Hi.
Thanks
everyone
for
joining
us.
The
title
of
this
presentation is Choice
Design and Max Difference Analysis
in
Optimizing
a
High
School
Laptop
Purchase
in
the
Context
of
a
STEM
project
or
STEAMS
project.
My
name
is
Patrick
Giuliano.
I
am
a
co-
author
and
co-
presenter
for
this
presentation.
And
of
course,
this is JMP Discovery Summit Europe 2022.
And
I'm
happy
to
be
presenting
today.
So
before
I
get
into
our
project
definition or project charter,
I
just
wanted
to
mention
some
general
context
for
this
project.
So
this
project
has
a
STEAMS
orientation,
which is basically a STEM framework,
but
with
the
addition
of
a
focus
on practical AI and statistics,
and through
the
lens
of
JMP.
All
right.
So
the
opportunity
statement for us here
is that every
year,
students
in
grade
nine
with
respect
to
Stanford
Online High
School,
they
need
to
take
core
courses
and
need
to
do
a
series
of
projects.
In
fact,
there
are
many
projects
per
year,
as many as 150,
and
many
of
them
require
the
collection
of
survey
data.
So
in
the
context
of
survey
data
collection,
JMP
has
a
powerful
Choice
Design
and
choice
modeling
platform
as well as Max
Difference
design
capabilities,
and
those
can
be
used
to optimize both survey
methodology
and
analyze
survey
data.
Within
this
particular
use
case,
we're
going
to
use
JMP
16
Choice
Design
choice
modeling
platforms
to
study
consumer
research,
and
also
specifically
to
assist
with
the
choice
of
a
laptop,
the
optimal
choice
of
a
laptop
for
a
student.
We're
going
to
also
take this a step further
and
look
at
some
reliability
questions
and
do
some calculations
to
look
at
the
opportunity
costs
associated
with
purchasing a warranty
at
different
stages
of
ownership.
All
right.
So
quick
orientation
to
our
STEM
diagram
here.
We
had
ten
respondents
in
the
context
of
our
example,
our
sample
data
set.
In
fact,
this
is
a
JMP sample
data
set,
which I will
provide
on
the
user
community
and
is
also
available
in
JMP
Sample
Data
directory.
There
is
ten
respondents
in
this
survey.
As
you
can
see
here
in
the
lower
left
hand
corner
of
the
slide,
there
are
four
attributes
that
change
within
a
choice
set.
There
are
two
profiles
per
choice
set.
There's
eight
choice
sets
per
survey,
one
survey
total
to
be
distributed,
and
of
course,
ten
responses
as
we
indicated.
So
in
terms
of
the
technology
associated with the laptop,
we're
looking
at
four
key
attributes.
We're
looking
at
hard
disk,
drive
space,
processor
speed,
battery
life,
and
the
computer
cost.
And
we
can
see
a
picture
of
the
design
as
well
as
the
different
choice
sets
that
are
paired
by
number
with
the
different
attributes
in
the
columns.
And
then,
quite
nicely,
we see a probability profiler
which
really
just
shows
the
opportunity
space
in
relation
to
the
changes
in
the
axis
across
the
four
parameters
on
the
right
result
in
a
change
in
probability
or
likelihood
of
purchase.
Okay.
So
let's
provide
an
orientation
to the
science
and
the
statistics
with
respect
to
consumer
research.
So
we're
going
to
think
about
collecting
information,
and
we
want
that
information
to
somehow
reflect
how
customers
use their particular
products
or
services
in
general.
We
want
to
have
some
understanding
of how satisfied
customers
are
with their purchase
and what features they
might
desire.
What
insights
they
can
use
to
improve
the
problem
statement
that
they're working on
in
the
context
of
the
purchase
that
they're
making.
In
this
project,
we
use
JMP's
Consumer
Research menu,
and
specifically,
we're
going
to
focus
on
the
Choice
Design
platform.
Okay,
so
here
are
some
nice
graphics
that
just show,
that
highlight
the
consumer
research
process.
And
like
many
processes,
we
can
see
that
it's
very
iterative
and
cyclical.
It's
focused
on
strategy
development,
decision
making,
improvement,
and
solving
challenges.
