The probability distribution function of

In summary, the conversation discusses a problem where two chips are drawn from each of two urns without replacement, and the number of red chips drawn from each urn is represented as X_1 and X_2. The goal is to find the probability distribution/density function of X_3, which is the total number of red chips drawn from both urns. The conversation includes a calculation of this probability, with some discrepancies between the answer given in the book and the answer the speaker got. The conversation ends with a clarification on the probabilities of different cases and a mention of a typo error in the book.
  • #1
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I've been practicing on how to get the probability distribution/density functions of certain random variables by solving some questions in my book. I cam across this particular problem, and though, It seems easy, the answer does not comply with what I got (or simply I got the wrong answer.)

Urn I and Urn II each has two red chips and two white chips. Two chips are drawn from each urn without replacement. Let [tex] X_1 [/tex] be the number of red chips taken from Urn I, [tex] X_2 [/tex] be the number of red chips taken from Urn II. Find the
[tex]p_X_3(k)[/tex] where [tex]X_3 = X_1 + X_2[/tex]

I got the answer when [tex]X_3 = 0[/tex] so thought I go with the case where [tex]X_3 = 1[/tex] and this can happen if either [tex]X_1 = 1[/tex] and [tex] X_2 = 0[/tex] or vice versa[tex]

P(X_3 = 1) = \left ( \left (\begin{array}{cc}2 \\ 1 \end{array} \right) \left( \begin{array}{cc}2 \\ 1\end{array} \right) / \left( \begin{array}{cc}4 \\ 2\end{array} \right) \right) \right \cdot \left ( \left (\begin{array}{cc}2 \\ 0 \end{array} \right) \left( \begin{array}{cc}2 \\ 2\end{array} \right) / \left( \begin{array}{cc}4 \\ 2\end{array} \right) \right) \cdot 2[/tex]

since you can have [tex]\left (\begin{array}{cc}2 \\ 1 \end{array} \right)[/tex] ways of getting 1 red chip and [tex]\left (\begin{array}{cc}2 \\ 1 \end{array} \right)[/tex] ways of getting the white chip out of [tex]\left (\begin{array}{cc}4 \\ 2 \end{array} \right)[/tex] ways of getting 2 chips from a set of 4 chips from Urn I and for Urn II there are 2 choose 0 ways of getting 0 red and 2 choose 2 ways of getting 2 white chips, so you multiply their probabilities, then multiply by two since the cases of Urn I and Urn II can interchange. I got a probability of 2/9, but when I referred to the answer at the appendix of the book the answer should be 2/90. Am I missing something did I misinterpret the question or is my computation wrong?
 
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  • #2
There is going to be 6 ways for the first urn and 6 for the second urn, so that gives us 36 choices. Thus probability 2/90 is impossible.

To start from the begining: If we draw a red on the first draw, the chances is 2/4, to draw a red a second time is now 1/3, thus two reds are 1/6. Similarly for two whites from the same urn. This leaves a 2/3 chance that we will draw both a red and a white from the same urn.

To check the work we see that all cases must add to 1.

0 Red = 1/6 from urn one, 1/6 from urn 2 = 1/36
1 red = 2/3 from one, 1/6 from the second or visa versa: 4/18 = 2/9.
2 Red = 2/3 X 2/3 = 4/9.
3 red, same as 1 red = 2/9.
4 red same as 0 red = 1/36.

Thus checking our work we have a total of 1/36 + 2/9 +4/9 + 2/9 + 1/36 = 17/18. WHAT WENT WRONG?

Well, there is another way you can draw two red, that is none from the first urn and two from the second, or visa versa; giving 2/36 to add to the case of 2 reds.
 
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  • #3
I knew it! 2/9 was the correct answer. And those were the same answers I got from solving that problem. But for some odd reason, the appendix of the book gave these answers:

Red = 1/6 from urn one, 1/6 from urn 2 = 1/36
1 red = 2/3 from one, 1/6 from the second or visa versa: 4/18 = 2/90.
2 Red = 2/3 X 2/3 = 1/20.
3 red, same as 1 red = 2/90.
4 red same as 0 red = 1/36.

As you can see, the book added an extra zero to those probabilities that from 1 red to 3 red. I can't believe it. The book made a typo error ^_^;;

thanx for the clarification on the cases btw ^_^
 
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What is a probability distribution function?

A probability distribution function is a mathematical function that shows the possible outcomes of a random event and their associated probabilities. It provides a way to model and analyze the likelihood of different outcomes occurring in a given situation.

What is the purpose of a probability distribution function?

The purpose of a probability distribution function is to describe the probability of different outcomes of a random event. It can be used to make predictions, analyze data, and understand the likelihood of certain outcomes in a given system or situation.

What is the difference between a probability distribution function and a probability density function?

While both probability distribution functions and probability density functions describe the probability of different outcomes, they are used in different contexts. A probability distribution function is used for discrete random variables, while a probability density function is used for continuous random variables.

How is a probability distribution function calculated?

The calculation of a probability distribution function depends on the type of distribution being used. For discrete distributions, the function can be calculated by assigning probabilities to each possible outcome. For continuous distributions, the function is calculated using integration or other mathematical methods.

What are some common types of probability distribution functions?

Some common types of probability distribution functions include the normal distribution, binomial distribution, Poisson distribution, and exponential distribution. Each of these functions is used to model different types of random events and their associated probabilities.

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