Statistics: Multivariate Dist/Functions of RVs

In summary, we found that the probability of a chosen point (X1,X2,X3) being within a certain distance from the point (0.5,0.5,0.5) is 1/8, and the pdf of Z = X + Y is 4z - 3z^2 for 0 ≤ z ≤ 1.
  • #1
icestone111
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Homework Statement



1) A point (X1,X2,X3) are is chosen at random. X1-3 are uniform distributions across the interval [0,1]
Determine P[(X1-.5)2 + X2-.5)2 + X3-.5)2 ≤ .25]

2) X and Y are random variables with the joint pdf: f(x,y) = 2(x + y) for 0≤x≤y≤1, 0 otherwise.
Find the pdf of Z = X + Y

2. The attempt at a solution

1) So I know the pdf for each one of the random variables is f(x) = 1 when 0≤x≤1
Imagining it in a grid, it would be the volume of the sphere with radius .5 centered at (.5,.5,.5). I am uncertain where to begin applying the pdf of each of the random variables to the graph of the probability needed to be found.

2) I know this has appeared once in another topic, but I can't figure out the bounds of this problem (if I'm not messing up elsewhere).
So the graph envisioned would be the points that lie above the line y=x, bounded by y <= 1, x >=0.
So what I would do from this point is attempt to find the solution for (1-x to 1)∫f(x,z-x)dx = (1-x to 1)∫2zdx = 2z -2z + 2zx = 2zx. What is wrong with this reasoning/where do I approach this problem after this?
 
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  • #2


1) To find the probability, we need to find the volume of the sphere with radius 0.5 centered at (0.5, 0.5, 0.5) within the given interval [0,1]. This can be done by integrating the pdf for each random variable over the given interval and then using the formula for the volume of a sphere. So the probability would be given by P[(X1-0.5)^2 + (X2-0.5)^2 + (X3-0.5)^2 ≤ 0.25] = ∫∫∫ f(x1)f(x2)f(x3) dx1dx2dx3 = (0 to 1)∫(0 to 1)∫(0 to 1) 1 dx1dx2dx3 = (0 to 1)∫(0 to 1) 1 dx2dx3 = (0 to 1) 1 dx3 = 1/8. So the probability is 1/8.

2) To find the pdf of Z, we first need to find the cumulative distribution function (CDF) of Z, which is given by Fz(z) = P(Z ≤ z) = P(X + Y ≤ z). Since X and Y are independent, we can write this as Fz(z) = P(X ≤ z - Y) = ∫∫ f(x,y) dx dy = (0 to z)∫(0 to z-x) 2(x+y) dy dx = (0 to z)∫ 2xz + 2x(z-x) dx = 2z^2 - z^3. Therefore, the pdf of Z is given by fz(z) = d/dz Fz(z) = 4z - 3z^2. So the pdf of Z is fz(z) = 4z - 3z^2 for 0 ≤ z ≤ 1.
 

What is the purpose of multivariate distribution in statistics?

The purpose of multivariate distribution is to study the relationship between multiple variables at the same time. It allows us to analyze the joint behavior of two or more random variables and understand how they are related to each other. This is especially useful in fields such as social sciences, finance, and healthcare where there are often multiple variables that affect an outcome.

What are some examples of multivariate distributions?

Some examples of multivariate distributions include the multivariate normal distribution, multivariate t-distribution, and multivariate Poisson distribution. These distributions are commonly used to model the joint behavior of multiple variables and are widely used in statistical analysis.

How do you calculate the expected value of a function of random variables?

The expected value of a function of random variables is calculated by taking the weighted average of the function values for each possible outcome of the variables, where the weights are the probabilities of each outcome. This is known as the law of the unconscious statistician and is a fundamental concept in multivariate statistics.

What is the difference between univariate and multivariate distributions?

The main difference between univariate and multivariate distributions is the number of variables involved. Univariate distributions deal with a single variable, while multivariate distributions involve two or more variables. Additionally, univariate distributions focus on the behavior of a single variable, while multivariate distributions study the relationship between multiple variables.

What are some real-world applications of multivariate distributions?

Multivariate distributions have various real-world applications, such as in market research to understand the relationship between different factors that affect consumer behavior, in finance to model the joint behavior of stocks and other assets, and in healthcare to study the relationship between multiple risk factors and diseases. They are also used in quality control to analyze the joint behavior of multiple variables in a manufacturing process.

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