Conditional Probability (with integrals)

In summary: What proportion of college students obtain a score greater than 0.8 on their math test?It is estimated that approximately 50% of college students score above 0.8 on their math test.
  • #1
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Homework Statement



Suppose a person's score X on a math aptitude test is a number between 0 and 1, and their score Y on a music aptitude test is also between 0 and 1. Suppose further that in the population of all college students in Canada, the scores X and Y are distributed according to the joint probability density function

[tex]f(x,y) = \frac{2}{5}(2x + 3y) if 0 \leq x \leq 1 and 0 \leq y \leq 1[/tex]

0 otherwise.

a) What proportion of college students obtain a score greater than 0.8 on their math test?
b) If a randomly selected student's score on the music test is 0.3, what is the probability that this student's score on the math test will be greater than 0.8?

Homework Equations




The Attempt at a Solution



a)

[tex]P (X > 0.8) = \int^1_0 \int^1_{0.8} \frac{2}{5}(2x + 3y) dx dy [/tex]

b)

[tex]P(A|B) = \frac{P(A \cap B)}{P(B)} \rightarrow P (X > 0.8 | Y = 0.3) = \frac{P((X>0.8)\cap(Y=0.3))}{P(Y = 0.3)}= \frac{\int^1_{0.8} \frac{2}{5}(2x + 3(1)) dx }{0.3}[/tex]

Did I set up both questions correctly?
 
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  • #2
The a part looks to be set up correctly, but something's bothering me about the b part. Isn't the probability of a single value zero? IOW, doesn't P(X = 0.3) = 0? Are you sure you have the wording of the problem exactly right? If the wording is "at least 0.3" or "below 0.3" that would make a difference.
 
  • #3
I've thought about this some more. I was assuming that this distribution was continuous, in which case P(X = 0.3) is in fact zero. If, OTOH, the distribution is discrete and uniformly distributed, then P(X = 0.3) is nonzero. If so, and assuming that the only scores possible are {0, .1, .2, ..., .9, 1}, then the probability of anyone of these values is 1/11 [itex]\approx [/itex] .0909.
 
  • #4
I tripled checked, the wording is correct.

So what you are saying is that P(Y=0.3) is 1/11 due to the fact that there are 11 choices (0, .1, .2, .3, .4, .5, .6, .7, .8, .9, 1) and all of them have an equal chance of occurring.

[tex]P(A|B) = \frac{P(A \cap B)}{P(B)} \rightarrow P (X > 0.8 | Y = 0.3) = \frac{P((X>0.8)\cap(Y=0.3))}{P(Y = 0.3)}= \frac{\int^1_{0.8} \frac{2}{5}(2x + 3(0.3)) dx}{1/11}[/tex]
 
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  • #5
Yep, that's exactly what I'm saying. In the absence of any clarifying information, I don't know what else to think.

The assumption that the scores are discrete rather than continuous impacts how probabilities are calculated. Instead of using an integral, as is the case for continuous random variables, the thing to do, would be to use a summation, which is what is done for discrete random variables. So P(X > 0.8) = P(X = 0.9) + P(X = 1.0). For each of these you would use your pdf with the appropriate x and y values.

When you hand in this problem (if that's what you do with them), it would be a good idea to state your assumptions--that the only possible scores are 0, 0.1, 0.2, and so on, and that they are uniformly distributed.

That's what I think, and that's the way I would tackle this problem. I could be wrong, and welcome any correction to my thinking.
 
  • #6
I asked my professor, and he said the scores are continous, and not discrete. This is what I thought of:

[tex]P(X > 0.8 \cap Y = 0.3) = \int^1_0 \frac{2}{5}(2x + 3(Y = 0.3)) dx = \int^1_0 \frac{2}{5}(2x + 3(0.3)) dx[/tex]

this way, why is no longer a variable, but a constant with a value of 0.3.
 
  • #7
That seems reasonable on the surface, but if you do the integration, you get .76. This doesn't seem like a plausible result; namely that more than 3/4 of the students got a score of 0.3 on the music test, and a score over 0.8 on the math test.
 
  • #8
Maybe all Canadian college students are good at math but suck at music? (Seriously doubt that).

For a continuous case, that's all I could come up with. For all I know, maybe my prof chose those those numbers at random.
 
  • #9
Things also don't make sense to me from a geometric standpoint. With a single continuous random variable, the P(X = a) is zero, and P(a < X < b) is the area under the pdf between a and b. For your pdf, the probability of a point is zero, and the probability along a line segment is also zero. An even with nonzero probability would represent some two-dimensional region in the X-Y plane, and the probability of this event could be thought of as the volume under the pdf curve and above the event region.

So I don't have any more suggestions. When you find out the solution, please post it.
Mark
 

1. What is conditional probability?

Conditional probability is a mathematical concept that measures the likelihood of an event occurring given that another event has already occurred. It is denoted as P(A|B), which reads as "the probability of A given B". It takes into account the relationship between two events and is often used to calculate the probability of a certain outcome in real-life situations.

2. How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint occurrence of two events (A and B) by the probability of the occurrence of one event (B). Mathematically, it is expressed as P(A|B) = P(A∩B)/P(B). This can also be written as P(A|B) = P(A and B)/P(B) to further emphasize the relationship between the two events.

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

Unconditional probability refers to the likelihood of an event occurring without taking into account any other factors. On the other hand, conditional probability considers the relationship between two events and calculates the probability of one event occurring given that another event has already occurred. In other words, conditional probability takes into account additional information, while unconditional probability does not.

4. How is conditional probability used in real-life situations?

Conditional probability is commonly used in real-life situations to make predictions or decisions based on available information. For example, it can be used in medical diagnosis to determine the probability of a certain disease given the patient's symptoms. It is also used in weather forecasting to calculate the chances of rain based on current weather conditions.

5. What role do integrals play in conditional probability?

Integrals are used in conditional probability to calculate the probability of continuous events. For example, in a situation where the outcome can take on any value within a range (such as the height of a person), integrals can be used to calculate the probability of a certain value or range of values. Integrals are also used in calculating the cumulative distribution function (CDF) for continuous random variables, which is an essential component in calculating conditional probability.

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