Hi,
I'm
Trish
Roth.
I
am
going
to
be
presenting
to
you
today
about
managing
a
learning
program
with
JMP
that
is
developing
the
next
generation
of
JMP
Ninjas.
A
little
bit
about
myself.
I
am
a
data
scientist
in
core
diagnostics.
My
colleague,
Jeff Pennoyer,
who
helped
develop
this
training
and
the
presentation
materials,
isn't
able
to
join
us.
But
acknowledge
his
contributions
and
also
many
other
colleagues'
contributions
over
the
years
in
putting
together
training
to
improve
the
skill
sets
that
we
have
within
our
organization
with
the
use
of
JMP.
We
both
have
biochemistry,
technology
backgrounds,
and
have
worked
in
the
data
science,
analytics
space
for
a
number
of
years,
along
with
many
other
folks
in
our
division.
A
little
bit
about
Abbott,
in
case
you
aren't
familiar.
It's
a
large
global
health
care
company.
We've
been
in
business
for
over
130
years,
operate
around
the
world.
We
have
over
113,000
employees.
We're
all
focused
on
bringing
life-
changing
health
technologies
to
the
people
who
need
them.
Y
ou
can
see
on
the
right
hand
side
a
number
of
the
different
product
lines
that
we
support.
It
varies
by
country.
If
you
go
to
abbott.c
om
from
your
location,
you'll
see
more
information
about
the
kinds
of
products
that
Abbott
delivers.
Both
Jeff
and
I
and
a
number
of
colleagues
who've
been
involved
with
this
project
and
training
are
from
the
diagnostics
division,
particularly
core
laboratory
where
we
work
with
large
hospitals
and
reference
laboratories
who
provide
diagnostic
testing
directly
to
patients
or
to
physicians.
Y
ou
can
see
some
of
the
other
product
lines
here.
T
he
purpose
of
the
presentation
is
really
twofold.
Wanted
to
give
some
insight
and
thoughts
around
how
we
approach
training,
the
types
of
things
we
include.
How
we
organize
it,
and
as
well
talk
about
some
of
the
features
and
functions
of
JMP
that
we
focus
on,
particularly
in
our
beginner
training,
to
get
people
comfortable
with
data
manipulation
and
data
preparation
and
data
summarization.
These
skill
sets
can
really
serve
them
as
they
continue
to
grow
and
develop
as
data
analysts
and
move
on
to
more
advanced
analytics
and
statistical
analysis
and
modelling.
But
this
is
a
good
foundation
to
get
people
started
and
comfortable.
T
hat's
the
approach
that
we
have
taken.
We
leverage
materials
that
JMP
provides.
We
leveraged
area
experts.
We
try
to
have
area
specific
examples
to
really
make
the
training
relevant
to
people
so
that
they
can
see
the
value
and
how
they
might
apply
it
in
their
day-to-day.
We
talk
to
both
managers
and
employees
about
what
they
want,
what
they
need.
As
we
try
to
think
about
how
much
can
we
really
deliver.
What
kinds
of
skill
sets
are
we
lacking,
or
where
do
we
not
have
enough
people.
How
do
we
grow
those
skill
sets.
How
do
we
fill
those
knowledge
gaps.
Then
on
the
employee
side,
people
want
to
contribute.
They
want
to
grow.
They
want
to
become
functioning
members
of
their
departments,
especially
when
they're
new.
They
want
to
be
independent.
H
opefully
we
can
find
an
intersection
between
those
needs
and
wants
to
develop
some
training.
We
always
have
to
have
the
conversation
around
investing
time,
both
from
a
manager
standpoint
and
giving
their
employees
time
and
space
to
work
on
developing
their
skill
sets
and
employees
have
to
be
willing
to
invest
time
and
practice
and
think
about
how
they're
going
to
apply
things
so
that
it
sticks
and
they
really
do
hone
their
skill
sets.
We've
defined
a
body
of
knowledge
that
we
focus
on,
particularly,
again,
at
the
beginning
and
intermediate
stages.
We
have
a
fair
amount
of
information
and
knowledge getting
started
and
we
do
this
in
what
we
call
a
boot
camp
style.
Sometimes
it's
intensive
over
a
couple
of
days.
A
couple
of
hours
at
a
time
to
just
really
get
people
into
JMP,
get
them
familiar
with
how
to
work
with
data
in
JMP.
Oftentimes,
they're
coming
from
Excel,
so
we
have
to
reorient
them
a
bit
but
the
basics.
What
are
the
menus?
What
are
the
preferences?
How
do
you
get
data
in?
How
do
you
do
some
basic
data
clean-up
and
summarization
functions,
basic
graphing,
creating
formulas?
T
his
allows
people
to
get
up
to
speed
and
be
able
to
actually
deliver
some
analysis
pretty
readily
once
they
get
through
this
boot
camp
core
information.
Then
depending
on
how
deep
we
want
to
go
with
the
learning.
Depending
on
what
the
organizational
needs
are.
What
the
time
availability
is.
We
will
start
to
get
into
the
more
traditional
exploratory
data
analysis
and
statistical
analysis
like
particularly
capability
analysis,
control
charting,
hypothesis
testing,
regression.
A
s
people
move
into
these
topics,
they
then
go
on
to
do
much
more
modelling.
We've
got
a
lot
of
interest
in
scripting.
O
nce
people
have
these
foundations,
they
can
start
to
move
on
to
these
other
topics
and
really
deliver
value
to
the
organization.
Once
we
got
the
material
and
how
much
we
think
we're
going
to
deliver
and
present,
we
have
to
really
think
about
what's
the
best
way
to
deliver
it.
We
do
like
the
in
person.
Obviously,
in
the
last
couple
of
years,
we've
not
done
a
lot
of
that.
But
there
are
some
folks
who
just
really
do
better
face
to
face
where
they
can
have
somebody
standing
over
their
shoulder
and
watching
what
they're
doing.
We
do
predominantly
use
virtual
conferencing.
We
can
bring
together
people
from
a
lot
of
different
sites,
locations
that
way,
and
minimize
travel.
