Probability problem, independent events

In summary, the probability of at least one out of 15 independent events with a 5 percent chance of occurring is 0.59 or 59%. This is calculated using the binomial distribution formula, where n is the number of events, p is the probability of each event, and r is the number of events that we are interested in.
  • #1
intric8
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If there is a 5 percent chance of getting a particular result, and there are 15 of these events taking place simultaneously with the exact same probability, then collectively, what is the chance that one of these events will take place?

For example, if 15 cars of the same make and model, identical in terms of manufacturing, have a 5 percent chance of developing a fuel injection problem within 5 years.. well, what are the chances that one of these 15 cars will have a problem within 5 years? I'm quite sure you don't just add the probabilities up. Any help with this problem would be most welcome.
 
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  • #2
If n independent events each have probability p of occurring then the probability that exactly r occur is given by the binomial distribution:
[itex]^{n}C_{r}p^{r}(1-p)^{n-r}[/itex]
where [itex]^{n}C_{r} = \frac{n!}{r!(n-r)!}[/itex]
The case you ask about is p = .05, n = 15, r = 1.
But perhaps you meant at least one occurring. In that case we have
[itex]1-^{n}C_{r}p^{r}(1-p)^{n-r}[/itex]
where r = 0. I.e., 1-(1-p)n
 

1. What is the definition of independent events in probability?

Independent events in probability refer to two or more events that have no influence on each other. This means that the outcome of one event does not affect the outcome of the other event. In other words, the probability of one event occurring does not change based on whether or not the other event occurs.

2. How do you calculate the probability of independent events?

To calculate the probability of independent events, you multiply the individual probabilities of each event. For example, if the probability of event A is 1/4 and the probability of event B is 1/3, the probability of both events occurring together would be (1/4) x (1/3) = 1/12.

3. Can independent events also be mutually exclusive?

Yes, independent events can also be mutually exclusive. This means that the two events cannot occur at the same time, but their probabilities are not affected by each other. For example, if you toss a coin and roll a dice, the outcomes of these two events are independent and mutually exclusive.

4. What is the difference between independent and dependent events?

The key difference between independent and dependent events is that the outcome of one event affects the probability of the other event in dependent events, while this is not the case for independent events. In other words, the probability of one event occurring changes based on whether or not the other event occurs in dependent events, but not in independent events.

5. How can you identify if events are independent or not?

You can identify if events are independent or not by looking at their probabilities. If the probability of one event does not change based on whether or not the other event occurs, then they are independent. However, if the probability of one event changes based on the occurrence of the other event, then they are dependent. Additionally, you can also determine if events are independent or not by using a tree diagram or Venn diagram to visualize the relationships between the events.

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