care
about
is
the
upper
limit
.
I
only
care
about
my
90%
probability
.
And
in
this
case
it
was
age
94
.
Based
on
this
analysis
,
I
now
know
that
I
want
to
be
90%
sure
I
don't
run
out
of
money
.
I
should
plan
to
live
to
be
94
.
This
is
based
on
my
historical
data
.
Quantifying
that
uncertainty
,
this
is
basically
...
A
little
bit
humour
tone
in
.
The
next
phase
in
this
was
to
try
and
figure
out
...
I'm
going
to
throw
in
a
little
bit
of
bonus
material
here
beyond
the
Life Fit situation
.
Here
we
have
this
tool
called
the
Pension
Tool.
We
could
actually
put
in
the
year
we're
going
to
retire,
and
we
could
also
assume
a
salary
increase
and
a
non
base
,
which
is,
essentially,
what
you
would
call
a
bonus
in
private
industry
.
We
could
put
those
into
this
tool
and
it
would
spit
out
our
pension,
our
estimated
monthly
pension
.
I
thought
,
well
,
that
doesn't
help
me
much.
What
I
want
to
do
is,
I
want
to
reverse-
engineer
that
tool
so
that
I
have
the
profiler
to
tinker
with
.
I
went
into
the
JMP
and
I
created
a
Response
Surface
model
,
and
I
basically
input
three
different
ages
at
retirement
,
62
,
65
and
68
.
I
did
three
different
salary
increases
and
I
tried
three
different
based
awards
as
well
.
Let
me
pull
data
over
here
.
This
is
what
the
data
looked
like
when
I
ran
the
experiment
.
I'm
just
going
to
show
you
the
the
screenshots
from
JMP
rather
than
do
an
actual
JMP
analysis
because
I'm
like
20
minutes
already
and I want to
make
sure
I
have
enough
time
to
cover
all
of
this
.
When
I
created
this
experiment
,
you'll
notice
that
the
runs
are
sorted
.
That's
okay
because
I'm
doing
a
computer
simulation
so,
I
don't
have
to
worry
about
lurking
variables
like
the
temperature
in
the
room
,
the
humidity
and
the
operators
.
I'm
going
to
get
the
same
answer
regardless
of
the
order
that
I
ran
this
.
So,
it's
easier
for
me
to
do
the
tool
to
run
them
in
order
.
But
when
you
do
that
design
of
experiments
,
you
want
to
randomize
your
run
.
Do
not
run
them
in
order
out
of
convenience
.
If
you
do,
you
feel,
okay,
your
set of strains
will
tell
you
that
you
can
use
the
how
to
set
easy,
how to
changes
with
the
change,
feature
in
JMP
to
make
sure
you
do
that
properly
by
using
some
blocking
.
My
inputs
were
all
orthogonal
.
I
covered
all
combinations
of
the... My
age
at
retirement
,
the
three
different
ages
and
the
three
different
month
based
awards
and
the
three
different
salary
increase
percentages
.
This
is
the
look
at
the
analysis
you
can
do
when
you're
setting
up
your
experiment
in JMP.
You
can
make
sure
that
your
main
effects
are
only
correlated
to
themselves
.
And
that
there's
minimal
correlation
to
other
variables
,
in
two
factor
interaction
is
also
a
squared
term
since
I
did
a
Response
Surface
model.
But
the
net
result
was
a
nice
prediction
profile
and
I'm
going
to
go
ahead
and
just
run
this
and
show
you
.
Prediction
profiler
.
When
I
set
up
the
experiment
,
I
tell
JMP.
When
you
set
up
an
experiment
in JMP
,
it's
nice
because
it
gives
you
these
scripts
already
in
your
data
table
.
I'm
just
running
the
model
data
table
.
And
JMP
says
,
Oh
,
I
know
what
you
want
to model
,
because
I
was
here
when
you
set
up
the
experiment.
And
it
gives
me
my
Y
and
it
gives
me
my
Xs
.
It
runs
my a
nalysis
for
me
.
I
can
now
use
the
profiler
JMP,
rather than
having
to
put
in
an
age,
salary
increase,
and a non base
award
into
that
tool
and
getting
one
number
out
.
Now
I
have
the
profiler
I reverse
engineered
their
tool
essentially.
I
can
see
what
my
monthly
benefit
is
going
to
be
versus
a
salary
increase
,
non
base,
where
you
see
that
non
base
award
doesn't
really
have
much
effect.
Salary
increase
,
not
much
effect
.
And
of
course
,
age
as
we
would
expect,
have
the
most
effect
.
What
do
I
do
with
this
information
?
Well
,
what
I
really
care
about
is
my
lifetime
benefit
.
And
I'm
also
I'm
concerned
about
inflation
.
So,
I
added
some
formulas
to
this
.
I
added
a
lifetime
benefit
,
which
is
just
my
monthly
benefit
times
how
long
I'm
going
to
live
.
If
you
want
to
look
at
the
formula
,
you
can
see
it's
basically
my
monthly
benefit
times
how
long
I'm
going
to
live
.
And
that's
my
lifetime
benefit
.
That's
what
I
really
care
about
,
is
how
much
money
am I
going to get
in
my
lifetime
.