We
also
do
some
very
informal
things,
small
bursts
of
topics,
one
particular
topic,
maybe
over
a
lunch
hour
or
a
small
group
meeting.
We
also
have
some
fully
independent
learners.
We
point
them
to
the
learning
resources
both
from
JMP
and
also
from
our
curated
set
of
information
and
presentations
and
recordings
that
we
have
internally.
W
e've
planned
for
how
to
organize
and
centralize
and
post
information
so
that
it
remains
accessible
to
others
when
they
want
to
come
and
do
some
training.
Just
have
a
little
snippet.
From
our
SharePoint
side,
just
basic
information.
You
don't
have
to
be
a
really
good
website
developer.
You
can
put
a
little
calendar
of
events.
You
can
have
information
for
beginners,
intermediates.
We
provide
links
to
past
recordings.
When
folks
finish
their
training,
we
like
to
do
a
little
congratulations
and
give
them
some
recognition.
I
nternally,
these
links
would
take
you
out
to
some
presentations
to
listing
folks
who
have
successfully
completed
elements
of
our
training.
But
then
this
leaves
a
body
of
knowledge
and
a
body
of
resources
internally
that
folks
can
leverage
as
well.
We
have
a
lot
of
links
out
to
the
JMP
community
where
there's
a
lot
of
good
information
and
SharePoint
document
libraries,
so
presentations
and
data
files.
We
can
keep
this
all
centralized
and
people
can
access
it
on
demand
when
they
have
time
or
interest
in
training.
A
s
well,
we
maintain
a
list
of
subject-
matter
experts
here
just
showing
Jeff
and
I.
But
there's
many
other
colleagues
that
have
been
involved
and
give
their
time
and
talent
to
help
others
develop.
They
put
a
direct
link
to
top
five
countdown
of
why
data
preparation
is
faster,
easier,
and
better
in
JMP.
It's
about
a
three
minute
video
from
Julian
Paris
at
JMP.
It
gets
people
energized
and
motivated
and
excited
when
they
see
all
of
the
features
and
functions
that
they're
going
to
be
learning
about. W
e
sometimes
kick
off
training
with
a
couple
of
little
videos.
As
I
mentioned,
we've
defined
some learning
levels
that
helps
folks
try
to
figure
out
what
they
should
sign
up
for
or
where
they
might
fit.
It
is
a
challenge
because
there's
such
a
broad
base
of
functions
within
JMP
that
an
intermediate
level
or
beginner
level
can
cover
a
lot
of
territory.
But
we
do
our
best
to
try
to
get
people
into
a
group
where
they
feel
comfortable
and
are
at
the
same
learning
pace
and
level.
O
ur
more
advanced
and
intermediate
folks,
we
have
them
do
teach
facts
and
presentations.
It
helps
hone
their
skills
and,
again,
builds
the
community
within
our
organization.
Put
a
little
example
of
how
we
survey
to
solicit
people
that
might
be
interested
in
the
training.
We
leverage
Microsoft
Forms.
We
can
create
internal
surveys,
collect
demographic
information
about
people
who
their
managers
are.
Again,
it's
very
important
that
there's
good
collaboration
and
communication
between
the
learners
and
their
managers
to
make
sure
that
this
is
something
that
can
be
supported.
We
need
to
know
where
people
are
so
we
can
consider
time
zones
as
we're
thinking
about
how
we're
going
to
schedule
training.
Just
survey
101,
the
more
you
can
give
canned
responses
that
a
user
selects
from
versus
entering
their
own
information,
the
easier
you
will
have
in
being
able
to
analyze
and
summarize
that
information
when
you
get
it
back.
Have
a
multi
response
question,
because
one
of
the
things
we
can
look
at
it
shortly
when
we
get
to
the
demo
is
how
JMP
can
handle
a
single
question
that
has
multiple
responses
so
that
you
can
understand
different
categories
that
people
might
have
selected.
But
we
do
also
want
to
understand,
again,
the
why
folks
want
to
participate
in
the
training
so
we
can
ensure
that
we
meet
their
needs
and
that
they're
coming
into
it
for
the
right
reasons.
S
ometimes
we
collect
other
information
about
other
things
they
might
be
interested
in
learning.
Now
we'll
move
into
JMP.
W
here
we're
going
to
start
is
we've
done
a
survey
and
we've
gotten
back
our
results.
We're
going
to
get
that
survey
information
into
JMP
to
find
out
who's
interested
in
taking
the
class.
Then
we're
going
work
up
through
a
series
of
columns
and
formulas.
How
are
we
going
to
keep
track
of
these
folks
as
they
move
through
their
training?
I
will
be
using
JMP
17
standard,
but
most
of
this
has
also
been
done
actually
started
out
in
15
and
16.
It
will
work
there
as
well.
Just
so
you
can
see
where
we're
going.
Again,
we're
going
to
import
this
information.
We're
going
to
do
some
cleanup.
We're
going
to
enrich
the
information
by
adding
some
formulas
and
columns.
Then
we'll
create
a
subset
so
we
can
track
our
beginners.
Then
we're
going
to
start
to
import
information
into
that
table
to
keep
track
of
what
people
have
completed.
Then
I've
got
some
scoring
formulas
so
I
can
figure
out
who
has
completed
the
training.
If
not,
what
elements
of
the
training
that
they're
missing.
Then
we
can
use
that
data
table
with
the
scoring
to
then
communicate
back
congratulations
to
both
the
student
and
their
manager.
I'm
going
to
get
out
of
PowerPoint
and
go
to
a
JMP
journal.
For
the
remainder
of
the
discussion,
we
will
be
in
JMP.
Again,
the
registration
form
comes
back
in
the
form
of
an
Excel
file.
It's
embedded
in
this
worksheet.
When
you
launch
it,
JMP
is
going
to
look
to
import
that
information.
As
a
best
practice,
I
always
click
on
the
Restore
Default
Settings.
This
is
a
fairly
simple
worksheet.
