Representing the Probability Distribution of XY in a Graph

In summary, the conversation discusses finding the probability distribution of the product of two random variables X and Y, given a set of 5 real numbers and their respective probabilities. The conversation also addresses the question of how to represent this probability distribution, whether through a graph or a correspondence between points.
  • #1
Alexsandro
51
0
Could someone help me to find the probability distribution de XY below ?
Take [tex] \Omega [/tex] to be a set of 5 real numbers. Define a probability measure and a random variable X on it which takes the values 1, 2, 3, 4, 5 with probability [itex] \frac{1}{10}, \frac{1}{10}, \frac{1}{5}, \frac{1}{5}, \frac{2}{5}, [/itex] respectively; another random variable Y which takes the values [itex] \sqrt{2}, \sqrt{3}, \pi [/itex] with probabilities [itex] \frac{1}{5}, \frac{3}{10}, \frac{1}{2} [/itex]. Find the probability distribution of XY.
 
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  • #2
Well, where are you stuck? What have you done, and what do you think you need to do?
 
  • #3
Just go ahead and do it! Since one possible value for x is 1 (with probability 1/10) and one possible value for y is [itex]\sqrt{2}[/itex] (with probability 1/5, one possible value for xy is [itex]1*\sqrt{2}= \sqrt{2}[/itex] with probability (1/10)(1/5)= 1/50. There are 15 possible values for xy. Calculate each of them.
 
  • #4
thanks

HallsofIvy said:
Just go ahead and do it! Since one possible value for x is 1 (with probability 1/10) and one possible value for y is [itex]\sqrt{2}[/itex] (with probability 1/5, one possible value for xy is [itex]1*\sqrt{2}= \sqrt{2}[/itex] with probability (1/10)(1/5)= 1/50. There are 15 possible values for xy. Calculate each of them.

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Thank you, for help. One doubt, that I have, consists of knowing the best way to represent the probability distribution on this situation: for a graph or correspondence between points ?
 

Related to Representing the Probability Distribution of XY in a Graph

What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of different outcomes occurring in a random experiment or process. It maps each possible outcome to its associated probability, providing a complete picture of the possible outcomes and their likelihoods.

What is the difference between discrete and continuous probability distributions?

Discrete probability distributions are used for random variables that can only take on a countable number of values, such as the outcomes of a coin toss or the number of children in a family. Continuous probability distributions are used for random variables that can take on any value within a specific range, such as height or weight.

How is a probability distribution represented graphically?

A probability distribution can be represented graphically using a histogram or a probability density function (PDF) curve. The histogram displays the frequencies of each outcome, while the PDF curve shows the probability of each outcome occurring.

What is the relationship between probability distributions and statistics?

Probability distributions are an essential tool in statistics, as they allow us to make predictions and draw conclusions about a population based on a sample of data. By understanding the underlying probability distribution of a dataset, we can make inferences and calculate probabilities about the population from which the data was drawn.

How are probability distributions used in real-world applications?

Probability distributions have many practical applications, such as risk assessment, weather forecasting, and financial analysis. They are also used in fields such as genetics, physics, and engineering to model and predict outcomes in complex systems. Understanding probability distributions is crucial for making informed decisions and understanding the uncertainty inherent in many real-world situations.

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