What is the Probability Distribution of X in a Two Dice Roll?

In summary, the conversation discusses a question about finding the probability distribution of X, where X is the smaller of two scores on two fair dice. The first poster asks for help and provides their own working out. Others respond, explaining how to find the probabilities for each possible value of X and clarifying the difference between the distribution function and probability mass function. The final answer is given, showing the correct probabilities for each value of X and how they add up to 1.
  • #1
Mo
81
0
Hello (first time poster),
i am having quite a bit of trouble with a particular problem on stats (which i despise of!) - in particular, discrete random variables.Ok here is the question:


"Find the probability distribution of X in each of the following questions ...

Two fair dice are thrown. X is the smaller of the two scores on the dice"


I have managed to do the first few, but this seems to have thrown me a little bit.I would be grateful for any help indeed.If anyone knows of any good resources online for AS-level Stats, i would also be grateful for the link.

Regards,
Mo
 
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  • #2
Is that a question or part of what is given?

If you want the probability that a particular die is the smaller of two, you would just have to count all the possibilities for the dice not being equal and divide by 36. There are 6 cases that the dice are equal, this leaves 30. You only want half of these because in 15 cases X is smaller, in the other 15, the other die is smaller. So it is 15/36.
 
  • #3
Mo said:
"Find the probability distribution of X in each of the following questions ...

Two fair dice are thrown. X is the smaller of the two scores on the dice"
Hi.

It's not too hard to find P(X=a) for a certain a. You just count all the outcomes which have a as a lowest score and divide by 36.

For example: P(X=3)=7/36.
(7 outcomes: (3,3),(3,4),(3,5),(3,6),(4,3),(5,3),(6,3))
 
  • #4
That is a whole question (well, sub-question) Believe it or not, i think i almost had it. here was my working out for the question:

X = Smaller of two scores

SET =
1,1 1,2 1,3 1,4 1,5 1,6
2,1 2,2 2,3 2,4 2,5 2,6
3,1 3,2 3,3 3,4 3,5 3,6
4,1 4,2 4,3 4,4 4,5 4,6
5,1 5,2 5,3 5,4 5,5 5,6
6,1 6,2 6,3 6,4 6,5 6,6

i then carried on with ..

P (X=1) P(1, any other except for 1) =5/36
P (X=2) P(2, any other except for 1 or 2) =4/36
P (X=3) P(3, any other except for 1,2 or 3) =3/36
P (X=4) P(4, 5 or 6) =2/36
P (X=1) P(5, 6) =1/36

Where i was getting confused was because i had been taught that the probabilities should add always add upto 1?

Thanks for your help so far :)
 
  • #5
You did have it... you just need to add up your P(x=1)+...+P(x=5) to get 15/36.

P(X is smaller) + P(X isn't smaller) = 1 = 100%, that's when it adds to 1...
 
  • #6
Ahh, yes, as in the total probabaility will be equal to 1.Thanks for your help, it is much appreciated.
 
  • #7
Galileo said:
Hi.

It's not too hard to find P(X=a) for a certain a. You just count all the outcomes which have a as a lowest score and divide by 36.

For example: P(X=3)=7/36.
(7 outcomes: (3,3),(3,4),(3,5),(3,6),(4,3),(5,3),(6,3))

He needs it for a particular die and equal shouldn't be counted, so P(X=3) = 3/36
 
  • #8
nnnnnnnn said:
He needs it for a particular die and equal shouldn't be counted, so P(X=3) = 3/36
That's not what the question asks.

A random variable is a function from the sample space to [0,1].
If the event is throwing a 3 and a 6 - (3,6), then X=3. If the event is (2,4), then X=2.
And ofcourse (3,3) gives X=3.
The probability of getting X=3 is 7/36 because of the reason I previously gave.

He is asked for the distrubution function, which according to my definition is:
[tex]F(a)=P(X\leq a)[/tex]
although I think the term is easily confused with the probability mass function:
[tex]p(a)=P(X=a)[/tex]
 
  • #9
Galileo said:
That's not what the question asks.

A random variable is a function from the sample space to [0,1].
If the event is throwing a 3 and a 6 - (3,6), then X=3. If the event is (2,4), then X=2.
And ofcourse (3,3) gives X=3.
The probability of getting X=3 is 7/36 because of the reason I previously gave.

He is asked for the distrubution function, which according to my definition is:
[tex]F(a)=P(X\leq a)[/tex]
although I think the term is easily confused with the probability mass function:
[tex]p(a)=P(X=a)[/tex]
I didn't realize that probability distribution was a function, guess I shouldn't have answered...

He thought it should have added to 1, your way does so that's probably why he thought that. Sorry Mo...
 
  • #10
Thanks for your help both.I completed the answer below just for anyone's future reference:


X = Smaller of two scores

SET =
1,1 1,2 1,3 1,4 1,5 1,6
2,1 2,2 2,3 2,4 2,5 2,6
3,1 3,2 3,3 3,4 3,5 3,6
4,1 4,2 4,3 4,4 4,5 4,6
5,1 5,2 5,3 5,4 5,5 5,6
6,1 6,2 6,3 6,4 6,5 6,6

(another, most probabaly easier method of viewing would be ...)

1 2 3 4 5 6
1 1 1 1 1 1 1
2 1 2 2 2 2 2
3 1 2 3 3 3 3
4 1 2 3 4 4 4
5 1 2 3 4 5 5
6 1 2 3 4 5 6

(basically this shows which number is the smallest out of the pair (from both die))

i then carried on with ..

P (X=1) P(where 1 is smallest number) =11/36
P (X=2) P(where 2 is smallest number) =9/36
P (X=3) P(where 3 is smallest number) =7/36
P (X=4) P(where 4 is smallest number =5/36
P (X=5) P(where 5 is smallest number) =3/36
P (X=6) P(where 6 is smallest number) =1/36

Notice how they do all add up to 1 (36/36)

Regards,
Mo
 

1. What is a discrete random variable?

A discrete random variable is a type of variable in probability and statistics that can take on a finite or countably infinite number of values. It is a numerical quantity that is associated with a random event or outcome.

2. How is a discrete random variable different from a continuous random variable?

A discrete random variable can only take on specific, isolated values, while a continuous random variable can take on any value within a certain range. For example, the number of heads in five coin tosses is a discrete random variable, while the weight of a person is a continuous random variable.

3. What is the probability distribution of a discrete random variable?

The probability distribution of a discrete random variable is a function that assigns probabilities to each possible value that the variable can take on. It is often represented in a table or graph, where the sum of all probabilities is equal to 1.

4. How do you calculate the expected value of a discrete random variable?

The expected value of a discrete random variable is calculated by multiplying each possible value by its corresponding probability, and then summing up all of these products. It represents the average value that would be obtained if the random variable is repeatedly measured or observed.

5. What is the significance of discrete random variables in real-world applications?

Discrete random variables are used in a variety of real-world applications, such as in finance, genetics, and engineering. They can help us understand the likelihood of certain events occurring, and make predictions or decisions based on this information. For example, a company may use discrete random variables to model the number of sales they are expected to make in a given period of time.

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