It
only
has
one
tab
and
you
can
quickly
evaluate,
are
the
columns
looking
right?
My
headers
in
the
right
place?
The
data
elements
look
like
they're
going
to
be
properly
imported.
W
e
have
a
quick
look
at
the
data.
If
we
did
have
any
hidden
rows
or
columns
or
empty
rows
columns,
we
could
decide
whether
or
not
we
wanted
those
to
be
imported
or
not.
We're
going
to
leave
the
defaults
for
this.
Simply
click
Import
and
there
we
go.
JMP has
ingested
the
information
from
the
spreadsheet.
Again,
there
is
83
respondents.
You
can
see
all
of
the
categories.
These
were
the
questions
in
the
questionnaire.
Each
one
comes
in
as
a
different
column
in
JMP.
Obviously,
this
is
anonymized,
so
you
can
see
the
learner's
email,
first
name,
last
name.
This
email
is
going
to
be
important
because
that's
going
to
be
the
key.
That's
going
to
be
the
piece
of
information
when
we
look
at
importing.
Did
they
attend
a
training
session?
Did
they
turn
in
their
homework?
That's
going
to
be
how
we
join
the
information.
All
of
the
reports
that
we
get
will
have
the
learner's
email.
That's
how
we
can
combine
data
that's
going
to
come
as
we
progress
through
the
training.
What
are
we
going
to
do
with
this
file?
We've
imported
it.
We're
going
to
do
some
data
functions
to
clean
it
up
and
enrich
it.
I've
listed
those
here.
We're
going
to
look
at
the
location
information
and
we're
going
to
see
that
we
have
some
permutations.
We're
going
to
use
the
Recode
function
to
clean
that
up.
When
we
surveyed,
we
combined
track
and
level.
When
we
did
this
survey,
we
were
actually
surveyed
for
more
than
just
JMP
training.
This
is
a
subset
of
that.
But
we
want
to
separate
those
into
two
pieces
of
information
rather
than
having
them
glued
together.
Then
we'll
take
a
look
at
doing
some
summaries
and
tabulations
and
graphics
so
that
we
understand
who
is
the
learner
population
that
has
signed
up
for
training.
I'm
going
to
jump
over,
save
this
one
to
the
version
where
I've
already
cleaned
this
up.
W
e'll
take
a
look
at
what
that
looks
like.
Again,
here is,
in
the
survey,
we
have
location
and
you
can
see
there's
a
new
column
with
a
plus
sign
that's
got
a
formula. Y
ou
can
have
a
look
at
it
and
see
that
it's
doing
some
manipulation.
Where
it
said
Lake
Forest,
we're
actually
converting
that
to
Chicago,
Illinois.
The
Recode
function
generated
those
formulas.
I
did
not
have
to
write
that
formula.
The
way
that
you
do
that
is
you
go
to
learner
location
and
you
can
just
right-
click
and
select
Recode.
It's
going
to
show
you
here's
all
the
data
elements.
You
can
see
pretty
quickly
that
there's
some
permutations.
Somebody
entered
Knoxville,
Tennessee
with
and
without
a
parenthesis.
Geographically,
you
may
or
may
not
know.
Chicago
is
a
big
location,
a
big
city,
and
actually
Des
Plaines
and
Waukegan
are
actually
all
suburbs
and
Lake
Forest
as
well
are
all
really
part
of
Chicagoland.
W
e
want
to
group
those
together.
They're
all
the
same
time
zone.
Those
folks
are
within
15,
20-
30
minutes
of
each
other.
W
e
could,
again,
we've
got
them
all
highlighted.
I
highlighted
multiple
by
holding
down
the
CTRL
key.
I'm
going
to
right-
click
and
I
can
say
I
want
to
group
these
all
to
be
Chicago.
N
ow
you
see
all
four
of
these
entries
are
going
to
be
Chicago.
I'm
actually
going
to
add
Illinois.
W
e
go
through
that
process
for
a
number
of
the
different
permutations.
Again,
Santa
Clara,
California
got
entered
with
and
without
the
state
designation,
so
I
can
group
these.
I
just
want
to
use
the
two-
letter
designation.
Similarly,
so
we
can
go
through
the
different
permutations
and
do
this
data
clean
up it.
The
way
that
we
want
to
save
it
is
we
could
overwrite
the
data.
I
don't
like
to
overwrite
data
in
my
data
tables.
I
want
to
save
the
formula
because
if
I
run
another
class.
Or
I
go
to
the
run
another
survey,
it's
likely
that
I
might
see
similar
permutations.
Just
for
the
purposes
of
demo,
I'm
just
going
to
rename
this
as
demo.
Now
it's
going
to
create
a
new
column
formula.
I'm
going
to
hit
Recode
and
you
can
see
that
it
created
this
new
column
here
with
the
formula.
A
gain,
I
didn't
do
all
of
the
clean-up
permutations,
but
you
can
see
how
it
did
the
mapping.
I
n
the
future,
if
it
sees
Des Plaines,
it's
going
to
group
all
of
these
under
Chicago.
W
hy
do
that?
It
obviously
reduces
the
number
of
variables
if
you
try
to
plot
or
summarize,
and
it
just
cleans
things
up. W
e
did
some
additional
clean
up
items.
As
I
mentioned,
this
track
and
level,
it
broke
it
into
two
pieces
using
a
word
formula.
We
just
said
take
the
first
word
of
track
and
level,
and
there
you
have
it.
JMP and
take
the
last
word
of
track
and
level,
and
that
will
get
you
level.
You
can
do
that
by
simply
taking
the
combined
data
column
and
using
some
pre-set
column
formulas
that
JMP
provides.
Here's
first
word,
or
you
can
select
last
word,
you
can
see
first
word
JMP.
Then
I
just
retitled
these
to
simplify
the
name.
Now,
why
do
I
do
that?
Now,
I
want
to
have
a
look
at
what's
in
this
data
set
so
I
can
use
the
Analyze,
Tabulate
function.
Now
that
I
have
them
separated,
I
could
leave
them
like
this.
Y
ou
can
see
the
population
of
beginners
and
intermediates.
