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If I Only Had Time to Work with Timestamp Data…I Would Learn So Much About My Process! - (2023-US-30MP-1491)

Working with timestamp data (dates, times, or datetimes) can be like wrestling a pig. It can be struggle for many reasons, including knowing the appropriate modeling type to use, how to process data with non-standard datetime formats, and how to easily perform datetime calculations.

With sensors becoming more prevalent in measurement systems and manufacturing equipment, learning how to work with datetime data is becoming increasingly more important if you want to use this data to understand relationships between process variables and critical quality endpoints. 

You do not have to get dirty or give up using this data altogether if you learn the ABCs of how this type of data is stored in JMP and how to process it. JMP makes working with timestamp data easier. In this presentation, I focus on the tools that I have seen customers benefit from the most when they are working with datetime data. 



All  right. Hi.

My  name  is  Wendy,

and  I'm  a  technical  lead  working  with health  and  Life  Sciences  in  the  Midwest.

My  motivation  for  this  talk  comes  from working  with  JMP  customers  and  prospects

across  a  broad range  of  industries.

When  I  reflect  on  my  customers' questions over  the  past  five  years,

the  biggest  opportunity  with  data  analysis

has  been  in  using  all  the  data  that's being  collected  to  make  decisions.

With  sensors  becoming  more  prevalent in  measurement  systems

and  manufacturing  equipment,

more  data  is being  collected  over  time.

It's  cheaper  to  collect and  store  the  data,

and  as  a  result,  scientists and  engineers  are  often  swimming  in  data.

They  want  to  use  the  data

to  better  understand  their  processes, to  make  better  products,

but  they  have  a  knowledge  gap  when  it comes  to  how  to  process  all  the  data.

This  talk  is  intended to  be  an  introduction

in  understanding  how to  work  with  data  collected  over  time.

What  do  I  mean  by  timestamp data  or  data  collected  over  time?

I  have  some  examples  here.

It  could  just  be  a  time or  a  date  or  both  a  date  and  a  time.

This  data  can  be  formatted in  many  different  ways.

When  I  say  timestamp  data, I'm  referring  to  all  of  these  variations,

and  it  really  is  the  granularity at  which  your  data  is  stored.

It's  a  pretty  broad  category,

but  this  is  what  we're talking  about  today.

The  title  of  my  presentation  is,

If  I  only  had  Time to  Work  with  Timestamp  Data,

I  would  learn  so  much  about  my  process.

I  would  almost  retitle  that  to  say, If  I  only  had  the  knowledge.

I've  seen  people  struggle with  working  with  this  type  of  data,

like  wrestling  with  a  pig,

because  they  simply  don't  have  some of  the  fundamentals

of  understanding  how JMP  recognizes  this  data

and  how  to  marry  data  together by  timestamp.

It  doesn't  have  to  be  like  this.

Today,  what  I'm  hoping  you walk  away  with  are  two  things.

One  is  just  some  basics about  timestamp  data,

and  then an  introduction  to  JMP   Query Builder

if  you've  never  used  this  before, to  join  Timestamp  Data.

All  right,  so  let's  start  with  the  ABCs.

I'm  bringing  open  a  spreadsheet  here,

and  we're  going  to  bring this  data  into  JMP

to  just  talk  about  some  of  the basics  when  it  comes  to  timestamp  data.

I've  got  three  columns  here.

We're  first  going  to  focus on  start  date  and  end  date.

Excel  is  a  very common  way  that  raw  data  is  stored.

Let's  bring  this  into  JMP.


Let's  first  focus on  the   Start and End date.

I'll  hide  my  special  column  here just  for  a  second.

Let's  first  focus  on the  way  that  this  data  came  in.

All  of  these  columns  actually  came  in.

JMP  is  recognizing  them  as  nominal  data.

The  first  thing  you  want  to  know about  timestamp  data,

if  you  want  to  work  with  it,

is  that  it  should  be  stored  in  JMP as numeric  and  continuous.

