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So I am on lesson 1 of a statistics and probability course so be nice haha. Just learned the fair chance coin toss and about set notation so you can see my level...

I am trying to attach the theoretical information to a real world situation to help the concepts stick in my head, and I just happen to be sitting at a coffee shop.

My question is, is there a model or something that you can create that will predict where someone will sit in a coffee shop given all the crazy amount of conditions. Because if the test was fair then you would just take the size of the group of customers over the total amount of tables or chairs and that would be the probability right? But there are so many other factors...

Suppose the coffeeshop has indoor and outdoor seating and is in a part of the world with four seasons.

Im thinking some conditions that would effect where someone would sit would be things like temperature, sunlight, amount of noise in cafe, amount of noise outside, personal preference, type of seating etc.

Is there a way to quantify and average all these conditions, and come out with a prediction on where someone will sit when they walk in the door.

Any thoughts?

Kevin
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  • Thanks for the response Tim! Is logistic regression and logic regression the same thing? Because when I type logic regression into google it keeps rerouting me to logistic regression, but when I put logic in quotes I did find this source http://kooperberg.fhcrc.org/logic/documents/intro.html. Is this what you were talking about? – Kevin Jun 17 '17 at 15:16
  • I meant logistic regression. Spelling corrector made it wrong. – Tim Jun 17 '17 at 16:01

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What you are saying is that you want to calculate conditional probability that a person will seat a seat in a cafe given some set of other variables. This means that you are talking about conditional probability. The most common way of calculating such models is to use logistic regression for predicting binary (true/false) outcomes, or multinomial regression for predicting multiple categories (sit at seat #1 vs sit at seat #2, vs ...).

Tim
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