MHB Uniform [0,1] Squared Probability Calculation

  • Thread starter Thread starter Barioth
  • Start date Start date
Click For Summary
The discussion centers on calculating the density of the squared value of a uniform [0,1] random variable, U. The initial query involves whether to use the probability expression P(U^2<a) as P(U<a^{1/2}) or P(-a^{1/2}<U<a^{1/2}). The conclusion reached is that both expressions yield the same result since U is constrained to the interval [0,1], making the negative range irrelevant. The participant confirms their understanding and notes that their teacher acknowledged their correct reasoning on the exam. This exchange highlights the importance of clarity in probability calculations involving transformations of random variables.
Barioth
Messages
47
Reaction score
0
Hi everyone!

Here is my question:

Let's say U a continuous random variable, U is a uniform [0,1]

We're looking for $$U^2$$ Density.

I go with

$$P(U^2<a)=P(U<a^{1/2})$$

Altough my teacher say I must go with

$$P(U^2<a)=P(-a^{1/2}<U<a^{1/2})$$

If we've U in [0,1] I don't see why we would want to look at value that are under 0?

Thanks for reading

Edit: Thinking about it, it is actualy the same since we can break it as

$$P(U^2<a)=P(-a^{1/2}<U<a^{1/2}) =P(-a^{1/2}<U<0)+P(0<=U<a^{1/2}) $$
$$= 0 + P(0<U<a^{1/2})= P(U<a^{1/2})$$

Am I right?
 
Last edited:
Physics news on Phys.org
Yes. You are right. ;)
 
Thanks, it was in my exam last week, the teacher gave me my point back, a great teacher :)
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

Similar threads

  • · Replies 29 ·
Replies
29
Views
4K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 9 ·
Replies
9
Views
3K
  • · Replies 8 ·
Replies
8
Views
3K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 36 ·
2
Replies
36
Views
4K
  • · Replies 3 ·
Replies
3
Views
2K