I
like
to
have
them
broken
out.
I'm
going
to
do
track
and
then
I
want
to
know
level.
T
here's
different
drop
zones
where
you
can
put
these
depending
on
what
you
want
to
see.
But
I'm
going
to
build
up
a
series
of
charts.
I'm
going
to
do
location.
Now
you
can
see
that
there
were
83
respondents,
61
beginners,
22
intermediates.
If
I
check
the
box
down
here
for
order
by
by
count
of
grouping
columns.
You
can
see
that
it
resorted
so
that
the
grouping
with
the
highest
count
is
listed
first.
So
rather
than
being
alphabetical,
it's
in
descending
order
by
how
many
are
in
each
of
the
categories.
You
can
quickly
see
what
is
the
distribution
of
the
locations
of
the
people
interested
in
your
training,
and
you
can
start
to
plan
for
how
you're
going
to
deliver
it.
If
you're
done,
you
can
click
done.
Then
what
I've
done
is
use
the
script
function
within
JMP,
Save
script
to
data
table.
Then
I
can
give
that
a
descriptive
name
and
it
will
save
it
right
back
to
the
data
table.
Here
we
go.
Tabulate
by
location
and
level.
If
I
click
that
button,
basically
it's
repeating
that
analysis.
I've
added
a
little
bit
more
detail
where
I,
in
addition
to
the
number
of
respondents,
percentage
of
total,
so
you
can
see
what
proportion
is
in
each
category.
Instead
of
tables,
graphics
are
always
nice.
Again,
I've
presaved
some.
We'll
take
a
look
quickly
at
how
to
build
those.
I've
taken
advantage
of
a
function
called
the
column
switcher.
Built
up
a
graph
that
I
liked
and
now
I
can
easily
toggle
between
different
categories.
You
see,
this
one's
a
little
bit
messy,
but
you
can
go
between
categories
to
have
a
look
at
different
managers.
Some
only
submitted
one
person
to
be
going
to
training.
Some
submitted
multiple.
It's
a
little
bit
busy.
But
again,
you
can
see
what
functional
area
they
come
from
and
you
can
see
what
location
they
come
from.
I've
also
added
a
data
filter.
If
I
really
just
wanted
to
hone
in
on
beginners,
I
could
select
beginner
intermediate.
You
can
see
in
one
page,
I
can
very
quickly
get
a
variety
of
graphs
and
easily
put
them
into
a
presentation
or
save
them
so
I
can
communicate.
The
beginners
are
mostly
from
Chicago,
but
there's
a
good
chunk
from
Dallas.
We've
got
two
Irish
sites
that
have
a
number
of
folks
that
are
interested.
But
if
I
go
to
intermediate,
Chicago,
Germany,
again,
Texas,
you
can
see
where
folks
are
coming
from
that
are
interested
in
different
levels
of
training.
The
way
we
build
this,
I'll
pull
it
off
to
the
side
and
we'll
just
look
at
it
quickly.
Graph
Builder.
I
started
with
location.
I'm
actually
going
to
drag
location
to
the
Y
axis
and
hit
a
bar
chart.
Now
I
see
each
of
the
categories.
The
reason
I
like
to
do
this,
sometimes
if
the
text
is
long,
it's
a
little
bit
easier
to
read
it
when
it's
in
this
horizontal
orientation
than
if
it's
in
a
vertical
orientation
and
you're
trying
to
read
it
sideways.
Again,
if
you
right
click,
I
can
change
the
ordering
and
then
go
order
ascending.
A
gain
now
it's
count
data.
So
it's
putting
the
category
with
the
highest
counts
at
the
top
and
the
least
counts
are
at
the
bottom.
I'm
going
to
change
this
from
mean,
it's
not
really
a
mean
it
is
just
a
count.
Now
I
can
add
a
label
that
is
percent
of
total
values.
I
can
see
again,
Chicago
is
40
%
and
you
can
get
the
proportion.
Then
on
the
Y,
X
axis,
rather,
you
can
see
the
actual
counts.
You're
getting
a
lot
of
information
in
one
graphic.
One
other
feature
that's
really
nice
in
Graph
Builder
is
I
right
click,
add
caption
box,
and
I
right
click
it
again
and
change
the
caption
box
location,
the
Y
position
we'll
put
it
at
the
bottom.
Just
there's
more
real
estate
at
the
bottom.
Again,
you
can
see
there
were
83
participants,
40
%
are
from
Chicago.
A
lot
of
information
in
one
graphic.
You
can
further
customize
it
by
right
click,
hide.
I
don't
really
want
all
these
annotations.
I
think
it's
obvious
that
it's
count.
I'm
just
going
to
hide
some
of
the
annotations.
It
just
makes
it
a
little
cleaner.
The
way
I
got
the
coloring
was
to
drag
location
over
to
color.
Now
each
category
has
got
a
different
color,
but
I
can
customize
those
so
I
can
click
on
the
bar.
Chicago's
blue,
it's
fine.
Next
one
down
is
Longford.
If
I
right
click,
I
can
change
the
coloring.
It
will
go
a
little
bit
lighter
blue.
Dallas,
Texas
was
the
next
category,
fill
color.
I
will
do
light
blue.
Then
the
rest
of
them,
I'm
just
going
to
hold
down
my
CTRL
key
as
I'm
clicking
through
all
of
these
categories
to
highlight
the
rest
of
them.
Again,
pick
one
of
them
in
the
legend,
right
click,
and
we're
just
going
to
send
them
to
gray.
That's
how
I
got
to
the
customization.
Now,
I
don't
really
need
to
see
this
legend,
so
I
can
get
rid
of
it.
The
way
the
column
switcher
works,
if
I
look
up
here
in
the
toolbar
column
switcher,
it's
also
icon.
It's
right
now
on
location.
I
can
add
to
that
organization
manager's
email,
again,
holding
the
CTRL
key.
Okay.
T
hat's
what
brings
up
this
window
where
now
I
can
switch
between.