But
as
we'll
see
later
in
the
analysis,
there
are
specific modeling considerations
that
Choice
Design takes into account
to
make
our
modeling
procedure
a
little
bit
simpler and more effective
for
these
particular
types
of
problems.
All
right,
so
going
back
to
our
overview of the study.
So
the
voice
of
the
customer
really
speaks
to
how
the
manufacturer
decides
to
construct
the
design
in
our
case.
And
so
we
have
two
sets
of
profiles,
again,
that
will
be
administered
to
ten
respondents.
And
the
goal
is
to
understand
how
laptop purchasers
view
the
advantages
of
a
collection
of
these
four
attributes.
All
right.
So
our
particular
use
case
here
is
going
to
be
the
Dell
Latitude
5400
Chromebook.
This
is
a
very
common
budget laptop
that
would
be
considered
appropriate
for
student
usage.
Okay,
so
here's
an
overview
of
the
Choice
Design
modeling
platform.
We
can
see
that
the
four
parameters
are
specified
under
the
Attributes
section.
HDD
size,
processor
speed,
battery life and sale price,
and
the
high
low
levels
of
the
design
are specified
over
at
the
right
under
Attribute
levels.
So
you
can
see
that
by
generating
this
design,
JMP
gives
us
a
preview
of
the
design
and
the
evaluate
design
platform
with
our
choice
sets,
and our
hard
drive
stays,
our
disk
size,
our
speed,
our
battery
life,
and
our
corresponding
price
for
each of the choice sets,
where
the
choice
sets
represent a choice
between
one
computer
possessing
a
certain
set
of
features
and
another.
So
as
I
mentioned
before,
we're
going
to
bring
in
the
sample
data
and
actually
point
to
the
specific
location
where
the
data
is
located.
We're
going
to
go
ahead
and
put that in the community
for
our
users
to
practice
with
this
data.
Okay.`
Here
is the display of the model
specification
window
for
Choice
Design.
So
after
we've
generated
the
design,
we
have
to
fit
the
design.
And
so
we
can
see
that
the
structure of a
design
is
ten
respondents
that
are
paired.
And
so
we're
going
to
set
the
data format to
one
table,
stacked.
Our
select
data
table,
it
populates
into
the
Laptop Results Data table.
Our
response
is
related
to
probability
of
purchase,
which
is
related
to
price.
We
put
subject
into
subject
ID,
choice
set
in
choice
set
ID,
responding
and
grouping.
We
cast
our
four
attributes
into
the
X
role
here.
And
then
we
have
a
subject
effects,
or
we
can
have
subject
effects,
which
are
optional,
which
we
didn't
consider
in
this
particular
context
that
we
can
have
them.
And
we
also
have
an
option
for
missing
value
imputation, which is nice.
One
of
the
things
you'll
notice
here,
though, is that
if
you're
familiar
with
JMP,
you'll see
that
the
run
model option is here,
but
there
are
no
other
options
and
in
many
other
modeling
platforms.
In JMP,
we
have
what's
called
personality
selection.
In
this
particular
context,
we're
limited
to
a
specific
model
under
a
specific
framework
only.
Why
is
that?
Well,
from
our
perspective,
this
is
likely
because
this
modeling
strategy
is very specific consumer
research,
and
we
would
really
only
want
to
consider
main
effects
between
choices
because those effects
reflect
the choice modeling
structure
that
we're
implementing.
We
pick
from
either
one
set
of
features
or
the
other.
Okay.
So
principally,
what
a
mathematical
model
or what
statistical
model
is underpinning
this
type
of
procedure?
It's
really
a
logistic
regression
type model
because
our
wide
response
is
a
probability.
Our
response
can
be
thought
of
as
whether
we're
going to make a purchase or not,
and
therefore
is
the
likelihood
of
making
a
purchase.
And
so
what
we
see
in
the
summary of the model output
is
that
a
negative
estimate
on the terms in
the
model
indicates
that the probability
of
purchasing
chance
is lower at
that
particular
attribute
level.
And
so
we
can
see
from
the
summary,
the parameter estimates
and
from
the
ranking
of
the
effects summary,
and
that
buyers
generally
prefer
a
larger
hard
drive
size,
faster speed, longer
battery
life,
and
the
cheaper
laptop.
And
we
can
see
that
speed
and
price
are really
the
most
significant
predictors
on
probability
of
purchase.
