How can I calculate the probability of X = 1,2,3 in a three-toss die experiment?

In summary, the conversation discusses the concept of X in an experiment of tossing a die thrice, which represents the number of different faces that appear (1,2,3). The speaker explains that there are three possible outcomes for X, depending on the results of the die tosses. They also suggest two methods for solving for P(X=1,2,3), either by enumerating all possible triplets or using combinatorics to calculate the number of ways to get different outcomes.
  • #1
leptons
1
0
Consider the experiment of tossing a die thrice. X is defined as the number of different faces that appear (i.e., X = 1,2,3). What is meant by the "number of different faces that appear"? Could you help me how could I get P(X = 1,2,3)?
 
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  • #2
well,
u r rolling three dice ... so u could get them all different so number of different faces u will have is 3 ... or you could get 2 same numbers but 1 different , so number of different faces u see is 2 ... or u could get all the three numbers same which means number of different faces u see is 1.

so X can take values 1,2 and 3.

Now u can solve it pretty easily in two possible ways ,
1> enumerate all possible triplets
(1,1,1),(1,1,2),...(6,6,6).
then simply do the counting.

2> if you are good at combinatorics ... then u can do it faster by considering
a. number of ways u can get all three numbers different
b. number of ways u can get two numbers same and 1 different
c. number of ways u can get all three numbers same

-- AI
 
  • #3


The "number of different faces that appear" refers to the number of unique outcomes that occur when the die is tossed three times. In other words, if the die lands on the same face all three times, the number of different faces that appear would be 1. If it lands on two different faces, the number of different faces that appear would be 2, and so on.

To calculate P(X = 1,2,3), we need to first determine the total number of possible outcomes. In this case, there are 6 possible outcomes for each toss, giving us a total of 6*6*6 = 216 possible outcomes for three tosses.

Next, we need to determine the number of outcomes that result in X = 1,2,3. This can be done by listing out all the possible combinations of three tosses that would result in 1, 2, or 3 different faces appearing. For example, if the first toss results in a 1, the second in a 2, and the third in a 3, then X = 3. Similarly, if the first toss results in a 1, the second in a 1, and the third in a 2, then X = 2.

After listing out all possible combinations, we can see that there are 56 outcomes that result in X = 1, 84 outcomes that result in X = 2, and 36 outcomes that result in X = 3. Therefore, P(X = 1,2,3) = (56+84+36)/216 = 0.463 or approximately 46.3%.
 

1. What is a discrete random variable?

A discrete random variable is a type of variable in statistics that can take on a finite or countably infinite number of values, where each value has a certain probability of occurring. Examples of discrete random variables include the number of heads when flipping a coin or the number of red cards in a deck of playing cards.

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

A discrete random variable can only take on a finite or countably infinite number of values, while a continuous random variable can take on any value within a certain range. A discrete random variable is typically associated with counting or whole numbers, while a continuous random variable is associated with measurements and can have decimal values.

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

The probability distribution of a discrete random variable shows the probabilities of each possible value that the variable can take on. It is usually represented in a table or graph, with the values on the x-axis and their corresponding probabilities on the y-axis. The sum of all probabilities in the distribution must equal 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 all of these products. This value represents the average value that can be expected to occur over a large number of trials.

5. Can a discrete random variable have a normal distribution?

No, a discrete random variable cannot have a normal distribution because a normal distribution is a continuous probability distribution. However, if the number of possible values for the variable is large enough, the probability distribution may appear to be normal when graphed.

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