It
gets
a
little
busy
and
I
have
to
go
through
the
same
color
customization
if
I
want
to
have
the
blue
and
just
a
simple
blue
and
Gray.
But
it
remembered
it
for
location.
I
can
say
done.
Again,
that
gets
me
real
estate.
I
don't
really
want
to
look
at
that
legend.
U
nder
Show,
I
can
turn
it
off
and
then
resize.
If
I
wanted
to
be
able
to
toggle
again
between
beginner
and
intermediate,
we
can
use
a
local
data
filter
and
say
I
want
to
be
able
to
filter
on
level
to
select
it,
do
the
plus
sign.
One
nice
feature
I
think
I've
noticed
in
17
is
if
I
resize
this,
it
enlarges
the
font
automatically,
which
is
actually
very
nice.
Now
I
can
toggle
between
beginner
and
intermediate,
or
I
can
clear
the
selection
and
again
leave
it
at
both.
Once
I'm
happy
with
that,
again,
onto
the
red
hotspot,
Save
script
to
data
table,
give
it
a
descriptive
name
so
you
know
what
it
is,
and
it
will
save
that
script
to
the
data
table
so
you
can
re
execute
it.
The
other
thing
you
can
do
is
under
the
Edit
menu
is
Edit
Journal
or
CTRL
J,
then
it
will
grab
an
image
of
that
analysis
and
place
it
in
your
journal,
which
is
what
I
have
done
here. I
f
I
go
to
the
journal,
you
can
see
that
I've
captured
these
images
of
the
graphs
the
way
that
I
like
them.
Then
nice
thing
about
doing
it
in
a
journal
versus
grabbing
a
static
picture
is,
again,
you've
got
your
in
JMP
and
you've
got
your
red
hotspot,
which
means
you
can
have
interactivity.
I
can
select
the
graph
from
the
journal,
and
as
long
as
the
table
behind
the
graph
is
open,
you
can
say
run
a
new
window
and
I
get
back
my
interactive
graph.
If
I
wanted
to
make
some
additional
changes,
change
the
text,
I
could
do
that.
But
I've
got
everything
saved
in
a
nice
workbook.
Now
we
have
a
learner
list.
We
know
where
they're
from.
I
wanted
to
quickly
touch
on
the
skill
set
piece
in
this
multi
response.
The
way
I
handled
that
is
I
actually
created
a
copy
of
the
column
called
skill
set.
If
you
look,
it's
a
little
bit
hard
to
see,
but
if
you
look
carefully
at
the
original
column,
each
of
the
selected
items
was
separated
by
a
semi
colon.
JMP
can
handle
a
semi
colon
as
a
delimiter.
I
found
that
it
didn't
work
very
well
in
this
analysis.
As
a
workaround,
I
created
a
copy
of
the
column,
and
then
I
did
CTRL
F,
and
you
can
do
a
simple
find
and
replace.
I
replaced
the
semi
colon
with
a
comma,
and
JMP
liked
the
comma
a
whole
lot
better.
Now
why
do
that?
Again,
I'm
going
to
go
to
the
graph
builder.
Then
the
final
thing
I
did
was
told
JMP,
Hey,
this
column
is
actually
a
multi
response
column
rather
than
being
a
number
or
character.
It's
multi
response.
That's
what
prompts
JMP
to
look
for
that
delimiter
and
understand
that
there's
different
categories
in
that
column.
Again,
we'll
just
quickly
go to
the
graph
builder
and
you
can
look
at
the
difference.
Now,
if
I
take
multi,
you
can
see
each
category
is
only
represented
once.
Of
all
the
different
reasons
why
people
are
interested
in
participating
in
training,
each
category
gets
counted
independently
and
you
don't
see
all
the
permutations.
A
gain,
right
click,
order
by.
You
can
see
what
the
most
popular
ones
people
want
to
improve
their
skill
set
so
that
they
can
be
more
efficient.
We've
been
talking
a
lot
about
storytelling
with
data,
how
to
getting
a
message
across,
how
to
drive
action
with
data
stories.
These
are
all
the
reasons
that
people
want
to
participate
in training.
Yeah,
that
multi
response
function
is
nice,
particularly
if
you're
doing
surveys.
Final
thing
we're
going
to
do
on
this
data
table
is
we're
just
going
to
take
a
subset
because
we're
going
to
focus
on
the
beginners.
A
gain,
you
can
use
the
data
filter
level.
I
only
want
the
beginners.
Again,
out
of
the
83,
it's
highlighting
the
61.
I'm
going
to
select
a
set
of
columns
in
my
data.
I
don't
want
all
of
them. I don't ant that one.
Then
we're
going
to
create
a
subset
table,
subset,
and
we're
going
to
tell
JMP,
I
only
want
to
use
this
selected
columns.
It
gives
you
a
nice
preview.
Here's
the
email
address,
where
they're
from,
and
their
level,
and
we
can
say
okay.
Now
we
have
just
a
list
of
the
beginners,
where
they're
from,
basic
information,
and
this
is
what
we
can
use
to
start
to
build
a
tracker
to
say,
Okay,
these
are
the
folks
that
are
beginners.
I
got
to
make
sure
that
they
complete
their
requirements.
How
am
I
going
to
do
that?
I'm
going
to
take
you
to
the
version
where
I
already
have
this
set
up.
Let's
close
the
registration
information.
Now
we're
into
the
completing.
What
I've
done
is
taken
this
basic
data
table
that
had
the
information
about
who
registered
for
training,
and
I
started
adding
a
whole
bunch
of
columns.
Beginner
training
consists
of
five
different
classes
that
they
need
to
attend,
five
different
sessions,
three
homework
assignments.
We
assign
them
a
couple
of
STIPs
modules.
That's
Cisco
Thinking
for
industrial
problem
solving.
They're
free
courses
and
modules
available
through
the
JMP
learning
community.
We
assign
a
couple
of
them
to
the
beginners.
They
can
take
more
if
they
want
to.
I've
got
them
all
listed
here.
I
will
talk
about
these
hash
marks
in
a
minute
and
why
these
columns
aren't
blank,
like
the
homework
columns.
