Uniform [0,1] Squared Probability Calculation

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In summary, the conversation discusses the calculation of the density for U^2, a continuous random variable with a uniform distribution between 0 and 1. The question is whether to use P(U<a^{1/2}) or P(-a^{1/2}<U<a^{1/2}) to find the density. The person asking the question realizes that both methods result in the same answer and their teacher has confirmed this. The person's teacher is praised for being a great teacher.
  • #1
Barioth
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Hi everyone!

Here is my question:

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

We're looking for \(\displaystyle U^2\) Density.

I go with

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

Altough my teacher say I must go with

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

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

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

Am I right?
 
Last edited:
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  • #2
Yes. You are right. ;)
 
  • #3
Thanks, it was in my exam last week, the teacher gave me my point back, a great teacher :)
 

Related to Uniform [0,1] Squared Probability Calculation

1. What is the meaning of "Uniform [0,1] Squared Probability Calculation"?

"Uniform [0,1] Squared Probability Calculation" refers to a mathematical method used to calculate the probability of obtaining a random number between 0 and 1. This type of calculation is often used in statistics and probability theory.

2. How is the probability calculated for a uniform [0,1] distribution?

The probability for a uniform [0,1] distribution is calculated by dividing the range of possible outcomes (1-0=1) by the total number of outcomes (in this case, infinite). Therefore, the probability for any individual outcome is 1/infinity, which is essentially 0. This means that all outcomes have an equal chance of occurring.

3. What is the purpose of using a uniform [0,1] distribution in probability calculations?

A uniform [0,1] distribution is often used in probability calculations because it represents a situation where all outcomes are equally likely. This type of distribution can help simplify complex calculations and is commonly used as a baseline for comparison in statistical analyses.

4. Can the uniform [0,1] distribution be applied to real-world situations?

Yes, the uniform [0,1] distribution can be applied to real-world situations, such as determining the probability of randomly selecting a red or blue marble from a bag with an equal number of each color. However, it is important to note that in many real-world scenarios, outcomes are not always equally likely, so a uniform distribution may not accurately represent the situation.

5. How is the uniform [0,1] distribution related to other types of probability distributions?

The uniform [0,1] distribution is considered a special case of the continuous uniform distribution. It is also related to the discrete uniform distribution, which represents a finite number of equally likely outcomes. Other related distributions include the normal distribution, which is often used in statistical analyses, and the binomial distribution, which is commonly used in experiments with binary outcomes.

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