Probability Distribution/Random Variable problem.

In summary, the problem with the homework equation is that the sum of the numerators don't equal the denominator. To solve the equation, you need to do disjoint cases.
  • #1
The_Iceflash
50
0

Homework Statement


Roll a dice 4 times
X = smallest face observed
P(X=6) = 1/1296

Find:
P(X=1)
P(X=2)
P(X=3)
P(X=4)
P(X=5)

Homework Equations



N/A

The Attempt at a Solution



[PLAIN]http://img145.imageshack.us/img145/2773/62b19dc3faac4cc4a989485.gif

The issue is that the sum of numerators don't equal the denominator and I'm pretty sure it has to.
 
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  • #2
Your calculation for [itex]P(X = 1)[/itex] is incorrect.

[itex]X = 1[/itex] iff at least one face is 1. The complement of this event is "no face is 1." So I can calculate the probability as follows:

[tex]P(X = 1) = 1 - P(\textrm{no face is 1}) = 1 - \left(5/6\right)^4 = 671/1296[/tex]

Another, more laborious way to do this is to break the event down into disjoint cases that are easier to calculate:

"at least face is 1" is the disjoint union of the following:

"exactly one face is 1"
"exactly two faces are 1"
"exactly three faces are 1"
"exactly four faces are 1"

So you can calculate the probabilities of these four events and add them to get the answer.

The probability of exactly one face is

[tex]{4 \choose 1}(1/6)^1(5/6)^3[/tex]

and so forth.

I didn't check your answers for P(X = 2) etc. You are correct that the probabilities of the 6 events you listed should add to 1.
 
Last edited:
  • #3
^Thanks. What would I do to get P(X=2)...P(X=5)? I'm confused on what to do for the other faces.
 
  • #4
For P(X = 2), I think you have to do disjoint cases as in my second method above.

"the smallest face is 2" is the disjoint union of the following events:

"exactly one face is 2, and the other faces are all > 2"
"exactly two faces are 2, and the other faces are all > 2"
"exactly three faces are 2, and the other faces are all > 2"
"all four faces are 2"

The probability of the first of these events is

[tex]{4 \choose 1}(1/6)^1(4/6)^3[/tex]

and you can calculate the others similarly.

This method should work for P(X=3),...,P(X=5) as well. There might be a simpler short-cut, as there was with P(X = 1), but I don't see one.
 

1. What is a probability distribution?

A probability distribution is a function that describes the likelihood of different outcomes occurring in a random experiment. It shows the range of possible values and the corresponding probabilities of each value occurring.

2. What is a random variable?

A random variable is a numerical quantity that takes on different values based on the outcome of a random experiment. It is usually denoted by the letter "X" and can be either discrete or continuous.

3. What is the difference between discrete and continuous random variables?

Discrete random variables can only take on a finite or countably infinite number of values, while continuous random variables can take on any value within a certain range. For example, the number of heads in 10 coin tosses is a discrete random variable, while the height of a randomly selected person is a continuous random variable.

4. How is a probability distribution graphically represented?

A probability distribution can be graphically represented using a histogram or a probability density function (PDF) for continuous variables. The histogram shows the frequency of each value occurring, while the PDF shows the probability density at each point along the range of values.

5. What is the expected value of a random variable?

The expected value of a random variable is the average value that would be obtained if the experiment was repeated a large number of times. It is calculated by multiplying each possible value by its corresponding probability and summing them up. It is denoted by E(X) or μ.

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