We
request
that
they
provide
a
data
example.
These
are
all
the
elements
of
the
training.
What
I've
leveraged
is
a
couple
of
column
features.
So
if
I
go
to
class
1
and
I
do
column
information,
what
you
can
see
is
1,
I've
used
this
list
check
function.
I've
told
JMP,
these
are
the
only
values
that
can
go
in
that
column.
That
just
helps
keep
the
data
sheet
clean.
If
I
do
any
data
entry,
it
forces
consistency
across
the
data
table.
The
other
really
nice
feature
is
called
value
colors.
Then
I've
assigned
a
specific
color
to
each
value.
Yes,
if
they
attended
class,
they
get
a
nice
dark
green.
If
our
students
were
recording,
they
maybe
they
watched
the
recording
later,
they
didn't
come
to
class
live.
Sometimes
people
tell
me
they're
out
of
the
office,
just
color
coded
that
red.
Then
the
key
feature
is
if
you
click
on
the
little
box
at
the
top
and
hit
Apply,
it
will
color
code
your
cells
similar
to
what
people
are
used
to
seeing
in
Excel,
it'll
color
code
your
cells
based
on
the
content
of
that
cell.
It
makes
it
very
easy
to
look
across
this
data
sheet
or
data
table
to
say
where
are
we
at,
how
many
people
are
missing
things,
how
many
people
are
green,
how
many
people
are
red.
Once
you
have
one
column
set
up,
you
can
use
copy
column
properties,
and
I
can
broadcast
that
across
the
remaining
four
columns
for
the
different
classes,
which
is
what
I've
done.
When
you're
using
these
value
colors,
it
puts
a
little
black
X
mark
as
an
attention
activator
to
let
you
know
that
it's
going
to
color
code
depending
on
what
you
enter
there
versus
in
the
homework
field,
I
hadn't
yet
activated
that.
These
are
all
the
elements
that
are
required
of
training.
Now
I
have
my
workbook
for
managing.
One
other
feature
we'll
talk
about
is
joining.
We
held
our
first
class.
I'm
going
to
clear
this
a
little
bit.
I
actually
already
have
this
Excel
file
open.
Microsoft
Teams
provided
me
with
a
summary
of
the
meeting.
I
had
42
participants.
Here's
who
they
are.
Here's
their
email
address.
It
does
actually
tell
me
how
long
they
were
in
the
meeting.
If
I
scroll
down
to
the
bottom,
I
can
decide
if
Learner
26
who
was
there
for
12
seconds if
they
were
going
to
get
credit
for
attending
or
not.
But
really
all
I
need
out
of
this
worksheet
is
just
their
email
address
because
that's
how
I
know
who
they
are
in
my
tracking
sheet.
I've
highlighted
it
and
I'm
just
going
to
highlight
it
in
Excel.
One
of
the
other
things
you
can
notice
is,
Learner
79
must
have
had
a
little
trouble.
They
were
in
for
one
minute
and
then
must
have
gotten
dropped
or
had
to go
back,
come
back
in.
There's
actually
two
entries
for
Learner
76
and
Learner
79.
You
always
want
to
look
at
your
data
first.
But
with
JMP
and
Join,
we
don't
have
to
worry
about
that
JMP
will
do
a
good
job
of
merging
the
information.
Another
really
fun
feature
is
the
JMP
add
in.
I've
highlighted
what
I
want.
There's
a
JMP
add
in
within
Excel,
select
it,
data
table.
It's
opening
it
on
my
other
screen.
I'll
pull
it
over.
I've
literally
just
grabbed
that
information
and
put
it
in
a
JMP
data
table.
I'm
going
to
quickly
add
a
column
called
Class
1.
This
is
not
a
data
table
we're
going
to
keep,
so
I'm
not
going
to
spend
its
character.
Okay.
Then
this
is
the
list
of
names
of
people
who
attended
class
1.
I'm
going
to
just
enter
Y
because
I
know
that
that's...
I'm
going
to
fill
to
the
end
of
the
data
table
and
it's
already
called
attendance.
I
don't
need
the
Excel
spreadsheet
anymore.
I'm
going
to
go
back
to
my
tracker.
You
can
see
that
I've
deleted
some
of
the
entries
here.
I'm
going
to
use
a
table
update
function.
I
like
update
because
I
just
keep
building
onto
the
same
table.
I
don't
constantly
generate
new
tables
that
I
have
to
rename
and
save.
In
that
attendance
list,
I
know
that
email
matches
email.
It
is
case
sensitive,
so
I
actually
had
to
make
sure
that
it
was
in
full
lowercase
in
both
locations
in
order
for
it
to
match
up.
JMP
will
give
you
a
really
good
preview.
If
you
don't
see
anything,
then
you
can
take
a
look
at
whether
or
not
maybe
you
missed
something.
Then
what
I
want
to
do
is
the
attendance
table,
which
is
the
update
table,
I
want
to
update
the
class
1
information
there.
I
want
to
replace
the
class
1
information
in
the
Master
table
because
it's
just
blank.
Let
me
see
if
I
can
do
this
so
you
can
see
what
happens
when
I
hit...
Here
we
go.
One,
it's
giving
you
a
preview,
but
if
you
watch
up
here,
this
is
the
tracker
sheet.
I'm
going
to
update
it.
I
hit
okay,
and
there
we
go.
Now
it's
updated
the
tracker
sheet
with
the
information
about,
yes,
these
people
attended
class
1.
As
we're
moving
through
a
training,
we're
going
to
do
that
on
a
repetitive
basis.
We're
going
to
get
reports
about
attendance.
We're
going
to
get
reports
about
who
completed
their
homework.
I
don't
have
to
manually
go
in
here
and,
okay,
you
were
there,
you
were
there.
I
can
do
it
in
a
much
more
automated
fashion.
Then
that
way,
if
somebody
emails
me
or
lets
me
know,
"Hey,
I
wasn't
there,
but
I
watched
the
recording,"
now
I
can
just
enter
that
manually
and
it
really
reduces
the
amount
of
manual
intervention.