Let's  first  open  up  Start  Date and  just  take  a  look  here.

JMP  recognized  it  as  character and  so  therefore  it  made  it  nominal.

We  need  to  communicate  to  JMP  that  this  is

date  data  or  timestamp  data  by  changing it  to  numeric  and  continuous.

That  is  the  type  of  data,

dates,  times  and  timestamp  data need  to  be  coded  in  JMP.

Now  I'll  keep  this  open and  I'm  going  to  click  apply.

You  can  see  that the  values  change .

So  this  is  correct .

This  looks  nonsensical  to  us,

but  this  is  the  other  thing  you  need to  know  about  timestamp  data

is  the  way  it's  stored  in  JMP

is  the  number  of  seconds from  a  reference  date.

It's  specifically  the  number  of  seconds from  January  1st  1904 .

You  don't  need  to  remember the  January  1st  1904 .

You  just  need  to  remember  that  dates, times  and  timestamps

are  stored as  the  number  of  seconds.

The  way  that  we  make  it  look  sensible to  us  is  by  changing  the  format.

I  almost  think  of  the  format as  a  mask.

This  is  correct.

The  software  understands  this,  but  I want  to  be  able  to  read  it  easily.

Now  let's  work  with  the  format.

I'll  go  to  this  drop  down  and  I  am just  going  to  call  out  these  three  menus.

There's  a  date  menu, a  time  menu,  and  then  a  duration.

If  you're  working  with  timestamp  data,

these  are  the  three  menus that  are  going  to  be  relevant  to  you

in  order  to  format  the  data.

We'll  talk  about a  special  case  in  a  second.

Let's  format  this  in  the  same  way that  it  appears.

Because  this  is formatted  as  a  date,

sorry,  day  and  then  a  month and  then  a  year,

let's  make  the  selection that  preserves  that.

I'm  going  to  select this  particular  option,

click  'Apply'  to  confirm that  that's  what  I  want  to  see.

Now  I'm  good  to  go.

Notice  again  in  the  columns  area.

Now  start  date  is  numeric  and  continuous and  now  JMP  knows.

January  26  is  five  days  after  the  21st.

It  doesn't  know  that  right now  for  end  date.

Now  let's  do  the  same thing  with  end  date.

This  is  going  to  allow  us to  subtract  the  two.

To  do  date  time  math.

I'm  going  to  come  in  here  again, numeric  and  continuous.

We'll  click  'Apply' .

That's  the  number  of  seconds from  that  reference  date.

Now  I'm  going  to  change  the  format.

Okay,  so  now  I  have  numbers  that  I can  perform  math  with .

I  can't  perform  math  on  character  data.

If  you  want  to  do  something  like calculate  a  date  difference,

now  we  can  do  that  now  that  our  data is  being  stored  and  JMP  properly.

Let  me  show  you  one way  that  you  can  work.

Do,  let's  say,  column  math  with  dates.

I'm  going  to  select  these  two  columns.

I'm  going  to  right  click,  and  I'm  popping into  this  new  formula  column  menu.

I  can  ask  JMP  to  help me  write  this  formula.

Because  I  have  two  columns  selected,

I'm  going  to  go  to  this  combined  menu  and tell  JMP  I  want  to  take  the  difference.

I'll  take  the  difference

in  reverse  order  because  I  want end  date,  minus  start  date.

There  you  go.

Again,  I'm  not  alarmed

because  I  remember  that JMP  stores  this  type  of  data  in  seconds.

To  convert  this  to  another  unit, let's  say  days,

we  just  need  to  work with  this  formula  a  little  bit  more.

Let's  do  that, I'm  going  to  hit  the  plus  sign.

Now  I'm  in  the  Formula  editor .

We  can  convert  this  to  days  by  dividing this  by  60  seconds  are  in  a  minute,

60  minutes  are  in  an  hour,

and  then  there's  24  hours  in  a  day.

