Conditional probability for random variable

In summary, the conversation discusses calculating conditional probabilities for a random variable with a given cumulative distribution function. The first two probabilities are calculated using the intersection of two events and the third probability involves finding the probability of a specific value given that the absolute value of the random variable is equal to that value. The conversation also addresses some confusion about the calculations and confirms that they are correct.
  • #1
libelec
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0

Homework Statement



For the random variable X with the following cumulative distribution function:

opyr9.png


Calculate P(X[tex]\leq[/tex]1.5|X<2), P(X[tex]\leq[/tex]1.5|X[tex]\leq[/tex]2) and P(X = -2| |X|=2)

The Attempt at a Solution



This is an exercise about a subject I'm yet to see in class, but the teacher asked us to give it a try. I understand for conditional probability with random variables that P(X[tex]\leq[/tex]a| X[tex]\leq[/tex]b) = (P(X[tex]\leq[/tex]a, X[tex]\leq[/tex]b)/(P(X[tex]\leq[/tex]b)), that is, the probability for the intesection of X[tex]\leq[/tex]a and X[tex]\leq[/tex]b divided by the probability of X[tex]\leq[/tex]b.

For the first case, the intersection is X[tex]\leq[/tex]1.5, and P(X[tex]\leq[/tex]1.5) = 2/3 acording to the CDF. And P(X<2) is also 2/3, so P(X[tex]\leq[/tex]1.5|X<2) = 1.

For the second case, the intersection is again X[tex]\leq[/tex]1.5 and its probability is 2/3, but now, due to the jump in the function for X = 2, P(X[tex]\leq[/tex]2) = 1. So P(X[tex]\leq[/tex]1.5|X[tex]\leq[/tex]2) = 2/3.

I don't know if what I'm doing is right or not. I've checked some text, but the exercises are mostly done through the distribution of probability function, not the CDF.

And for the third case I'm confused, since I have no idea how to calculate P(|X|= 2). Is it the sum of P(X=2) and P(X=-2) (since I can think |X|= 2 as (X=2 v X=-2), so the P(X=2 v X=-2) = P(X=2) + P(X=-2), taking into account that those are disjoint events)?

Thanks.
 
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  • #2
Anyone?
 
  • #3
For the last part, you want the probability that X=2 given that |X|=2.

So there are two possibilities:
X=-2, X=2. These are disjoint events, each with a non-zero probability, and you want to know that given X is 2 or -2, what's the probability that it's two. There isn't really anything involving distributions here other than reading P(X=2) and P(X=-2) off of the graph
 
  • #4
Office_Shredder said:
For the last part, you want the probability that X=2 given that |X|=2.

So there are two possibilities:
X=-2, X=2. These are disjoint events, each with a non-zero probability, and you want to know that given X is 2 or -2, what's the probability that it's two. There isn't really anything involving distributions here other than reading P(X=2) and P(X=-2) off of the graph

Right, so it's P(X=-2)/(P(X=-2) + P(X=2)) = (1/3)/(1/3 + 2/3) = 1/3. Is this OK?

And are the other calculations (and most importantly, the reasoning behind them) OK as well?

Thanks.
 
  • #5
Except that the probability that X=2 is not 2/3

Your other calculations look good

EDIT: I typed the wrong number for what the probability that X=2 isn't.
 
Last edited:
  • #6
OK, thank you.
 
  • #7
I apologize. I typed the wrong number in my last post, it was supposed to say that P(X=2) is not 2/3 like you had in your calculation.
 

Related to Conditional probability for random variable

1. What is conditional probability for random variable?

Conditional probability for random variable is the probability of an event occurring given that another event has already occurred. In other words, it is the likelihood of an outcome given some prior knowledge or condition.

2. How is conditional probability for random variable calculated?

Conditional probability for random variable can be calculated using the formula P(A|B) = P(A and B)/P(B), where P(A|B) is the probability of event A given event B has occurred, P(A and B) is the probability of both events occurring simultaneously, and P(B) is the probability of event B occurring.

3. What is the difference between conditional probability and joint probability?

Conditional probability focuses on the probability of an event occurring given that another event has occurred, while joint probability looks at the likelihood of multiple events occurring simultaneously.

4. How is conditional probability used in real life?

Conditional probability is commonly used in fields such as statistics, machine learning, and risk assessment. Examples include predicting the likelihood of a disease given certain risk factors, or determining the probability of a stock market crash based on economic indicators.

5. Can conditional probability be greater than 1?

No, conditional probability cannot be greater than 1. This is because a probability cannot exceed the total certainty of an event occurring, which is represented by 1 or 100%. Therefore, the highest possible value for conditional probability is 1.

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