I
know
if
I
retire
earlier
,
I'm
going
to
get
less
money
.
That's
a
no
brainer
.
But
I
want
to
know
,
is
there
a
point
of
diminishing
returns.
W
hen
I
looked
at
the
age
at
retirement
,
my
lifetime
benefit
for
80
,
84
and
98
created
three
separate
columns
for
those
data
will
be
the
column. All these
data wil
l
be
provided
to
you
as
well
,
if
you
want
to
tinker
with
it
.
But
what
you
can
see
is
a
lifetime
benefit
for
aging
.
It is
actually
a
point
of
diminishing
returns.
Where
I
might
as
well
just
retire
at
65
because
I'm
going
to
get
less
money
,
but
I'm
going
to
get
it
for
a
longer
period
of
time
.
Now
,
as
that
starts
to
increase,
if
my
lifetime
at
age
84
,
turns out
my
lifetime
benefit
would
have
been
a
little
bit
better
hanging
on
there
.
And
as
I
get
older
,
of
course
,
I'm
better
off
waiting
as
long
as
I
can
to
retire
.
But
I
really
don't
know
.
I
don't
know
what
that
number
is
.
My
best
guess
is
84
.
That
was
the
fiftieth
percentile
on my
fit
.
If
I
want
to
be
really
conservative
,
I'm
looking
at
90
.
But
I
also
wanted
to
look
at
how
inflation
matters
.
I
looked
at
the
present
value,
I
put
a
formula
in
JMP
.
There's
actually
a
formula
for
present
value
.
Let
me
explain
[inaudible 00:26:05]
I'm going to
show
it to you
real
quick
.
In
JMP
you
can
actually
use
a
formula
similar
to
what
Excel
has
and
calculate
this present
value.
And
that
adjusts
for
inflation
.
This
is
what
your
money's
worth
now,
relative
to
what
you're
predicting
,
the
future
inflation
rate
is
going
to
be
annual
inflation
rate
.
At
0%
this
present
value
is
just
your
lifetime
.
The
number
of
payments
you're
going
to
have
through
your
lifetime
times
the
payment
amount
essentially
,
but
you
get
penalized
as
inflation
goes
up
,
it
becomes
less
and
less
.
If
I
look
just
at
the
inflation
present
value
data
...
If
I
just
model
those
,
what
I
noticed
was
...
I'll make these
little
bigger
so
you
can
see
them
.
I
noticed
that
when
my
salary
increase
is
smaller,
lower
...
This
is
a
two
factor
interaction
.
You
can
see
the
slopes
changing
.
That
was
some
small
salary
increases
,
which
are
probably
not
unrealistic
in
the
coming
future
.
There
is
a
point
of
diminishing
return
when
we
penalize
for
inflation
.
At
lower
inflation
,
there
is
not
that
big
of
a
benefit
to
wait
to
retire
.
Once
I
hit
about
65
or
so
,
it's
starting
to
flatten
out
.
Really
the
difference
isn't
that
big
.
If
we
look
at
the
predicted
value
,
these
are
not
my
actual
numbers
.
These
were
based
on
censoring
the
data
using
a
pitch factor
of
$4,000
a
month
,
which
is
the
typical
retiree
income
.
I
noticed
that
as
I
weigh
,
it
flattens
out
so
it
doesn't
really
pay
to
wait
to
retire
.
I'm
using
this
information
to
help
me
decide
when
I
really
want
to
retire
.
Right
now
,
I'll
be
62
in
May
,
so
I'm
starting
to
approach
the
ability
to
do
this
.
Probably
going
to
wait
till
64
,
65-
ish
to
where
the
difference
in
my
present
value
is
not
that
big
.
That's
a
little
quick
bonus
on
how
I
use
the
designer
experiments
feature
in JMP
to
reverse-
engineer
this
.
In
summary
,
I
looked
at
the
retiree
death
data
and
I
set
the
Life
Distribution
and
Fit
Life by X,
I
looked
at
four
different
time
periods
and
noticed
maybe
we
did
get
a
little
bit
healthier
and
healthier
in
2013
,
but
by
2018
that
have
flattened
out
.
I
do
have
some
2021
data
that
I
don't
have
in
this
presentation,
but
next
year
at
the
conference
in
Spain
in
March
,
that
it's
actually
pretty
flat
from
2013
to
20
18
to
2021
.
We're
really
not
getting
healthier
,
living
longer
.
That's
going
to
help
me
with
my
decision
.
I
took
a
custom
designed
experiment
as bonus
material
here
,
and
I
reverse-
engineered
this
web
based
applet
that
we
have
and
used
the
profiler
to
replace
that
one
data
point
at
a
time
.
A
really
cool
thing
,
you
can
do
in JMP
.
I
would
take
questions
here
if
were
alive
.
And
with
that
,
I
want
to
say
one
last
thing
.
I
really
want
to
dedicate
this
presentation
to
my
brother
,
who
I
lost
a
couple
of
years
ago
to
brain
cancer
,
who
was
only
15
months
younger
than
me
.
He
was
a
fellow
Sandi
and
also
a
fellow
JMP
user
.
Some
of
you
may
have
met
him
at
the
JMP
conference
and
so I'm
dedicating
this
to
him
.
Thank
you
very
much
.