I'll  click 'Okay' .

Now  we  can  confirm,  right,  that  this is  the  difference  between  the  two.

You  can  just  do  some  mental  math  here.

It's  not  too  challenging.

That  looks  good.

I  want  to  present  you  another  way to  do  date  time  math  or  timestamp  math.

We  could  use  the  Formula  editor.

Let's  go  ahead  and  create  a  new  column.

I  will  right  click  and  go to  the  full  Formula  Editor.

There  is  a  more  comprehensive  list  of  date time  functions  in  the  full  Formula  Editor

than  you're  getting in  the  data  table  when  you  right  click.

This  date  difference,

if  I  hover  over  it,  you  can get  a  peek  of  what  it  offers  you.

It  allows  you  to  specify  an  interval  name.

I'll  double  click  on  that to  bring  it  into  the  view.

Now  let's  tell  JMP  that  we  want  to  take that  date  difference  with  the  two  dates,

and  then  we  can  specify the  units  that  we  want .

We  don't  have  to  do the  60  X  60  X  24.

I'll  put  the  end  date  first,

start  date,  and  then  my  interval  name does  need  to  be  in  quotations.

This  little  hover  help  does let  you  know  that  that's  the  structure.

Let's  click  'Okay'.

Now  I  can  see  I went  backwards  on  that.

Let's  double  click  in  here.

We  just  need  to  swap  those  two.

Start  date  should  be  the  first  one.

There  you  go.

These  are  the  same  values  we  got when  we  performed  that  division.

Two  different  ways to  perform  these  calculations.

But  the  big  takeaway  is

to  familiarize  yourself with  some  date  time  formats.

Not  formats,  but  calculations.

You  can  do  that  by  exploring these  quick  formulas.

It's  via  a  right click  in  the  data  table.

There's  a  date  time  menu  here

or  in  the  full  Formula  Editor

that's  going  to  be  this  very  large menu  of  various  date  time  formulas.

Okay, so  now  let's  wrap  up  the  ABCs

by  looking  at  this  special  date time  column  that  I  hid.

I'll  unhide  it  so that  we  can  focus  on  it.

Now  this  is  a  full  week day  of  the  week  name.

You  see  the  date  and  the  time.

Let's  try  formatting  this,

or  I  should  say  communicating  to  JMP how  this  data  should  be  stored.

I'm  going  to  right  click  and  go

into  column  info  just  like  we did  with   Start and End date.

I'm  going  to  go  to  numeric and  continuous .

Because  that's  how  this  type of  data  needs  to  be  stored  in  JMP.

I'm  going  to  click  apply.

Now  I'm  disappointed  because  JMP  has basically  deleted  all  those  values.

It's  telling  me  that  it  does  not  recognize

that  information  as  being  a  date .

It worked,

we  got  seconds  when  we  did  that with  Start  and  End  Date,

but  we're not  getting  that  here.

This  tells  us

that  we  need  to  work  a  little  bit  harder because  this  is  a  special  format

to  tell  JMP  that  this  is  time data  or  date  time  data.

We're  going  to  leverage  and  undo here  to  get  our  data  back.

Now  I'll  right  click  go back  into  column  info

and  here  let's  first  do numeric  continuous.

Here  we're  going  to  go  to  the  format  menu

and  I'm  going  to  introduce  format  pattern.

We  talked  about  dates,  time  durations.

Here's  where  you  want  to  go  if  you  have a  special  situation  like  we  have  here.

Now  I'm  going  to  say set  format  pattern.

Now  the  process  is  about communicating  to  JMP.

Each  element  in  that  it  is a  date,  time  duration  or  other.

See  this  other  menu.

That's  what  we're  going  to  do  is  each piece  we're  going  to  tell  JMP  what  it  is.

Wednesday,  I'm  kind of  focusing  on  this  first  line.

