What is the Probability of a Student Getting an A?

In summary: P(Xp=Xc | Xh=Xp )=0 , not 1/3 .And, of course, you should give the values of the probabilities, not just their symbols....and P(Xp=Xc | Xh=Xp )=0 , not 1/3 .And, of course, you should give the values of the probabilities, not just their symbols.In summary, the conversation discusses the use of the Law of Total Probability to determine the probability of a student getting an A. It also touches on the importance of choosing appropriate random variables and the concept of mutually exclusive sets in probability problems. Additionally, the conversation includes a discussion about the Monty Hall problem and the strategy of switching choices to increase
  • #1
Shackleford
1,656
2
I'm not sure I'm doing this correctly. Am I correct in observing right off the bat that the probability of a student getting an A is 10/24? It seems I don't have to use the Law of Total Probability.

It's the same for (b). I can setup a simple algebraic equation to solve for the scout batting average.

http://i111.photobucket.com/albums/n149/camarolt4z28/Untitled.png?t=1301785453

http://i111.photobucket.com/albums/n149/camarolt4z28/IMG_20110402_175748.jpg?t=1301785242
 
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  • #2
Naughty. If you followed the format that the forum wants, you would have stated the Law of Total Probability under Relevant equations and I'd know what your were talking about.

I think your law of Total Probability is "probably" something like:
P(A) = P(A and B) + P(A and not B) = P(A given B) P(B) + P(A given not B) P(not B)

Your observation is correct, but you can justified it by using
A = the student gets an A
B = the student is a man
 
  • #3
Stephen Tashi said:
Naughty. If you followed the format that the forum wants, you would have stated the Law of Total Probability under Relevant equations and I'd know what your were talking about.

I think your law of Total Probability is "probably" something like:
P(A) = P(A and B) + P(A and not B) = P(A given B) P(B) + P(A given not B) P(not B)

Your observation is correct, but you can justified it by using
A = the student gets an A
B = the student is a man

It's in the title.

That makes sense. I was having a hard time choosing my random variables. I suppose, generally, I should choose my random variables based on the condition of answering the question and some piece of information I already have. Is this correct?
 
  • #4
Shackleford said:
It's in the title.
The fact it's in the title doesn't tell me what the law states. As far as I knew, the "Law Of Total Probabilitiy" might be that
P(A) + P(not A) = 1.

I suppose, generally, I should choose my random variables based on the condition of answering the question and some piece of information I already have. Is this correct?

Yes, for this type of problem you should look for random variables that divide the possibilities into mutually exclusive sets whose union is the entire set of possibilities. A tricky problem might divide it into more than 2 sets. (e.g. people under 20, people between 20 and 40, people over 40 )
 
  • #5
Stephen Tashi said:
The fact it's in the title doesn't tell me what the law states. As far as I knew, the "Law Of Total Probabilitiy" might be that
P(A) + P(not A) = 1.
Yes, for this type of problem you should look for random variables that divide the possibilities into mutually exclusive sets whose union is the entire set of possibilities. A tricky problem might divide it into more than 2 sets. (e.g. people under 20, people between 20 and 40, people over 40 )

But your random variables are not mutually exclusive:

A = the student gets an A
B = the student is a man

Well, I guess A is just the event whose probability you want.

The sets would be B = student is man, C = student is not a man. This of course implies it is a woman, and we have all the necessary information for that set.
 
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  • #6
Okay. I did (b). It works, but I'm not sure why.

P(F1) and P(F2) make sense.

However, I called the event E the batting average. The probability of E given that F2 has occurred is .380.

http://i111.photobucket.com/albums/n149/camarolt4z28/IMG_20110403_121045.jpg?t=1301850735 Edit: Actually, maybe I should call even E hitting the ball, which would be the batting average.
 
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  • #7
That looks OK, but do your course materials use a notation for the complement of an event such a [tex] A^c[/tex] or [tex] \lnot A [/tex] or [tex] \sim A [/tex] ? If so , you should use that notation to represent the event "no scout present".
 
  • #8
Stephen Tashi said:
That looks OK, but do your course materials use a notation for the complement of an event such a [tex] A^c[/tex] or [tex] \lnot A [/tex] or [tex] \sim A [/tex] ? If so , you should use that notation to represent the event "no scout present".

Yeah. I figured that. On my paper, I did that for (a). I should be consistent with (b).

I also have a question on the Monty Hall problem. I'm trying to show why the probability is increased when the contestant changes his pick. I'll post my work here in a second.
 
  • #10
As I read your work, you have [tex] P(winning | X_p = X_c) = \frac{2}{3} [/tex],

but if the contestants strategy is to switch then he always loses if his first pick is correct.
 
  • #11
Stephen Tashi said:
As I read your work, you have [tex] P(winning | X_p = X_c) = \frac{2}{3} [/tex],

but if the contestants strategy is to switch then he always loses if his first pick is correct.

Like I said, I'm not sure how to do (c). I know I did (a) and (b) correctly. How do I approach (c)? It seems a little more tricky.
 
  • #12
Your basic approach is correct. Just keep in mind that [tex] X_p [/tex] refers to the contestants first choice, not his final choice. Change the numerical values you assigned accordingly.
 
  • #13
Stephen Tashi said:
Your basic approach is correct. Just keep in mind that [tex] X_p [/tex] refers to the contestants first choice, not his final choice. Change the numerical values you assigned accordingly.

Okay. Here's what I got.

If his strategy is to change his first pick, and if he picks correctly on the first choice, then clearly the probability of winning is zero since he switches. P(Xp=Xc is still 1/3.

I think I understand the second term. P(Xpnot =Xc is still 2/3. The probability of winning given that his first choice was not correct, and he switches to the other door, given that the host would not open the door with car, is 1.

http://i111.photobucket.com/albums/n149/camarolt4z28/IMG_20110403_223552.jpg?t=1301888246
 
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  • #14
That looks correct to me.
 
  • #15
Stephen Tashi said:
That looks correct to me.

Excellent. Thanks for your prompt help!
 

Related to What is the Probability of a Student Getting an A?

What is the "Law of Total Probability"?

The Law of Total Probability, also known as the Law of Total Probability, is a fundamental rule in probability theory that states that the total probability of an event is equal to the sum of the individual probabilities of all possible outcomes of that event. In other words, it is a mathematical formula that helps calculate the probability of an event by taking into account all possible outcomes.

How is the "Law of Total Probability" used in real life?

The Law of Total Probability is used in various fields such as finance, economics, and statistics to calculate the probability of an event occurring. For example, in the finance industry, it is used to calculate the risk of investments, while in the medical field, it is used to determine the likelihood of a patient having a specific disease based on their symptoms.

What is an example of the "Law of Total Probability" in action?

An example of the Law of Total Probability in action would be calculating the probability of rolling a total of 7 on a pair of dice. There are 36 possible outcomes when rolling two dice, but only 6 of those outcomes result in a total of 7. Therefore, the probability of rolling a 7 is 6/36 or 1/6, which follows the Law of Total Probability.

Can the "Law of Total Probability" be applied to more than two events?

Yes, the Law of Total Probability can be applied to any number of events. The formula for calculating the total probability of multiple events is P(A) = ∑ P(A|Bi) * P(Bi), where A is the event of interest and Bi represents all possible outcomes of the other events.

What is the relationship between the "Law of Total Probability" and Bayes' Theorem?

Bayes' Theorem is an extension of the Law of Total Probability. While the Law of Total Probability calculates the probability of an event based on all possible outcomes, Bayes' Theorem takes into account additional information, such as prior knowledge or observations, to update the probability of an event occurring.

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