And
we
spoke
a
little
bit
on
the
buyer side
about
why
there
isn't
an
interaction
term.
We
think
that
it's
really
because in the context
of
this
research
problems,
it's
not
really
a
practical
consideration.
But
certainly, it's not likely
due to lack of degrees of freedom
because
in this
particular
data set,
we
had
over
100
observations
and
we're
only
fitting
four
terms.
Okay.
So
another
thing
to
notice
here,
I
think,
that's
not
immediately
obvious
on
the
slide
is
there's
some discussion
or
notes
at
the
bottom of the primer estimates
that
say
converged
in
the
gradient.
And
that
just
really
speaks
to
the
fact
that
this
model
estimation
procedure,
this
likelihood-
based
modeling
procedure
is
an
iterative
procedure.
It's
not
necessarily
deterministic,
and
it
involves
iterating
to
find
an
optimal
solution.
Okay.
So
let's
take
a
look
at
the
effect marginal analysis
and
the
context
of
this
report.
So
we
can
see
here
that
the
report
shows marginal probability
for
each
of
the
four
attributes
that
they're
different
levels.
And
so
what
I'm
highlighting
here
is
that
the
marginal
probabilities
that are
the
most
different
from
each
other
indicate
where
there's
the most
differentiation
in
terms
of making
the
purchase
decision.
So
clearly,
price
and
computer
processing speed
are
the
most
important
in
terms
of really
driving
that
probability
decision.
And
so
as
an
example
here,
you can see that 71% of buyers
may
choose
an
80
gigabyte
over
40
gigabyte
hard
drive size,
as
indicated
by
the
marginal
probability
of
.7129,
and
68%
of
buyers
may
choose,
for
example,
$1,000
over
a
$1,200
or
$1500
price.
So
that's
indicated
by
the
.6843
in
this
marginal
probability.
So
we
can
look
within
each
of
these
marginal
probability panels
to
look
at
what
the
preference
would
be
on
the
basis of a given factor,
like
price
or
speed,
and
also
look
across
the
effect marginal panels,
and
specifically
look
at
the
differences
in marginal probability
or marginal utility
to
see
which
factors
are
most differentiating
in
terms
of
driving
the
purchase
decision.
Okay.
So
the
next
thing
I'm
going
to
talk
about
is
the
utility
profiler.
So
in
addition
to the
response
probability
or
likelihood of purchase,
we
also
have
something
called
utility.
And
so
utility
is
really
something
like
probability,
but
it's
defined
differently.
And
the
utility
profiler report
shows,
in effect,
a
measure
of
the
buyer's
satisfaction
at
a
particular
scenario.
In
the
context
of
utility,
a
higher
utility
indicates
higher
happiness,
if
you
will;
and a lower utility, below zero,
for example,
indicates
relative
unhappiness.
And
so
we
can
see
from
this
profiler
that the utility is increased.
Or
if
you
will
maximize
when
purchasers
spend
the
least amount,
they
have
the
longest
battery
life,
the
highest
processor
speed,
and
the
largest
size,
which
is
completely
intuitive
in
this
context.
And
what
we
can
do
is
we
can
think
about utility
very
much
like
we
can
think
about desirability
in
the
context
of traditional experimental
design,
where we want to
maximize
this
utility
function
in order
to
maximize
the
buyer
satisfaction.
And
so
as
I
mentioned
here,
there
is
a
relationship
between
probability
and
utility.
Mathematically,
what
is
it?
Well,
we
don't
get
into that here,
but
it
is
articulated
in
JMP's
documentation.
And
this
is
something
that
I'm going to be thinking
about
as part of the dialogue to this
talk
when it's archived in
the
user
community.
So
look
forward
to
that.
Okay.
So
now,
let's
look
at
the
probability
profiler.
So
the
probability
profiler
is
going
to
be
similar
to
the
utility
profiler,
as
I
discussed
in
the
prior
slide,
but
it's,
of
course,
a
little
bit
different.
So
what
is
it,
practically?
Well,
the
profiler
set
at
these
particular
settings
of
X,
the
response
probability
is
12%.
So
the
way
we
can
interpret
this
is
we
can say that 12% of buyers
would
consider
to
spend
$1,500
to
get
a
laptop
with
a
40 gigabyte disk,
1.5
GHz
processor
and
a
four
hour
battery
life.
