Uniform distribution of two random variables

In summary: In order to evaluate##P\left[x+y<=z\right] ##solved a double integral of the joint density.X is gong from 0 to 1 and same holds for Y.integrating x from 0 to z and y from 0 to z-x i get##\frac {z^2} {2}##i think the reasoning should be this.In summary, the professor said that if both x and y are numbers in the interval [0, 1], then z = x + y will be in the interval [0, 2].
  • #1
DottZakapa
239
17
Homework Statement
given X an Y uniformly distributed on [0,1] independent find the density of S=X+Y
Relevant Equations
Fs(Z)=P[X+Y<=Z]
i did not get how the professor came to such result. In particular:

in order to evaluate
P[x+y<=z] solved a double integral of the joint density. What i am not getting is did i choose the extreme of integration in order to get as result ##\frac {z^2} {2}##
 
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  • #2
DottZakapa said:
a double integral
Please show your work
 
  • #3
in order to evaluate
##P\left[x+y<=z\right] ##solved a double integral of the joint density.

X is gong from 0 to 1 and same holds for Y.
integrating x from 0 to z and y from 0 to z-x i get

##\frac {z^2} {2}##

i think the reasoning should be this.
 
  • #4
he divided the problem in two parts the above is for z between 0 and 1. the other is for z between 1 and 2, for which an other integral has been computed
i do not understand how this 2 came out
 
  • #5
DottZakapa said:
he divided the problem in two parts the above is for z between 0 and 1. the other is for z between 1 and 2, for which an other integral has been computed
i do not understand how this 2 came out
If both x and y are numbers in the interval [0, 1], then z = x + y will be in the interval [0, 2]. Is that what you're asking? The double interval arises by working with the region in the plane [0, 1] X [0, 1].
 
  • #6
DottZakapa said:
a double integral
what integral ?
 
  • #7
You might think about it by considering the joint distribution ##f_{X,Y}(x,y) = f_X(x)f_Y(y)##. You want $$P(Z \leq z) = P(X + Y \leq z) = P(Y \leq z - X)$$ You can try sketching this on the plane ##[0,1] \times [0,1]##, and calculate this probability using a double integral or much more simply by considering areas (be careful about different cases!). Then convert cdf ##\rightarrow## pdf. I have the feeling that this is what your professor was driving at.

Alternatively, you can use the idea of a convolution $$f_Z(z) = \int_{-\infty}^{\infty} f_X(z-y) f_Y(y) dy$$but I think this approach has more room for error.
 
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  • #8
DottZakapa said:
in order to evaluate
##P\left[x+y<=z\right] ##solved a double integral of the joint density.

X is gong from 0 to 1 and same holds for Y.
integrating x from 0 to z and y from 0 to z-x i get

##\frac {z^2} {2}##

i think the reasoning should be this.
That is outlining your work, not showing it. There is not enough here to say for sure what you are doing wrong. We need to see the actual integral you wrote down.
At a guess, you wrote ##\int_{x=0}^z\int_{y=0}^{z-x}1.dxdy##.
One problem with that is, e.g., for z>1 and x<z-1 your y integral range goes above 1.
A more fundamental problem is that it will give you the CDF, not the PDF.
 
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Related to Uniform distribution of two random variables

1. What is the concept of uniform distribution of two random variables?

The concept of uniform distribution of two random variables refers to the probability distribution where the values of the two variables are equally likely to occur within a given range. This means that the probability of any value occurring between the minimum and maximum values of the range is the same.

2. How is uniform distribution different from other types of probability distributions?

Uniform distribution is different from other types of probability distributions, such as normal or exponential distributions, because it has a constant probability of occurrence for all values within a given range. Other distributions may have varying probabilities for different values.

3. What is the formula for calculating the probability of a specific value in a uniform distribution of two random variables?

The formula for calculating the probability of a specific value in a uniform distribution of two random variables is P(x) = 1/(b-a), where P(x) is the probability of the value x occurring, and a and b are the minimum and maximum values of the range.

4. How is uniform distribution used in statistical analysis?

Uniform distribution is commonly used in statistical analysis to model situations where all outcomes are equally likely, such as in rolling a fair die or selecting a random sample from a population. It can also be used to compare the distribution of data to a theoretical uniform distribution.

5. What are the limitations of using uniform distribution in statistical analysis?

One limitation of using uniform distribution in statistical analysis is that it may not accurately represent real-world situations, where outcomes are often not equally likely. Additionally, it may not be appropriate for modeling data with extreme values or outliers, as the probability of these values occurring is low in a uniform distribution.

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