Again,
a
lot
like
what
people
are
used
to
working
with
spreadsheets,
but
I
think
once
you
get
used
to
it,
you
can
actually
do
a
lot
more
here.
File,
close
this
one.
This
is
just
a
transient.
I
don't
need
to
keep
it
for
any
reason,
so
I'm
not
going
to
save
it.
Then
we're
going
to
go
to...
Now
time
has
gone
by.
We've
run
a
whole
bunch
of
classes.
You
can
see
people
who've
come
to
class.
People
have
missed
stuff.
Some
people
came
and
I
guess
decided
it
wasn't
for
them.
I've done
some
SIPs
modules.
This
is
what
it
looks
like
in
the
end.
This
is
the
accounting
of
what
all
of
the
participants
have
completed.
One
thing
that
would
be
nice
is
SIPs,
they
send
me
a
copy
of
their
certificate,
so
I
do
have
to
manually
enter
that
information.
It
would
be
great
to
be
able
to
get
a
report
that
I
could
then
just
join
in
or
do
some
easier
way
of
tracking
that,
but
to
be
determined.
Then
the
last
piece
is,
okay,
great,
I
know
who
was
there.
I
know
what
they
did.
Now
I
got
to
score
it.
This
is
where...
Again,
we've
got
the
tracking
spreadsheet
and
we
come
all
the
way
over
to
the
end.
Now,
I've
added
a
whole
bunch
of
other
columns
which
are
based
on
formulas
and
added
color
coding.
It's
relatively
a
little
busy,
but
it's
relatively
easy
to
see
how
much
green
there
is
versus
red.
I've
got
things
color
coded
so
it
highlights
to
me
who's
missing
information.
T
hen
I
actually
even
created
a
formula
for
each
person.
What
exactly
is
missing?
If
nothing's
missing,
it's
just
a
series
of
commas.
And
then again,
a
conditional
formula,
and
we'll
look
at
these
really
in
the
moment
that
tells
me,
hey,
did
they
meet
the
minimum
requirements?
All
the
things
that
I
said
that
they
had
to
do.
If
they
did,
then
I
get
a
finished,
and
so
it's
really
easy
for
me
to
then
say,
okay,
I
know
who's
finished,
I
know
who
hasn't,
and
it
updates
automatically.
A
ll
this
information
will
be
available
next
time
I
run
the
training.
Once
I
built
it
once,
I
can
make
minor
modifications,
and
so
it
becomes
a
really
helpful
tool.
Just
to
finish
out,
the
power
of
the
formula
building.
Here's
class
score.
I
said
we
held
five
classes.
I
decided
you
had
to
at
least
make
it
to
a
minimum
of
three.
How
did
I
score
that?
I
created
a
column
called
class
score.
Again,
you
can
see
it's
got
a
formula.
We'll
take
a
look
at
the
formula.
It
looks
pretty
busy,
but
we
can
build
it
up
in
pieces
and
show
you.
Once
you
get
accustomed
to
building
logic,
you
can
copy
and
paste
the
elements
and
replicate
them
pretty
quickly.
I
f
we
just
take
a
really
quick
look,
each
box
is
a
different
class.
They
come
to
class
one.
If
there's
no
entry
in
that
field,
it
means
they
didn't
come.
If
the
entry
is
not
a
Y
and
it's
not
an
R
because
remember,
Y
stands
for
yes,
R
stands
for
recorded.
I
f
they
didn't
come
to
class
directly
or
participate
in
the
recording,
they
get
zero
points
for
class
one.
If
they
had
a
Y
or
an
R
in
these
different
ways,
you
could
write
the
logic,
they
get
a
point
and
you
basically
take
this
element
and
you
can
paste
it
and
then
update
same
formula
class
two,
class
three.
You
see
I'm
adding
them
up.
They
get
one
point
for
class
one,
one
for
class
two,
three,
four,
and five.
It
totals
up,
and
it
is
a
little
bit
hard
to
see
with
some
of
the
color
coding,
but
this
person
only
came
to
one
class.
This
one
came
to
all
four.
Again,
this
is
where
we're
using
the
value
colors.
They're
not
showing
an
order.
If
it's
zero,
one
or
two,
because
that's
not
the
minimum
requirement,
I
color-
coded
it
in
some
red
coloring
and
three,
four
and
five,
which
is
minimum
or
above
is
green.
Again,
a
lot
of
information
you
can
build
up
in
formulas
pretty
quickly.
Same
thing
for
homework.
If
we
look
at
the...
There
it's
a
minimum
of
two.
We
can
take
a
really
use
a
slightly
different
logic
this
time
just
to
show
you
the
flexibility.
In
the
homework
field,
if
nothing's
there,
that
means
they
didn't
get
a
check
mark
or
a
tick
saying
they
completed
the
homework.
If
homework
is
missing,
and
then
this
little
exclamation
point
means
not.
If
it's
not
missing,
which
means
it's
there,
they
get
a
point.
A
gain,
you
sum
them
up.
So
if
they
did
all
three
homeworks,
they
would
get
three
points.
You
just
work
your
way
across
tips.
We
required
two.
Some
people
overachieved
and
you
see
this
person
at
the
top,
it's
really
dark.
Did
all
seven.
I
developed
an
extra
credit
formula
saying,
okay,
if
you
were
assigned
to,
if
you
did
more,
I'll
give
you
some
bonus
points.
And
that
way,
if
you
missed
a
homework,
you
you
can
cover
it
with
an
extra
steps
module.
A
gain,
you
can
just
build
up
logic
statements.
You
have
to
really
think
through
what
your
requirements
are
and
what
the
logic
form
is
going
to
be.
We'll
just
do
a
quick
how
do
we
build
that?
I
think
I
had
this
column
seven,
which
was
an
example.
Yeah.
All
right.
We'll
just
clear
this
out,
edit
formula.
Again,
I'm
just
going
to
clean
this
up,
build
it
from
scratch
really
quick.
