Uniform distribution Probability

In summary: If those were the probability functions, then the probability of John leaving before Mary arrives would be the integral from 0 to 30 of f(t)g(s) dsdt, and the probability of John arriving after Mary leaves would be the integral from 45 to 60 of f(t)g(s) dsdt.
  • #1
SMA_01
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0
John is going to eat at at McDonald's. The time of his arrival is uniformly distributed between 6PM and 7PM and it takes him 15 minutes to eat. Mary is also going to eat at McDonald's. The time of her arrival is uniformly distributed between 6:30PM and 7:15PM and it takes her 25 minutes to eat. Suppose the times of their two arrivals are independent of each other. What is the probability that there will be some time that they are both at McDonald's, i.e. their times at McDonald's overlap.

So let T= John's arrival time
and
S=Mary's arrival time


I don't really know where to go from here. Can anyone provide hints in the correct direction?

Thanks
 
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  • #2
Maybe try to find the probability that the times do not overlap. This can happen one of two ways: either John leaves before Mary arrives, or John arrives after Mary leaves. What are the probabilities of these two events?
 
  • #3
jbunniii said:
Maybe try to find the probability that the times do not overlap. This can happen one of two ways: either John leaves before Mary arrives, or John arrives after Mary leaves. What are the probabilities of these two events?

Thank you. I'm a bit confused on how to find the density function, though.
For John, I'm guessing f(t)=1/60 for 0<t<60 and 0 otherwise
For Mary, g(s)=1/45 for 30<s<75 ?
Or is that completely off?
 
  • #4
If t represents the number of minutes after 6:00, then, yes, those are correct.
 
  • #5
HallsofIvy said:
If t represents the number of minutes after 6:00, then, yes, those are correct.
... and if those functions are the density functions of the arrival times, as opposed to representing the probabilities of being present at time t.
 

FAQ: Uniform distribution Probability

1. What is a uniform distribution probability?

A uniform distribution probability is a type of probability distribution where all possible outcomes have an equal chance of occurring. This means that the probability of each outcome is the same, and there are no biases or preferences for certain outcomes. It is often represented by a flat, rectangular curve.

2. How is a uniform distribution probability calculated?

A uniform distribution probability can be calculated by dividing 1 by the total number of possible outcomes. For example, if a dice is rolled, the probability of getting any number from 1 to 6 is 1/6 or 16.67%.

3. What are some real-life examples of uniform distribution probability?

Some real-life examples of uniform distribution probability include flipping a coin, rolling a dice, or drawing a card from a deck. In these scenarios, each outcome has an equal chance of occurring, making it a uniform distribution.

4. How is a uniform distribution probability different from other types of probability distributions?

A uniform distribution probability is different from other types of probability distributions, such as normal or binomial distributions, because it assumes that all outcomes are equally likely. Other distributions may have certain biases or preferences for certain outcomes, making them non-uniform.

5. What are the applications of uniform distribution probability in science?

Uniform distribution probability is commonly used in scientific research, particularly in experiments and surveys where random sampling is necessary. It is also used in fields like finance, physics, and statistics to model and analyze certain phenomena.

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