Finding the cumulative distribution function

In summary, the experiment is to toss two balls into four boxes in such a way that each ball is equally likely to fall in any box. The cumulative distribution function of X is defined as F(x) = \sum_{i = 0}^{i = x}f(i).
  • #1
cielo
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Homework Statement



The experiment is to toss two balls into four boxes in such a way that each ball is equally likely to fall in any box. Let X denote the number of balls in the first box.


Homework Equations



What is the cumulative distribution function of X?

The Attempt at a Solution



I have enumerated what I suppose to be included in the sample space as follows: {(bb, 0, 0, 0), ((b, b, 0, 0), (b, 0, b, 0), (b, 0, 0, b), (0, bb, 0, 0), (0, b, b, 0), (0, b, 0, b), (0, 0, bb, 0), (0, 0, b, b), (0, 0, 0, bb)}

b stands for the box having a ball, bb stands for the box having the two balls, 0 for the box having no ball

I honestly don't know what to do next. Can some please guide me answering the problem?
 
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  • #2
Ok, your first step will be to draw up a probability distribution table. Now X denotes that the number of balls in first box. So, what is f(0), f(1), f(2), f(3), ... where f(x) is the probability that there are x balls in the first box. Since you have already listed all the possibilities of the distribution this shouldn't be too hard.

The cumulative distribution function of X, F(x) is defined as [tex]F(x) = \sum_{i = 0}^{i = x}f(i)[/tex]
 
  • #3
though as there are 2 balls you only need consider up to X=2
 
  • #4
praharmitra said:
Ok, your first step will be to draw up a probability distribution table. Now X denotes that the number of balls in first box. So, what is f(0), f(1), f(2), f(3), ... where f(x) is the probability that there are x balls in the first box. Since you have already listed all the possibilities of the distribution this shouldn't be too hard.

The cumulative distribution function of X, F(x) is defined as [tex]F(x) = \sum_{i = 0}^{i = x}f(i)[/tex]


Thank you for guiding me here.
I have constructed this table with the corresponding probability of values.
x 0 1 2
f(x) 6/10 3/10 1/10

Is there a general formula for finding the cumulative distribution function and the probability density function specific for this problem?

Also, please help me how to find the mean and variance of X.
 
  • #5
those probabilties don't look right to me...

i think the best way for this case is a probability tree - consider separately throwing the 1st ball & the 2nd ball

In each case there is a 1/4 chance of a ball going into the 1st box, let "b" represent a ball in the first box, 0 not in 1st box

this leads to 4 outcomes:
P(00) = (1/4)(1/4)
P(0b) = ...
P(b0) = ...
P(bb) = ...

see if you can fill in the other probabilties, in term of the random variable X
P(X=0) = P(00)
P(X=1) = P(0b) + P(b0)P(X=1)
P(X=2) = P(0b) + P(b0)
 
  • #6
Praharmitra, thank you for the first one guiding me in this problem!
 
  • #7
lanedance said:
those probabilties don't look right to me...

i think the best way for this case is a probability tree - consider separately throwing the 1st ball & the 2nd ball

In each case there is a 1/4 chance of a ball going into the 1st box, let "b" represent a ball in the first box, 0 not in 1st box

this leads to 4 outcomes:
P(00) = (1/4)(1/4)
P(0b) = ...
P(b0) = ...
P(bb) = ...

see if you can fill in the other probabilties, in term of the random variable X
P(X=0) = P(00)
P(X=1) = P(0b) + P(b0)P(X=1)
P(X=2) = P(0b) + P(b0)


lanedance, you gave me a very idea in solving this problem.

P(x=0) = P(00)=(3/4)*(3/4)=9/16
p(X=1) = p(b0) + P(0b) = (1/4)*(3/4) + (3/4)*(1/4) = 6/16
P(x=2) = P(bb) = (1/4)*(1/4) = 1/16

now the cdf follows:
F(x) = o for 0>x
F(x) = 9/16 for 0<=x<1
F(x) = 5/16 for 1<=x<2
F(x) = 1 for x >=2

yehey
 
  • #8
nice work... but the cdf is cumulative

so the probs are correct in the pdf
p(X=0) = 9/16
p(X=1) = 6/16
p(X=2) = 1/16

the cdf should sum up, and i'd write it as follows
P(X<=0) = 9/16
P(X<=1) = 15/16
P(X<=2) = 1
 
  • #9
lanedance said:
nice work... but the cdf is cumulative

so the probs are correct in the pdf
p(X=0) = 9/16
p(X=1) = 6/16
p(X=2) = 1/16

the cdf should sum up, and i'd write it as follows
P(X<=0) = 9/16
P(X<=1) = 15/16
P(X<=2) = 1


Sorry, there were typo errors...excited in sharing what I've done. Once again, thank you! I sit here corrected.
 

Related to Finding the cumulative distribution function

What is a cumulative distribution function (CDF)?

A cumulative distribution function (CDF) is a mathematical function that shows the probability of a random variable being less than or equal to a specific value. It is used to describe the overall distribution of a set of data.

Why is the CDF important?

The CDF is important because it allows us to understand the distribution of a set of data, and can be used to make predictions about future outcomes. It also provides a way to compare different datasets and determine if they have similar distributions.

How is the CDF calculated?

The CDF is calculated by adding up the probabilities of all the values that are less than or equal to a specific value. This can be done using a formula or by creating a graph and visually determining the cumulative probabilities.

What is the relationship between the CDF and probability density function (PDF)?

The CDF and PDF are related because the CDF is the integral of the PDF. The PDF gives the probability density of a specific value, while the CDF gives the cumulative probability up to that value.

How is the CDF used in statistical analysis?

The CDF is used in statistical analysis to calculate the probability of obtaining a specific outcome or set of outcomes. It can also be used to determine the percentile of a given value in a dataset, which can be helpful in comparing data sets and making predictions.

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