What  is  that?

Make  this  a  little  bit  bigger.

Well,  that's  the  day  of  the  week .

It's  the  full  name.

I'm  going  to  make  that  selection.

Then  I  do  have  to  tell  JMP. "Okay,  well,  I  have  a  comma  next,

and  a  space".

And  now  I  have  the  full  month  next.

I'm  just  scrolling and  finding  that  in  the  menu.

Here  is  month  long  name.

I'm  going  to  make  that  selection.

Again,  I  have  a  space and  now  I  have  a  two  digit  day

and  then  I  have  a  comma and  then  I  have  a  year.

It's  a  four  digit  year .

You  can  see  it  being  a  little  bit of  a  preview  of  what  I'm  specifying  here.

That's  really  the  process.

Just  to  kind  of  shorten  this  up, I'm  going  to  do  my  little  cheat  thing

and  I'm  just  going  to  paste in  the  full  thing .

We  built  it  up  to  year, but  the  process  would  be  the  same  for

telling  JMP  about the  hour,  minute  and  AM, PM.

This  is  a  game  changer  for  those of  you  who  have  special  formatting.

I  do  see  this  more  and  more  often  with

the  various  pieces  of  equipment that  are  collecting  data  over  time.

All  right,  so  now  let's  look  to  see,

now  JMP  does  recognize  this as  date  time  data.

We  have  numeric  and  continuous,

and  we  can  see  via  the  formatting that  this  data  is  correct.

Okay,  let's  do  some  review, and  then  we'll  push  on  to  part  2  here.

What  have  we  talked  about  so  far?

JMP  recognizes, let's  call  it  timestamp  data  broadly

as  the  number  of  seconds.

You  don't  need  to  remember the  January  1st 1904 .

Just  know  that  it's  the  number  of  seconds.

The  data  should  be  stored  as  numeric and  continuous,

and  the  format  is  the  way  that  you  make  it legible  or  understandable  to  you.

JMP  understands  it  as the  number  of  seconds.

We  need  to  view  it  as  a  specific  type of  format  so  that  we  can  understand  it.

The  last  thing  I'll  say  is  develop a  familiarity  with  date  formulas.

We  had  a  chance  to  do  that  both in  the  data  table  with  those  quick

formulas  and  then in  the  full  formula  editor.

Oh,  yes,  I  said  that  was  the  last  thing.

But  that  special  case  we  addressed

with  the  special  date  times,  if that's  your  situation,

you  can  deal  with  it using  set  format  pattern

so  that  you  can  ultimately  work  with  that special  formatting  of  timestamp  data.

Okay,  now  we're  going  to  go  to  part  two,

and  this  is  where we  are  going  to  do  something  that

I  have  seen  lots  of  customers struggle  with,  which  is,

they  want  to  align  data by  timestamps,

but  they  can't  do  it because  of  a  lack  of  awareness  of  tools.

That  tool  is  going to  be  JMP   Query Builder.

We're  going  to  just  dive  into  an  example

that  I  think  will illustrate  this  particular  challenge.

All  right,  so  here's  a  case  study.

We  are  making  this  product  called Forever  Young  Elixir,

who  doesn't  want  that  product.

To  make  Forever  Young  Elixir,  here's a  little  bit  of  a  sketch  of  that  process.

We  start  with  raw  materials.

Then  these  raw  materials go  into  a  pressure  step.

This  is  very  abstract.

Then  they  go  into  a  temperature  step,

and  then  we  get  our  finished  product.

What  we're  most  concerned  about

with  respect  to  this  finished product  is  potency .

We  want  high  potency in  our  finished  product.

Every  finished  batch,

we  take  a  sample  and  we  measure  potency.

So  here  you  can  see  this  is  a  sample of  the  data  that's  being  collected.

This  first  batch  finished at  10:00  AM  on  6:28,

and  it  had a  potency  of  95.41%,  etc .

