Modeling

One of the biggest hurdles to overcome when dealing with statistics is the vocabulary and the very intimidating use of Greek symbols in the equations …er models.

Modeling is something we all do in everyday living and decision making. It defines a pattern for something to happen or that has already happened.

Note: In the world of statistics, the latter is common during statistical analysis, while the former is in the planning stages for a study or in Predictive Statistics for business planning.

  • Time to Wake Up For Work.TTWUFW = Start Work At Time – (Morning Personal Prep Time + Drive Time To Work + Amount Of Buffer Time To Not Be Late).
  • Cost of groceries from the store.COGFTS = cost of all items purchased.

Each of these can be expanded to better approximate the actual outcomes.

Lets’ take the Time to leave For Work model as an example.

We ask ourselves when creating this model “What are the components that contribute or effect this outcome.

You just got hired for a new job across town and want to know what time do you need to wake up in the morning to arrive there at a comfortable time so you’re not late.

You begin working on your model based on what you know and make a list of the variables that affect the solution.

Variables:

We place the outcome on the left side of an equation then the equal sign and on the right side we list all the components that contribute or effect that outcome. We separate each component with a plus sign (+) showing it adds/contributes to the outcome.

If we wrote this out we would say…

What time do you need to wake up for work (TTWUFW) If you Start work at 8:00 am (SWAT) and it takes you 30 minutes morning prep time (MPPT) and it takes 20 minutes to drive to work (DTTW) and you like to be at work 15 minutes before you start working (AOBTTNBL).

In equation form using shortened labels for each component we get.

TTWUFW = SWAT + MMPPT + DTTW + AOBTTNBL

Wait that doesn’t look exactly like the first equation! You’re right but this is closer to the exact form for statistical model even though we all know from getting ready for work some things negatively contribute to the outcome. Let’s not worry about the exact form quite yet, we will return to that later.

In the model  for what time do you need to wake up for work  begin with what time do you need to start work and subtract from that all the component times  for each of the pre-work activities and the result is what time you need to wake up for work.

To get a more accurate “prediction” of when we need to leave for work we expand as best we can all of the components to include their actual parts and rewrite the equation.

TTWUFW = SWAT – ( Time to Shower + Time to Shave + Time for personal Hygiene + Time to Dress + Time for Breakfast/Coffee + Time to Warm up Car + Time for Drive to Work + Amount of Buffer to Not Be Late + Unknown Unplanned Event time)

There is a practical limit to how detailed your “model” needs to be but it is easy to see that the time used in any component effects what time you will need to wake up for work.

 

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