Conditional Probability Statistics

Click For Summary

Homework Help Overview

The discussion revolves around a problem involving conditional probability related to hard drive damage during electrical storms. Participants are exploring the implications of given probabilities regarding storms, line hits, and damage outcomes.

Discussion Character

  • Exploratory, Assumption checking, Mathematical reasoning

Approaches and Questions Raised

  • Participants are attempting to apply Bayes' Theorem to calculate the probability of hard drive damage given the occurrence of an electrical storm and the chance of a power line being hit. There are questions about the relevance of certain probabilities in the context of the problem.

Discussion Status

Some participants have provided insights into the problem setup and questioned the relevance of specific probabilities, particularly regarding the occurrence of storms. There is an ongoing exploration of how to correctly apply the probabilities to arrive at a solution.

Contextual Notes

Participants are considering the specific scenario of the next electrical storm, which influences the relevance of the overall probability of storms occurring. There is also a focus on the assumptions made regarding the probabilities of damage and hits during storms.

whitehorsey
Messages
188
Reaction score
0
1. Assume that there is a 50% chance of hard drive damage if a power line to which a computer is connected is hit during an electrical storm. There is a 5% chance that an electrical storm will occur on any given summer day in a given area. If there is a 0.1% chance that the line will be hit during a storm, what is the probability that the line will be hit and there will be hard drive damage during the next electrical storm in this area?



2. Bayes Theorem? P(A|B) = (P(B|A) * P(A))/ P(B)



3. I'm thinking it has something to do with Bayes Theorem...
P(hard drive damage) = 0.5
P(electrical storm) = 0.05
P(line hit) = 0.001

I started off by figuring out what is the probability of the line getting hit:
P(l|es) = (0.001 * 0.05)/ 0.05 = 0.001
but I'm not sure what I should do next and if my P(l|es) is correct.
 
Physics news on Phys.org
The circumstance is 'the next storm', and you know the probability of a hit during such a storm. The probability of a storm is irrelevant.
So you have: prob of a hit; prob of damage given a hit.
 
whitehorsey said:
1. Assume that there is a 50% chance of hard drive damage if a power line to which a computer is connected is hit during an electrical storm. There is a 5% chance that an electrical storm will occur on any given summer day in a given area. If there is a 0.1% chance that the line will be hit during a storm, what is the probability that the line will be hit and there will be hard drive damage during the next electrical storm in this area?
Here is how I would do this problem:
Since the problem says "during the next electrical storm", the "There is a 5% chance that an electrical storm will occur on any given summer day in a given area" is irrelevant- we are given that there is a storm. Suppose there are 1000000 electrical storms. There is a .1% (.001) chance of a line being hit by lightning. So out of 1000000, how many lines are hit by lightning? Of those, 50%= .5 will result in hard drives damaged. So of those 1000000 storms, how many drives are damaged? What proportion is that?
2. Bayes Theorem? P(A|B) = (P(B|A) * P(A))/ P(B)
3. I'm thinking it has something to do with Bayes Theorem...
P(hard drive damage) = 0.5
P(electrical storm) = 0.05
P(line hit) = 0.001

I started off by figuring out what is the probability of the line getting hit:
P(l|es) = (0.001 * 0.05)/ 0.05 = 0.001
but I'm not sure what I should do next and if my P(l|es) is correct.
 
Thank You!
 

Similar threads

  • · Replies 4 ·
Replies
4
Views
2K
Replies
2
Views
1K
  • · Replies 6 ·
Replies
6
Views
2K
  • · Replies 10 ·
Replies
10
Views
2K
  • · Replies 6 ·
Replies
6
Views
3K
  • · Replies 30 ·
2
Replies
30
Views
5K
  • · Replies 9 ·
Replies
9
Views
2K
Replies
2
Views
4K
  • · Replies 9 ·
Replies
9
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K