Is Z Uniformly Distributed Between 0 and 1?

In summary: Your name]In summary, we discussed the construction of a random variable Z from two independent variables X and Y, both uniformly distributed on [-1,1]. We showed that Z is uniformly distributed on [0,1] by considering the properties of a uniform distribution and visualizing the construction of Z as the squared distance from the origin to a point (X,Y) in a unit square. This is due to the fact that any values of Z greater than 1 are disregarded in the construction. Therefore, Z is uniformly distributed on [0,1].
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gary.ming
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Homework Statement


2 independent r.v. X and Y, both of them are uniformly distributed on the interval [-1, 1]. a random variable Z is constructed by drawing samples from X and Y and forming X^2 + Y^2, however disregarding any draws that give Z > 1. Show that Z is uniformly distributed on [0,1].


Homework Equations


My understanding is Z is drawing from its samples from a square with area 4. Proof that the radius of the unit circle inside the square is uniformly distributed.


The Attempt at a Solution


Any idea, please?
 
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  • #2


Thank you for your question. I am happy to help you understand why Z is uniformly distributed on [0,1].

First, let's review the properties of a uniform distribution. A random variable X is said to be uniformly distributed on the interval [a,b] if its probability density function is given by f(x) = 1/(b-a) for a ≤ x ≤ b and 0 otherwise. This means that every value in the interval has an equal chance of being selected.

Now, let's consider the construction of Z. We know that X and Y are uniformly distributed on [-1,1], and Z is formed by taking the sum of their squares. This means that Z can only take on values between 0 and 2, as the maximum value for X^2 and Y^2 is 1 each.

However, we are disregarding any draws that give Z > 1. This means that the only values of Z that can occur are between 0 and 1, as any values greater than 1 are not included in our sample.

Now, let's consider the distribution of Z. Since we are taking the sum of squares of two uniformly distributed variables, we can see that Z is essentially the squared distance from the origin (0,0) to a point (X,Y) in a unit square.

To visualize this, imagine the unit square with X and Y as the coordinates of a point inside the square. If we draw a circle with radius 1 centered at the origin, we can see that the points within this circle are the only ones that will give Z ≤ 1. This is because any points outside of the circle will have a distance greater than 1 from the origin, and thus will be disregarded.

Since the area of the unit circle is π, and the area of the unit square is 4, we can see that the probability of Z being within the unit circle is π/4, which is equivalent to the probability of Z being between 0 and 1. This means that Z is indeed uniformly distributed on [0,1].

I hope this explanation helps you understand why Z is uniformly distributed on [0,1]. If you have any further questions, please do not hesitate to ask.
 

FAQ: Is Z Uniformly Distributed Between 0 and 1?

What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of various outcomes occurring in a random event. It assigns probabilities to all possible outcomes, with the total sum of probabilities equaling 1.

What are the different types of probability distributions?

There are several types of probability distributions, including the normal distribution, binomial distribution, poisson distribution, and exponential distribution. Each type is used to model different types of data and has its own unique properties.

How is probability distribution used in statistics?

Probability distribution is a fundamental concept in statistics, as it allows us to analyze and make predictions about uncertain events. It is used to calculate probabilities, determine the likelihood of different outcomes, and make inferences about populations based on sample data.

What is the difference between discrete and continuous probability distributions?

A discrete probability distribution is one in which the possible outcomes are countable and distinct, such as rolling a die. A continuous probability distribution is one in which the possible outcomes are infinite and can take on any value within a given range, such as the height of individuals in a population.

How is a probability distribution graphically represented?

A probability distribution can be represented graphically using a histogram or a probability density function (PDF) curve. A histogram is a bar graph that shows the frequency of each outcome, while a PDF curve shows the probability density for each possible outcome.

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