Sum of squared uniform random variables

In summary, the probability that X^2+Y^2 is less than or equal to one is equivalent to the probability that Y is less than or equal to √(1-X^2), due to the properties of uniformly distributed random variables between 0 and 1. Additionally, it is important to remember that for variables between 1 and 0, x^2 is less than x and √x is greater than x.
  • #1
mjkato
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Homework Statement


If X and Y are independent uniformly distributed random variables between 0 and 1, what is the probability that X^2+Y^2 is less than or equal to one.

Homework Equations


P(Z<1) = P(X^2+Y^2<1)

For z between 0 and 1, P(X^2<z) = P(X < √z) = √z

The Attempt at a Solution


I'm a tad lost- I assume what you'd be looking for is the probability that Y^2 is less than or equal 1-X^2, or rather that Y is less or equal to √(1-X^2). Is that all there is to it?
 
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  • #2
mjkato said:

Homework Statement


If X and Y are independent uniformly distributed random variables between 0 and 1, what is the probability that X^2+Y^2 is less than or equal to one.

Homework Equations


P(Z<1) = P(X^2+Y^2<1)

For z between 0 and 1, P(X^2<z) = P(X < √z) = √z

The Attempt at a Solution


I'm a tad lost- I assume what you'd be looking for is the probability that Y^2 is less than or equal 1-X^2, or rather that Y is less or equal to √(1-X^2). Is that all there is to it?

You have to remember something really important about variables between 1 and zero. Here it is:

if xε[0,1[ then x^2<x and √x> x I think that should help you a bit
 

FAQ: Sum of squared uniform random variables

What is the "Sum of Squared Uniform Random Variables"?

The "Sum of Squared Uniform Random Variables" is a statistical concept that refers to the summation of multiple independent and identically distributed (i.i.d.) uniform random variables, each of which is squared. In simpler terms, it is the sum of the squares of several random numbers that are uniformly distributed within a certain range.

How is the "Sum of Squared Uniform Random Variables" used in statistics?

In statistics, the "Sum of Squared Uniform Random Variables" is used to calculate the variance of a distribution. This is because the squared values of the random variables allow for easier calculation and interpretation of the data.

What is the formula for calculating the "Sum of Squared Uniform Random Variables"?

The formula for calculating the "Sum of Squared Uniform Random Variables" is:
S2 = ∑ (Xi)2, where S2 is the sum of squared uniform random variables, Xi is each individual random variable, and ∑ represents the summation of all the squared random variables.

Can the "Sum of Squared Uniform Random Variables" be used for non-uniform distributions?

Yes, the concept of "Sum of Squared Uniform Random Variables" can be extended to non-uniform distributions. In this case, the random variables are transformed to have a uniform distribution before being squared and summed. This allows for the application of the formula for calculating variance to any type of distribution.

What are some real-world applications of the "Sum of Squared Uniform Random Variables"?

The "Sum of Squared Uniform Random Variables" has various applications in fields such as physics, engineering, and finance. For example, it can be used to model the behavior of particles in a gas, to analyze the reliability and performance of electronic circuits, and to simulate the changes in stock prices over time.

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