Again,
once
you're
in
the
formula
editor,
if
you're
not
sure
where
things
are,
you
can
type
and
it'll
show
you
if
it's
under
conditional.
Just
going
to
clean
that
up.
Then
it
guides
you,
well,
what
does
it
need?
If
what?
Well,
if...
Make
sure
you
highlight
the
box.
Class
one,
I
want
to
do
a
comparison.
Is
missing
zero
else
1.
We're
just
going
to
do
a
simpler
formula.
There
you
have
it.
Now
you
can
add.
Now
I
need
to
do
the
same
thing
for
class
two.
Depending
where
you
click,
you'll
highlight
different
parts
of
the
formula.
You
want
to
make
sure
you
get
the
whole
box.
You
can
use
your
up
arrow.
Once
this
whole
formula
box
is
highlighted,
I
can
say
Control +
C
for
copy.
Now
I
can
just
paste.
I
don't
have
to
build
up
this
if
then
else
logic.
I
nstead
I
can
just
say,
okay,
class
one,
I
want
to
apply
the
same
thing
for
class
two.
That's
how
you
iteratively
would
build
up
a
formula
once
you
applyââ
You
can
start
to
see
that's
how
we
added
up
the
scores
based
on
the
yeses
or
our
content
being
in
the
attending
class
formula.
Then
the
final
piece
is
I
want
to
know
what's
missing.
One
thing
you'll
notice
is
that
this particular
row
here,
notice
that
they
aren't
designated
as
having
finished
their
training.
If
I
look
across
the
row,
they
completed
four
classes,
they
completed
all
three
homeworks,
they
actually
completed
four
SIPs
modules.
I
know
it's
difficult
to
see
with
the
coloring,
but
what
they
did
n't
complete
was
providing
a
data
example.
It's
blank.
That
is
a
mandatory
element
of
completing
the
course.
Even
if
they
overachieved
on
everything
else,
unless
they
apply
their
learning
and
provide
us
with
some
example
of
how
they
use
JMP,
they
can't
get
full
completion
credit.
That's
why
even
though
they
have
the
points,
they're
missing
the
one
critical
element.
These
formulas
are
stored
in
the
table
that
you
can
reference
later,
but
it
gives
you
an
idea
of...
You
can
build
up
some
pretty
complex
formulas,
but
it's
saying,
okay,
if
their
class
score
is
less
than
three,
that
means
they
didn't
attend
enough
classes.
Note
that
class
is
one
of
the
items
missing.
These
double
pipes
are
for
concatenate.
It's
just
putting
a
comma
delimiter
between
the
elements.
Then
it's
saying
if
homework
is
less
than
two,
that
means
they
didn't
finish
the
minimum
number
of
homeworks
and
so
on.
Then
you
can
see
this
data
example.
If
data
example
is
missing,
it's
not
points,
it's
just
black
or
white.
It's
either
there
or
it's
not
there.
You
can
get
a
listing
of
what
they've
completed
and
what
they've
not
completed.
Once
you
have
that,
you
can
quickly
tabulate and
we'll
just
go
to
missing.
Then
I
can
add
email.
Now,
because
I
did
it
in
the
opposite
order,
here's
people
that
are
missing
one
SIPs
Module.
Here's
people
that
are
missing
two,
here's
people
that
are
missing
two
SIPs
modules
and
didn't
do
a
data
example.
Then
I
can
communicate
back
out
to
those
groups
of
folks
exactly
what
they're
missing,
and
so
they
can
either
get
it
done
or
say,
I'm
not
going
to
be
able
to
finish
this.
Close
this. Once
we've
spent
the
time
to
build
up
those
formulas,
again,
we
can
do
some
graphics
based
on
that
finished
column,
and
I
can
see
what
percentage
of
people
by
site
or
location
finished
the
training.
I
can
tabulate
35...
We
had
just
over
50
%
completion
rate.
Not
great,
but
that's reality
and
we
can
circle
back
with
what
were
your
barriers
to
finishing?
Again,
you
can
look
at
your
metrics,
you
can
report
back
on
what's
happening,
all
being
driven
off
of
this
one
data
table
by
using
different
formulas
and
different graphics. It's
very
simple
bar
charts
and
summary
tables.
Hopefully
that
gives
you
a
flavor
of
without
getting
into
advanced
analytics
and
model
building
and
response
surface
modeling,
you
can
get
a
lot
of
mileage
out
of
the
fundamental
features
of
JMP.
It's
really,
in
my
mind,
a
very
good
jumping
point
for
folks.
We've
had
a
lot
of
success
with
getting
people
up
and
running
and
comfortable.
If
you
can
navigate
through
these
tabulations
and
summaries
and
data
cleanups
and
making
some
graphs
and
customizing
the
graphs
and
thinking
about
how
to
annotate
the
graphs
so
they
have
a
quick,
meaningful
message
in
the
most
crisp
presentation,
you
will
have
really
moved
the
needle
on
the
capabilities
of
your
organization, and
hopefully
generated
some
excitement
for
the
use
of
JMP.
With
that,
I
thank
everyone
for
tuning
in.
Hopefully,
when
this
is
posted
in
the
community,
if
you
have
questions,
thoughts
or
suggestions,
certainly
welcome
the
discussion
and
hearing
what
other
people
have
to
say.
But
don't
undervalue
how
far
you
can
get
with
getting
a
broad
base
of
beginners
up
and
running.
They
can
go
out
and
do
great
things,
as
I
said,
by
way
of
summary,
get
people
excited
and
get
people
up
and
running.
The
nice
thing
is
you
get
beginners
and
advanced
practitioners
now
on
the
same
platform.
They
can
start
to
talk
to
each
other.
The
beginners
can
move
along
and
the
advanced
practitioners
don't
have
to
go
backwards.
We're
trying
to
remember
how
Excel
works,
they
can
stay
in
the
platform
where
they
do
most
of
their
analytics.
When
you
do
that,
you
can
join
the
Ninja
community
and
dare
mighty
things
like
flying
helicopters
on
Mars.
Thank
you
very
much.