Okay.
So
the
way
we
like
to
think
about
this
is that
for
any
special
condition
that you want
to
know
what
the
probability
is at
a
specific
set
of
factor
levels,
the
probability
might
be
more useful
than,
for
example,
utility,
which
describes
a
measure
of the
buyer's
overall
satisfaction.
And
you'll
notice
clearly
that the
profiler is
limited
to
just
two
levels.
And
again,
this
goes
back to the nature
of choice design and consumer research.
And
we
really
want
to
h
one
in
on
the
buyer's
interest
by
giving
them
a
successive
series
of
dichotomous
choices
to
choose
between.
Like
how
when
we're
at
the
optometrist,
the
optometrist does
the
lens
flipping
and
says
"Is
A
better
or
B
better?"
A or B and
then
you
go
on
to
the
next
one,
and
then
she
asks
for
a
similar
selection
between
A
or
B.
Okay,
so
the
next
part
of
the
project
is really
around
warranty
consideration.
So
I
spoke
about
this
a
little
bit
in
the
beginning.
So
let's
go
into
this
in
a
little
bit
more
depth
before
we
conclude.
So
suppose
the
optimal
choice
for
the
consumer
was
a
laptop
sold
at
a
price
of
$1,000.
Suppose
that
the
consumer
purchased
an
extended
warranty
protection
plan.
If
they
were
to
purchase
one
for
two
years duration,
it would be $102
and
for
three
years,
it'd
be
$141.
One
of
the
key
questions
for
the
consumer
is,
well,
after
they
buy
the
laptop, do they buy
the
warranty as part of the purchase
right
after
the
laptop
or
the
extended
warranty,
or
do
they
wait,
or
do
they
just
not
buy
the
warranty
at
all?
And
I
think
it's
pretty
obvious
to
everyone
that if
you
buy
a
higher
price laptop,
your warranty is going to be
a
higher
price
more
often
than
that.
So
this
is
also
a
consideration
in
terms
of
making
the
final
purchase
decision.
We
didn't
necessarily
incorporate
in
this
hypothetical
experiment.
Okay. So
this
slide
just
goes
into
a little bit
more
detail
about
warranty.
There's
a
lot
of
information
here
and I'll
let our audience take a look at it later.
But
a
lot
of
this
comes
from
our
typical
computer manufacturers
website,
like
Dell.
And
like
anything,
there's a lot of language
that
is
specific
to
an
original
limited
warranty
and
different
terms
and
conditions
that
are
associated
with
the
warranty.
Some
things
that
I
think
I
should
highlight
are
this
idea
of
onsite
services.
The
fact
that
more
and
more
now,
we
have
services
where
a
service
provider
will come
to
you
and
do
repair
or
exchange
of
the
product
at
your
location.
There's
also
the customer
carry-in
and mail-in services,
very
popular
now.
Even
within
the
context
of
Amazon,
mail-in
services
been
around
for
a
long
time.
Customer
carry-in
is
now
a
part
of
Amazon's
ability
to
provide service
in
the
context
of
going
to,
for example,
a
Whole
Foods
Market
for
return.
And
then,
of
course,
product
exchange.
But
these
onsite
service
models
and mail-in service models
are
very
interesting
and
much
more
common
today.
The
other
thing
worth highlighting
is
that
limited
warranty services
depend
on
how
you
purchase
your
original
warranty
policy.
So
if
warranty
services
are
limited,
those
services
are
stipulated
in
the
original
warranty
policy.
Okay.
So let's talk a little bit about
the
JMP's
reliability
and
forecasting.
So
in
the
context
of
this
particular analysis,
we're
going
to
use
the
life distribution platform in JMP
to do some reliability
and
forecast
calculations.
And
what
we're
going
to
try
to
do
is predict
future
failures
of
components
or
that
be
into
the
computer
as
a
system
to
help
us
get
a
better
sense
of
whether
we
should
purchase
a
warranty
and
at
what
time
in
the
life
of
the
product.
So
we
can
think
of
this
analysis
in
terms
of
what
we
call
reliability
repair
cost.
So
we
want
to
compare
with
the
extended
warranty
protection.
We
want
to
compare
the
ready
initial
warranty
to
the
extended
warranty
protection.