I  have  one  row  per  batch  because  I get  one  potency  measure  for  each  batch.

I  also  collect  temperature  data  over  time.

There's  a  sensor  on  this  temperature  step that  continuously  measures  the  temperature

that  let's  call  it  my  product in  process  is  going  through.

I  have  the  same  thing for  my  pressure  step.

There's  a  sensor  here  too, and  I'm  collecting  pressure  over  time.

I'm  making  this  product  and  I  had some  ideas  about  how  to  set  the  process.

But  I  want  to  know, can  I  make  this  better?

Can  I  get  more  batches that  are  of  higher  potency,

more  consistently higher  potency  batches?

I  want  to  use  my  sensor  data

to  figure  out  is  there  a  correlation between  the  temperature  and  potency

and  ultimately  also  pressure  and  potency,

and  where  should  I  try  to  get that  process  to  sit  at  in  these  steps

to  get  to  maximize  potency .

That's  what  we're  marching  towards.

I  want  to  identify

temperature  settings  that  are  going to  give  me  the  highest  potency.

Let's  focus  on  this  graph  here.

I'm  plotting  every  single  batch  here

and  its  potency  value  on  the  y  axis, and  I  have  the  average  temperature

that  it  was  experiencing in  that  temperature  step.

We're  going  to  focus  on  temperature,

but  these  steps  would  be  analogous for  that  pressure  information  as  well.

This  is  what  I'm  marching  towards.

Being  able  to  build  a  graph  like  this allows  me  to  see,

oh,  I  can  maybe  maximize  potency

by  maintaining  a  temperature  setting

of  between  63  and  maybe  68 .

This  is  where  I'm  getting the  highest  potency  values.

We  need  to  ultimately  get  a  data  table that  has  both  potency  values

and  temperature  values  so  that  we can  perform  this  correlation.

A  little  bit  of  an  asterisk  here  only

because  there  are  certainly  other ways  to  analyze  this  type  of  data.

We're  going  to  take  the  average,

but  there  are  certainly  other  tools  and JMP  and  other  approaches  one  could  take

to  analyze  this  type  of  data.

We're  not  going  to  address  that  here.

Just  kind  of  putting  it  out there  as  another  thing  to  explore.

We're  going  to  stay  focused  on  this

data  preparation  aspect of  working  with  this  data.

When  we  have  this  data  aligned, let's  focus  on  batch  1, 2, 3, 4, 5.

We  have  one  potency  value for  batch  1, 2, 3, 4, 5,  that's  the  95.41,

and  that's  why you  see  it  across  all  the  rows.

I  only  have  one  value  for  that  batch,

but  I  have  many  values for  temperature .

It's  just  sort  of  streaming and  being  collected.

How  do  I  do  this  alignment?

What  do  you  do,

when  you  don't  have  a  batch  ID?

That's  really  kind of  the  crux  of  the  issue.

When  we  have  unique  identifiers,

we  can  join  that  data by  those  unique  identifiers.

When  we  do  not,

how  do  we  address  this  problem?

That's  what  we're going  to  talk  about  now.

Really  the  first  step before  even  getting  into  JMP

is  to  think  about  the  process.

With  a  hypothetical  batch finishing  at  09:10  AM,

when  is  it  inside of  this  temperature  step?

What  is  the  relevant  time  range

for  a  finished  batch that  finishes  at  09:10  AM?

That's  not  a  software  question.

This  is  a  let's  understand the  process  question .

In  talking  to  the  manufacturing  engineers,

we've  determined  that  a  batch  spends five  minutes  in  this  temperature  step

before  it becomes  a  finished  product.

We're  going  to  use  this  five  minutes,

and  we're  going  to  calculate from  the  finished  product

timestamp  and  determine  that for  a  9:10  finish,

the  product  or  the  pre  made  product

started  in  this  temperature  step at 9:05AM.

