Probability Distributions

In summary, a group of students conducted trials to determine the average waiting time for customers in line at a supermarket, finding it to be 7 minutes. Using this data, the probability of a customer waiting more than 7 minutes in line can be calculated using the exponential distribution function and was found to be approximately 0.368. The use of a Gaussian distribution was not suitable for this situation, as it is not possible to wait a negative amount of time in line. A Poisson distribution or exponential distribution with one parameter would be more appropriate. In another question, the probability of landing on a number between 20 and 45 on a spinner with 100 equal segments was discussed, with the conclusion that the probability would be 0.26
  • #1
danago
Gold Member
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A group of students wish to determine how long, on average, customers are waiting in line at a supermarket before being served.

The students conduct trials and record the times taken. They found that they were kept waiting for an average of 7 minutes.

If a customer goes to that same supermarket, what is the probability they will be waiting more than 7 minutes in line before being served?


Originally, i would have thought to model this situation using a Gaussian distribution, but i am not given a standard deviation to work with, only a mean.

My next thought was to use an exponential distribution function.

[tex]
\begin{array}{c}
P(x > 7) = \int_7^\infty {\frac{{e^{ - \frac{x}{7}} }}{7}} dx \\
= \mathop {\lim }\limits_{a \to \infty } \int_7^a {\frac{{e^{ - x/7} }}{7}} dx \\
= \mathop {\lim }\limits_{a \to \infty } \left[ { - e^{ - x/7} } \right]_7^a \\
= \mathop {\lim }\limits_{a \to \infty } (e^{ - 1} - e^{ - a/7}) \\
= e^{ - 1} \approx 0.368 \\
\end{array}
[/tex]

Does that look right?

Also, if i was given more information, would a gaussian distribution have been suitable?

Thanks,
Dan.
 
Last edited:
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  • #3
Strictly speaking, a Gaussian distribution is never appropriate as a model of a queueing process. How can one wait a negative amount of time in a line?

Queueing models often are modeled as Poisson processes with an underlying exponential distribution. Your assumption was a good one. Moreover, you were given but one statistic. This fits well with the exponential distribution, which takes only one parameter.
 
  • #4
Alright thanks for the replies :)

I have another question i needed help with:

A spinner has 100 equal segments marked out on it, numbered from 1 to 100. What is the probability that the dial lands on a number between 20 and 45?

I would have just thought the probability would have been (45-20)/100=0.25

Is it as straight forward as that, or am i missing something?
 
  • #5
Is that between 20 and 45 inclusive? If so, then there are 26 possibilities. If not, then there are only 24 possibilities.
 
  • #6
Well, think about it logically. If the probability is uniform, that is, each segment has an equal chance of being hit, then it would be that simple.
 
  • #7
danago said:
Also, if i was given more information, would a gaussian distribution have been suitable?

Well, as soon as the problem defines 'average' in a useful fashion...

You assume that 7 minutes is the mean, but...
If 7 minutes is the median, the problem becomes very easy.
If 7 minutes is the mode, then things get a bit more interesting.
 

1. What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of different outcomes occurring in a particular event or experiment. It shows the possible values that a random variable can take on and the probability of each value occurring.

2. What are the types of probability distributions?

There are several types of probability distributions, but the most commonly used are the normal distribution, the binomial distribution, and the Poisson distribution. Each distribution is used to model different types of data and has its own set of characteristics and parameters.

3. How do you calculate the mean and standard deviation of a probability distribution?

To calculate the mean of a probability distribution, you multiply each possible value by its corresponding probability and then add up all the products. The standard deviation can be calculated by taking the square root of the variance, which is the average of the squared differences from the mean.

4. What is the difference between discrete and continuous distributions?

Discrete distributions have a finite or countable number of possible values, while continuous distributions have an infinite number of possible values within a given range. Discrete distributions are typically used to model events with distinct outcomes, while continuous distributions are used to model events with continuous outcomes.

5. How are probability distributions used in real-life applications?

Probability distributions are used in a variety of fields, such as statistics, finance, and engineering, to model and analyze real-life data. They can be used to make predictions, estimate risks, and determine the likelihood of certain events occurring. For example, the normal distribution is commonly used in finance to model stock prices, while the binomial distribution is used in medical research to determine the effectiveness of a treatment.

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