And
of
course,
if
the
failure
rate
is
too
high,
then
we
won't
want
to
purchase
warranty
protection
at
all.
And
if
the
failure
rate
is
very
low,
then there would be no need as well
for
us
to
purchase
any
warranty
because we
would
have
a
product
that
lasted
for
a
long,
long
time.
You
can
think
of
each
product
or each computer, in
this
case,
has
its
own
unique
reliability
model.
And
it's
something
to
think
about
in
this
context.
So
if
you've
made
a
particular
purchase
in
this
hypothetical
scenario,
you
can
construct
a
reliability
model
on
the
basis
of
this
particular
computer.
Okay.
So
this
slide
talks
about
a
nice
visual
representation
of
a
reliability
lifecycle,
if
you
will,
or
failure
rate
over
time
for
a
particular
product.
So
we
basically
have
three
phases.
We
have
a
startup
and
commissioning
phase,
which
is
like
a
burning
phase
for
a
product.
Then
we
have
a
normal
operation
phase,
and
then
we
have
an
end
of
life
phase.
And
so
these
phases
can
be,
we
referred
to
them
sometimes
as the first phase,
the running phase or
the
burning
phase,
like
the
infant
mortality
phase
in
the
context
of
survival
for
life.
For
clinical
studies,
the normal active operation phase
can be thought
of
as
the
phase
where random failures may happen,
and
the
end
of
life
phase
is
really
like
the
wear-
out period,
the period before
the
product
completely
wears
out
in
sales.
And
so
corresponding
to
these
periods,
we
can
consider
a
general
range
of
limited
warranty
over
time,
and then a
transition
phase
somewhere
where that limited warranty
becomes
an
extended
warranty
protection
policy.
So
where
we
go
from
a
short
term
warranty, maybe a year,
to
a
long
term
warranty,
two
or
three
years.
So
like
we
said,
if
the
failure
rate
is
very
low,
then perhaps if the product is
expected
to
last
more
than
two
years,
then
maybe
you
don't
need
a
warranty policy
because
you
may
plan
to
replace
a
product
like this every two years
because
it's
just
technology
and opportunity cost of price
versus
the
benefit
of
new
technology.
So
the
important
thing
to
think
about
again in
the
context
of
making
the initial purchase
is
looking
at the
startup and
commissioning
of
a
product.
If
we
purchase
an
original
warranty,
which is what we commonly do,
it
usually
covers
maybe
a
year
of
service
and
it's
part
of
the
initial
purchase
of
the
product.
Okay.
So
now,
what
we're
going
to
do
is
we're
going
to
switch
over
to
a
different
data
set.
This
is
also
sample
data
which
will point
you
to
on
the
community.
But
we're
going
to
assume
that we have
a
database
related
to
the
Dell laptop.
And
it
lists
the
return
months,
the
quantity
return,
and the sold month
over
on
the
right.
And
so
what
we
can
do
is
we
can
graph
this
information.
And
the
X
is
referred
to
the
failure
rate.
The
X
is
in
the
space
of
an
asset
for the failure rate,
the
reliability
model,
and
how
many
parts
we
shipped.
That's
what
these
three
variables
speak
to.
And
based
on
this
probability,
we
can
calculate
if
they
purchase
the
warranty
policy
or
not
against
the
repair
cost.
How
do
we
choose
a
warranty
policy
based on
the
failure
rate
or
return
rate?
So
we
can
use
the
JMP's
reliability
forecasting
capabilities
to
do
this.
So
here's
a
picture
of
the
model
that
we
use
to
fit
this
data.
So
we
have
a
probability
on
the
Y
axis,
first
time
in
months.
And
what
JMP
does
is it
applies
multiple
models.
So
we
fit
all
available
models
at
least
once
that
were
non-
zero,
that
were
not
producing
non-
negative
estimates.
And
what
we
can
see
here
is the ranking of
potential
models
to
fit
this
reliability
data.
And
so
Weibull
is
the
top
choice
here
based on the AICc,
BIC,
and
negative 2
Likelihood
ranking.
So
we
went
ahead
and
went
with
the
Weibull
for
our
analysis,
subsequently.
Okay.
So
here's
an
upper
left
hand
corner
of
the
slide.
What
we
see
is
the
actual
Weibull failure
probability model fit with its parameters,
with
primary
estimates
for
beta,
Alpha,
scale,
and
location.
