What percentage of non-fatal accidents are caused by drivers who do not text?

In summary, out of 200 accidents attributable to drivers who text, 4 are fatal and 196 are not fatal. Therefore, the percentage of non-fatal accidents caused by drivers who do not text is 98%.
  • #1
DotKite
81
1

Homework Statement




A study of texting and driving has found that 40% of all fatal auto accidents
are attributed to texting drivers, 1% of all auto accidents are fatal, and
drivers who text while driving are responsible for 20% of all accidents. Find
the percentage of non-fatal accidents caused by drivers who do not text.

Homework Equations





The Attempt at a Solution



Let T denote texting while driving and let F denote fatal accidents.

P(F|T) = .40
P(F) = .01
P(T) = .2

I guess we are trying to find
p(F[itex]^{c}[/itex]|T[itex]^{c}[/itex]
= (p(F[itex]^{c}[/itex][itex]\bigcap[/itex] T[itex]^{c}[/itex]) / p(T[itex]^{c}[/itex]

We know p(F|T) = p(F[itex]\bigcap[/itex]T) / p(T) = 0.4 => p(F[itex]\bigcap[/itex]T) = 0.08

Also p(F[itex]^{c}[/itex] [itex]\bigcap[/itex]T[itex]^{c}[/itex]) = 1 - p(F[itex]\bigcup[/itex]T)

p(F[itex]\bigcup[/itex]T) = p(F) + p(T) - p(F [itex]\bigcap[/itex]T) = .01 + .2 - .08 = .13

I am going to stop here because when I start plugging in everything I have I wind up with the wrong answer. Is ther an assumption I have wrong or have interpreted, as usual, the problem wrong?
 
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  • #2
P(F|T) = .40
That would be "For texting drivers, 40% of all accidents are fatal", which does not match the problem statement.
 
  • #3
So would it be p(F[itex]\bigcap[/itex]T) = .40?


You got any hints?
 
Last edited:
  • #4
DotKite said:
So would it be p(F[itex]\bigcap[/itex]T) = .40?


You got any hints?

Now that reads "in 40% of all accidents the accident was fatal and the driver was texting". Still not what you want, given the probability of a fatal accident is only 0.01. Try again. Read these probability statement back in english. Here's a big hint. What does P(T|F) mean? State it in english.
 
  • #5
P(T|F) reads the probability of an accident being caused by texting, given that it was fatal?
 
  • #6
DotKite said:
P(T|F) reads the probability of an accident being caused by texting, given that it was fatal?

Ok, but, you should read it a little more literally. Nobody said anything about texting being the cause. It is just the probability that driver was texting given the accident was fatal. Now what's the value of that given the problem statement?
 
  • #7
0.4?
 
  • #8
Imagine 1000 accidents. "1% of all auto accidents are fatal" so there are 10 fatal accidents. "40% of all fatal auto accidents are attributed to texting drivers" so 4 of those fatal accidents are attributable to texting. "drivers who text while driving are responsible for 20% of all accidents" so 200 accidents are attributable to drivers who text.

That is, out of 200 accidents attributable to drivers who text, 4 of them are fatal and 16 are not fatal.
 
  • #9
DotKite said:
0.4?

Yes, there's a big difference between P(T|F) and P(F|T). You might want to take another look at the expression you wrote for what you are trying to find.
 
  • #10
HallsofIvy said:
Imagine 1000 accidents. "1% of all auto accidents are fatal" so there are 10 fatal accidents. "40% of all fatal auto accidents are attributed to texting drivers" so 4 of those fatal accidents are attributable to texting. "drivers who text while driving are responsible for 20% of all accidents" so 200 accidents are attributable to drivers who text.

That is, out of 200 accidents attributable to drivers who text, 4 of them are fatal and 16 are not fatal.
Typo (?):
... and 196 are not fatal.
 
  • #11
haruspex said:
Typo (?):
... and 196 are not fatal.
Yes, thanks. Unfortunately, I can no longer edit it so I cannot pretend I didn't make that blunder!
 

Related to What percentage of non-fatal accidents are caused by drivers who do not text?

What is conditional probability?

Conditional probability is a measurement of the likelihood of an event occurring, given that another event has already occurred. It takes into account additional information that may affect the outcome of the event.

How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint occurrence of two events by the probability of the first event occurring. This can be written as P(A|B) = P(A and B) / P(B), where A and B are two events.

What is the difference between conditional probability and regular probability?

Regular probability is the likelihood of an event occurring without any additional information. Conditional probability takes into account additional information that may affect the outcome of the event.

What is the importance of conditional probability in statistics?

Conditional probability is important in statistics because it allows us to make more accurate predictions and decisions by considering additional information. It also helps to understand the relationship between two events and how they may impact each other.

Can you give an example of conditional probability?

One example of conditional probability is the likelihood of getting a head on a coin toss, given that the coin is biased. In this case, the probability of getting a head would be higher than if the coin was fair, as the additional information of the coin being biased affects the outcome.

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