Solving Coffee Shop Customers Problem: Probability Mass Function

  • Thread starter cosmonova
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In summary, the conversation discusses a problem involving a coffee shop that is open from 9.00 to 1.pm. with an expected 8 customers staying for half an hour. The problem seeks to find the probability mass function of the number of customers present in the shop at a certain time, but without more information, it is difficult to determine the function. Two plausible functions are a uniform distribution for the time each customer enters and a Poisson distribution for the number of customers entering with an average of 8 customers staying for half an hour. The problem also involves finding the probability function for n customers over a period of time with each choosing a specific interval of time.
  • #1
cosmonova
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I've been working on this problem for quite a long time now but I'm not being able to solve it. It goes as follows :

A coffee shop opens his doors from 9.00 to 1.pm.
supposedly 8 customers will enter this shop and stay for a period of half an hour.
Let X be the number of customers present in this shop at a certain time t, what is the probabilty mass function of X ?

I would be very much grateful if you could please help me in solving this problem.
Thank you.
 
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  • #2
? All you say is that 8 customers will enter this shop and stay for a period of half an hour. Without more information, we can't say what the probability function is- it might well be that the 8 customers are guarenteed to come at 9 and leave and 9:30!

I could see two plausible probability functions here: the simpler (in concept, probably not in calculation) is that the time each customer enters the shop is uniform over the 3 1/2 hour period from 9 to 12:30.

However, it is more common for problems like this to obey a Poisson distribution. I suspect what you intend here is a Poisson distribution for the number of customers entering, with an average of 8 customers, each staying for 1/2 hour.
 
  • #3
Thank you for your reply.
The problem here is that the number of customers that are present at the same time (which is X) could take the following values : 0,2,3,4,5,6,7 and 8.
Hence they could as you said be all present at the same time (hence, X=8), as well as they could enter each for half an hour and leave , without anyone meeting the other (Hence X=0).
The problem actually is to find the probability mass fuction of X for n customers over a period t of time, with each choosing a interval of time of length l , where sum of all intervals = t. But I just gave a simple example here.
Thank you again for your reply and hope I have made the problem clearer now.
 

What is a probability mass function (PMF)?

A probability mass function is a mathematical function that assigns a probability to each possible outcome of a discrete random variable. It is often represented as a table, graph, or formula.

Why is a probability mass function important in solving coffee shop customer problems?

A probability mass function allows us to understand the distribution of our data and make predictions about future outcomes. In the context of coffee shop customers, it can help us understand the likelihood of certain customer behaviors and make informed decisions about how to best serve them.

How do you calculate a probability mass function?

To calculate a probability mass function, you need to know the possible outcomes of a discrete random variable and their corresponding probabilities. Then, you simply divide each probability by the total number of possible outcomes to get the PMF for each outcome.

What is the difference between a probability mass function and a probability density function?

A probability mass function is used for discrete random variables, while a probability density function is used for continuous random variables. This means that a PMF calculates the probabilities for specific outcomes, while a PDF calculates the probabilities for a range of values.

How can a probability mass function help improve a coffee shop's customer service?

By understanding the probabilities of different customer behaviors, a coffee shop can make data-driven decisions to improve their customer service. For example, if the PMF shows that a certain type of customer is more likely to order a specific drink, the coffee shop can ensure they have enough ingredients and staff to serve that drink efficiently.

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