And
then
the
lower
right
hand
side
of this slide,
what
we
did
is
we
estimated
the
probability
of
failure
at
specific
months,
again,
using
JMP's
reliability
tools.
And
so
how
do
we
tie
this
analysis
into
something
practical?
Well,
beta
is
a
very
important
parameter.
Beta
is
a
very
important
parameter
for
Weibull
distribution.
So
a
beta
less
than
one
might
indicate a product
that
really
doesn't
survive
phase
one;
that
initial
where
a
burning
phase
that
we
showed
in
the
bath
tip
curve.
So
in
that
case,
we
want
to
buy
a
warranty
at
all.
A beta approximately equal to one
that
would
be
maybe
a
product
that's in
the
middle
of
that
curve,
that's
in
that
steady
state
period.
In
which
case
then,
we
wouldn't
want
to
buy
a
warranty
necessarily
anyway
in
that
case
either,
because
we
have
a
very
reliable
product.
So
we
wouldn't
necessarily
want
to
invest
money
in
a
warranty
when
we
would
expect
to
use
it.
Only
when
the
beta
is
greater
than one,
and
you
can
see
in
this
case,
it's
significantly
greater
than
one.
Do
we
really
want
to
consider
purchasing
a
warranty?
In
this
particular
example,
1.6
being
higher
than
1.5,
probably
indicates
that
the
product
is entering that wear-out
period,
that
third phase,
the productive curve.
So
if
we
look
at
this
year,
drawing
on,
say,
an
example
of
35, 36 months,
our
failure
probability
is
maybe
20%.
So
that's
how
we
would
look
at
this,
right?
As
you
go
over
and
you
look
at
the
time,
what
would
the
probability
failure
be
in
the
first
two
columns
of
the
estimated
probability
output
there?
In
this
particular
example,
it's
not
completely
clear
whether
we
should
purchase
a
warranty
or
not,
and
we
likely
need
more
information.
If
we
had
seen
a
beta
that
was
in
the
three
to
four
range,
then
that
would
probably
suggest
that we
would
want
to
purchase
an extended warranty
because
we
would
anticipate
that
wear-
out
would
be
inevitable.
In
the
next
few
slides,
we're
just
going
to
derive a simple decision model
to
go
with
this
reliability
analysis
in the spirit of the choice analysis,
the
choice
modeling methodology
that
we
applied
in
the
beginning.
So
the
consumer
decision
model
has to consider a number of factors:
survival
probability
at
each
month,
failure probability at each month,
which
are,
in
effect,
the
same
thing.
And
the
market
laptop
value
each
month
between
one
year
and
three
months,
of
course,
price
depreciation
happens,
and the monetary loss if not purchasing
the
extended
warranty
protection
if
the
repair
is
needed.
And
then
we
want
to
compare
that
monetary
loss
versus
the
expense
of
purchasing
the
warranty.
So
there's
like a
cost
benefit
analysis
or
a
risk
analysis
that
we're
making.
So
we
show
here,
in
this
slide
again,
the months after purchase,
and
then
we
show
the
survival
probability
at a particular month,
survival
probability
of
the
prior
month,
and
then
the
conditional
failure
probability
at
that
month.
And
so
we
just
use
simple conditional
probability
to
calculate
that
column
that
I
should
have
indicated
with
the
two
all
the
way
over
on
the
right.
And
what
we're
doing
here
is
we're
using
the
previous
Weibull
estimate
at each month to
calculate
the conditional failure
probability
at
the
subsequent
months.
All
we're
really
doing
is
using
the Lag function
to
generate
column
one.
So
it's
just
the
difference
between
each survival
probability
in
month,
let's
say,
13
and
it's
prior
month,
12, and
14,
and
13,
and
so
on.
And
then
we're
just
taking the survival
probability
of
the
prior month,
subtracting
the
survival
probability
at
the
current
month,
times
the
survival
probability
of
the
previous
month.
This
is
conditional
probability.
Okay.
So
let's
talk
about
the
market
laptop
value, which is really
the
second
thing
we
discussed
in this
extended
warranty
protection
slide.
So
we
can
create
a
simple
linear
model
to
model
the
declination
of
value
over
time.
And
that's,
in
fact,
what
we
did.
