Range of Function D & R: Find the Answers

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Discussion Overview

The discussion revolves around finding the range of a function dependent on two variables, specifically the difference D = X1 - X2 and its absolute value R = |D|, where X1 and X2 are independent normal variables. Participants explore the implications of this function in terms of its distribution and range, including calculations and graphical representations.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • Some participants propose that the range of R is from 0 to infinity and that of D is from negative infinity to infinity.
  • Others argue that since D is normally distributed, its mean, variance, and standard deviation can be defined, and they question how to sketch the distribution of R based on D.
  • A participant suggests that the range of |D| could be understood as the union of the positive range of D and the positive version of its negative range.
  • Some participants clarify that the function D is a surface in R^3 rather than a line, and they discuss the implications of this in terms of the range.
  • There is confusion regarding the relationship between the distribution of x-y and the modulus of that distribution, with participants questioning how to visualize the modulus of a distribution function.
  • One participant raises a simpler question involving a random variable U to illustrate the concept of probability related to absolute values.

Areas of Agreement / Disagreement

Participants express differing views on the nature of the distribution and the range of the functions involved. There is no consensus on how to visualize or calculate the modulus of the distribution function, and the discussion remains unresolved regarding the implications of the normal distribution on the ranges of D and R.

Contextual Notes

Limitations include potential misunderstandings about the graphical representation of the functions and the distribution of random variables. The discussion also highlights the complexity of defining ranges in the context of normal distributions and absolute values.

jayknight
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Hi guys...I am having big trouble finding the range of a function dependent on 2 variables.
The function is D=X1-X2.
R=|D|
How do you find the range of R and D?Is the range of R from 0 to infinity and that of D from - infinity to infinity?
 
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For the record,this is the question.

Suppose X1 and X2 are independent normal variables with mean m and standard deviation s. Define the difference
D = X1 – X2
Remember that any linear combination of normal variables is itself normally distributed, so it follows that D is normally distributed.
(i) Write down the mean, variance, and standard deviation of D.
(ii) Now define the range ( R ) as the absolute value of D. That is:
R = D|
Sketch the distribution of D, and hence sketch the distribution of R.
 
if the question was f(x) = x could you figure out the range? Is there a way you could make your function D look and behave like f(x)? The range of |D| is going to be the range of D that is positive union with the positive version of the range of D that is negative.
 
JonF said:
if the question was f(x) = x could you figure out the range? Is there a way you could make your function D look and behave like f(x)? The range of |D| is going to be the range of D that is positive union with the positive version of the range of D that is negative.

According to my calculations,it is a stright line passing thru x=X1 and y=-X2.For the modulus of this...Id say the y-intercept becomes y=X2.

However the fact that the question suggests the shape of normal distribution confuses me.According to my diagram,the fn is an inverted version of the normal distribution curve but with sharp edges instead of a curve like geometry.
 
jayknight said:
According to my calculations,it is a stright line passing thru x=X1 and y=-X2.For the modulus of this...Id say the y-intercept becomes y=X2.

This is a function from R^2 to R, so it's graph can't be a line - it is a surface in R^3. Actually it is a plane.

If we use the range in its traditional meaning, then it is the z in R for which there are x and y with f(x,y)=-x-y=z. Clearly, if x,y are in R this poses no restriction on z, so the range is R.
However the fact that the question suggests the shape of normal distribution confuses me.According to my diagram,the fn is an inverted version of the normal distribution curve but with sharp edges instead of a curve like geometry.

The is now something else. x and y are now random variables, and you're asked to sketch the distribution of |x-y|. x-y is normal (and in particular this implies the answer to the first part of your question), so you can plot that distribution and this has nothing to do with the previous question you asked. What is the distribution of x-y? Sketch it. What is the distibution of |x-y|?
 
Last edited:
matt grime said:
This is a function from R^2 to R, so it's graph can't be a line - it is a surface in R^3. Actually it is a plane.

If we use the range in its traditional meaning, then it is the z in R for which there are x and y with f(x,y)=-x-y=z. Clearly, if x,y are in R this poses no restriction on z, so the range is R.




The is now something else. x and y are now random variables, and you're asked to sketch the distribution of |x-y|. x-y is normal (and in particular this implies the answer to the first part of your question), so you can plot that distribution and this has nothing to do with the previous question you asked. What is the distribution of x-y? Sketch it. What is the distibution of |x-y|?
Hey Matt. Thanks for that. But I still am not very clear about the distribution situation. Isnt the the distribution got using the probability distribution function? And also..the modulus of x-y...I can't picture the modulus of a distribution fn. If the fn is on the positive subaxis inthe graph,the modulud would be the same grapg right? I get the feeling my understanding of this subject is a bit flawed.
 
jayknight said:
Hey Matt. Thanks for that. But I still am not very clear about the distribution situation. Isnt the the distribution got using the probability distribution function?

the distribution of what?

And also..the modulus of x-y...I can't picture the modulus of a distribution fn.

x-y is normal of mean 0. The probability that |x-y| is in the interval [a,b] (with 0<=a<b) is the probability x-y is in [a,b] plus the probability y-x is in [a,b]. Which is what? (x-y and y-x are identically distributed, remember).
 
matt grime said:
x-y is normal of mean 0. The probability that |x-y| is in the interval [a,b] (with 0<=a<b) is the probability x-y is in [a,b] plus the probability y-x is in [a,b]. Which is what? (x-y and y-x are identically distributed, remember).
hmm..is it just the positive part of the probability distribution curve?
 
No. (Though it is not clear what 'it' means.)

Let's try a simpler question.

Suppose U is a random variable, and U=-1 with prob 1/2 and 1 with prob 1/2. What is the probability that |U|=1?

Now suppose that U instead is the uniform distribution on {-3,-2,-1,0,1,2,3}. What is the probability that |U| is either 1 or 2?
 

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