That  is  the  work  ahead  of  us in  the  software  is  to  associate

the  sensor  data  using  that  five minutes  to  that  end  product .

We've  got  the  finished  time for  the  Forever  Young  Elixir  batch,

and  we  need  to  collect  the  sensor  time that's  relevant  for  that  finished  product.

Okay,  so  let's  dive  into  the  software.

Here  is  our  potency  data.

We  have  one  row  per  batch,

100  batches  in  here.

Let's  go  take  a  look  at  the temperature  sensor  data.

This  is  being  collected almost  every  second.

We  have  500  rows  in  here,

so  no  batch  ID.

this  is  the  challenge

that   Query Builder  is going  to  help  us  with.

If  I  scroll  down,  you  can  see, oh,  some  missing  values,  maybe.

I  don't  know  if  the  sensor  was  down, that  can  happen.

Our  first  step  really  is

we're  going  to  work  with  this  temperature data  I'm  sorry,  the  potency  data.

We're  going  to  calculate  a  start  time.

A  start  time  meaning  when  did  it start  being  in  that  temperature  step.

Let's  do  some  labeling  because  we're going  to  have  a  lot  of  timestamps  here.

I'm  going  to  call  this batch  finish  timestamp.

Now  let's  do  some  math.

We're  going  to  subtract  five  minutes from  the  batch  finish  timestamp.

Let's  go  to  the  formula  editor, and  I'm  going  to  select

batch  finish  timestamp, and  I'm  going  to  say  minus  five

now,  because  I  know,  we  all  know  now that  JMP  likes  to  work  in  seconds.

This  five  won't  work.

I  would  need  to  change  five  to  seconds,

or  I  could  ask  JMP to  do  that  for  me .

I  could  change  this  to  5 X  60.

Or  I  can  come  here  and  say that  five  is  in  minutes.

Now  let's  check  it  out.

Again,  we're  not  alarmed  because  we  know

we  just  need  to  change the  mask,  right,  the  format.

Let's  change  it  to  a  timestamp.

We'll  make  it  consistent with  what  we  have.

There  we  go. We'll  call  this  temperature  start  time.

For  batch  1, 2, 3, 4, 5, that  finishes  at  10:00 AM

we're  going  to  start  collecting that  sensor  start  time  at  9:55.

Let's  go  pop  over  to  Potency data,  sorry,  temperature  data.

If  you're  used  to  using  the  join function  in  the  Tables  menu,

you  don't  want  to  do  that  for  date data  or  timestamp  data.

What  you  want  to  use is  JMP   Query Builder.

That's  what  we're  going  to  use.

I'm  going  to  join  temperature sensor  data  with  potency  data.

I'm  going  to  double  click  in  here to  set  the  join  criteria.

This  is  going  to  allow  us  to  set  two criteria  for  aligning  this  data.

The  first  one  is  I  want  to  collect temperature  data

that's  greater  than my  temperature  start  time,

or  I  should  say  greater  than  or equal  to  my  temperature  start  time.

In  my  potency  data  that  I  calculated.

I  have  a  second criterion  that  I  have,  which  is

I  want  to  stop  collecting  data for  a  particular  batch

and  use  the  batch  finish  time.

We  did  greater  than  before and  now  it's  a  less  than.

I'll  click 'Okay' .

Now  let's  build  the  query.

We've  told  the  software  how  we  want to  align  these  rows

and  now  we're  going  to  go  to  the  next window,  which  is  the  build  query  part.

First  step  is  we  need  to  tell  the  software

which  columns  we  want in  the  resulting  table.

I'll  just  add  all  of  the  columns

and  you  can  see  you  get this  preview  down  here,

right,  so  we  can  start  to  see do  some  sanity  checks  on  the  data.

This  is  the  join  that  we're  about  to  do.

Does  it  look  correct?

I  think  what's  worked  well  for  me  is

to  focus  on  a  single  batch so  we  can  pick  on  1, 2, 3, 4, 5.