And
the
slope
on
that
model,
we
use
four
points
to
calculate
the
slope.
And
the
slope
on
the
model
indicates
the
percent
drop
every
month
on
average.
You
can
see
the
slope
is
about
1.3%,
so we
expect
about
a
1.3% drop
per
month
on
average.
And
note
here
that
we
only
really
care
about
the
declination after 12 months
and beyond
because
the
first
twelve
months
are
typically
covered
under
warranty.
Okay.
So
how
do
we
model
the
cost
of not purchasing
the
extended
protection?
Well,
we
can
compare
to
a
two
year
warranty
policy,
failing
at
two
years
as
the
worst
case.
And
we
can
see
that
if
we
look
at
extended warranty
protection
plan
at two years versus
three
years,
$102
versus
$141,
we
get
a
Delta
of
around
$45.
Similarly,
the
cost
if
not
purchasing
the
two
year
production
plan
is
$48
at
two
years
versus
$95
at
three
years,
and
so
that
difference
is
on
the
order
of
$40
to
$50
as
well.
So
what
this
shows
us
is
that
there's maybe a $50 gap
between
the
warranty
plan
and
the
estimated
cost,
and
that
may
be
attributed
to
the
services
and
other
fixed
costs.
But
really
to
make
the
best
decision
about
whether to purchase a warranty or not,
we
want
to
consider
the
cost
of
not
buying a warranty in this framework,
and
the
magnitude
of
beta
together
to
make
the
best
decision
about
whether
to
purchase
or not.
Okay.
So this is nearly
the
end
of
our
analysis.
And
I
wanted
to
just
highlight
one
other
thing
here.
So
we
can
use
a
forecast
capability
here
to
show
us
how
can
we
determine
the
return
rate,
what
resources
do
we
need,
and
a
lot
of
that
depends
on
the
performance
of
service.
How
good
service
is.
If
we
have
too
many
returns,
if we don't forecast,
we
may
not
have
enough
technicians
to
do
the
work.
So
this
is
the
type
of
analysis
where
we're
considering
the
producer.
This
is
from
the
standpoint
of
the
service
provider
and
the
producer.
Whereas
in
the
prior
analysis,
we
are
considering
everything
from
the
perspective
of
the
purchaser
or
the
consumer.
So
this
is
really
a
producer
cost
model.
If
they
don't
purchase
the
warranty,
what's
the
labor
cost,
and
what's
the
material
cost?
So
the
labor
costs
to
handle
all the repairs,
and
the
material
cost
to
replace
parts
for
repair.
And
so
we
can
see
that
there's
a
slight
upward
sloping
trend
on
the
long term repair forecast,
and
that
trend
really
tells
us
what
the
value
proposition
is.
So
as
a
manufacturer,
you may be making revenuein the beginning,
but
then
you
may
lose money
in the long run
if you're doing significant
repair work.
Or
as
I
said
before,
you
may
not
have
the
capacity
to do the repair work
that
you're
obligated
to
do
because of the
liability
problems
with
the
product.
Okay,
so
just
to
conclude
here.
I
just
wanted
to
share
an
overall
key
learnings
here
of
our
project.
We
use
the
STEM
or
STEAMS
framework
to
really
break
up
this
project
into
a
number
of
different
elements
and
apply
an
interdisciplinary
framework.
And we use Choice Design,
and to help consider
survey
design
methodology
as well
as
an
analysis
of
survey
data.
We
also
augmented
our
design
with
a
reliability
performance
model
to qualify our
purchase
and
whether
or not
that
was
a
good
purchase.
Of
course,
the
project
in
the
context
of
the
co-
authors,
including
Mason
Chen.
Chen was very
useful
for
motivating
high
school
students at
SOHS,
and teachers
to
learn
new
methods.
The
final
thing
is
one
thing
that
we
could consider in the future
is
increasing
the
number
of
levels
to
choose
from,
which
would
bring
our
model
into
more
of
a
traditional
modeling
framework.
A
modeling
framework
that's
more
like a
lease
regression
model
or another
particular
popular
modeling
framework
that
looks
continuous
data.
And
so
in
closing,
I
just
wanted
to
highlight
our
references
and
our
statistical
details;
we're
going
to
definitely
provide
those
to
you.
Thank
you
very
much
for
your
time
and
I look
forward
to
any
questions.