This  first  batch  here,

I  can  see  that  I  have a  single  potency  value.

Then  I  can  also  see  that

I've  collected  temperature  values  that  are inside  that  window  of  9:55  and  10:00  AM.

This  looks  good.

Now,  because  I  just  have one  potency  value,

I  do  need  to  summarize that  temperature  data,

if  I  want to  create  a  correlation.

This  is  where  we're going  to  use  an  average.

I  could  run  the  query  and  get  this  raw data  and  then  summarize

using  table  summary.

But  I  want  to  show  you  how  you can  do  this  in   Query Builder.

We'll  stay  in  this  window

and  we're  going  to  use  this aggregation  option  for  temperature.

We  need  to  get  rid  of some  of  our  columns  here

because  we  don't  actually want  this  level  of  granularity.

We  want  this  all  at  the  batch  ID  level.

I'm  going  to  get  rid  of  timestamp,

which  is  the  timestamp for  the  temperature  sensor.

I  can  keep  the  batch  finish  timestamp

because  there's  only one  value  for  each  batch  ID

and  I  don't  necessarily need  this  temperature  start  time.

We  can  reorganize  this in  a  more  logical  way .

Maybe  we  start  with  batch  ID, we  preserve  the  finish  time

and  then  we've  got  our Potency  and  our  temperature.

Now  we're  going  to  go  to  the  temperature one  and  take  an  average .

Certainly  you  could  calculate  additional statistics  as  well.

Maybe  you  want  to  also  look  at,

you  could  look  at  a  min  value.

You  could  look  at  a  max  value .

There  are  many  other  ways to  kind  of  look  at  this  data.

We're  just  going  to  stick  to  temperature

so  it  looks  like  we  maybe  had some  sensor  data

where  we  didn't  have batch  finished  batch.

Okay,  so  this  looks  good. Let's  just  do  one  more  sanity  check.

Batch  1, 2, 3, 4, 5, the  average  temperature  is  56

and  we  have  this  potency  value.

You  could  certainly  do  more  checks.

That's  something  I  would  recommend  is

just  to  go  to  your  raw data  and  just  confirm.

But  this  looks  good.

We're  ready  to  create  the  table.

At  this  point, I'm  going  to  say  run  query

and  we're  ready  to look  at  correlations .

I've  got  this  blank  row, I  could  just  delete  this  guy,

but  now  I  have  Potency  and  average temperature  for  each  batch.

Now  I  can  go  into  graph  builder and  look  at  that  relationship.

Here's  potency,  here's  temperature

and  maybe  we  add  a  model  to  it .

Let's  change  this  to  a  line  of  fit.

It  certainly  doesn't look  like  it's  linear,

probably  better modeled  as  quadratic.

We're  done.

We  could  add  some  more  statistics  here, but  this  is  really  a  nice  starting  place

where  we  can  start  to  see,

to  make  statements  like I'm  seeing  higher P otency  values

when  temperature  is

maybe  between  64  and  68.

I  should  go  back  to  my  process and  try  to  see  if  I  can  set

the  temperatures  to  stay within  that  operating  window.

Okay,  so  with  that,  I'll  conclude

and  again,  just  remember,

when  you're  working  with  timestamp  data and  you  want  align  rows,

think  of  JMP   Query Builder, don't  think  of  Tables  Join.

You  really  need  to  set  to  be  able  to  set

two  criterion  for  the  boundaries in  order  to  align  that  data.

Just  as  a  wrap  up, I'll  do  some  shameless  promotion.

I've  documented  this case  study  in  a  blog  post.

If  you'd  like  to  review  it, you  can  take  a  look  here.

I  even  include  the  data  set  in  here as  well  so  you  can  recreate  this.

There  are  some  nice  references as  well  to  some  other  blog  posts

that  others  have  written  on  this topic  of  working  with  timestamp  data.

All  right,  thank  you  very  much.