Is aX+b uniformly distributed?

In summary, the conversation discusses the properties of a random variable X uniformly distributed over (0,1) and how it changes when multiplied by a constant and added to another constant. It is determined that the resulting random variable, aX + b, is also uniformly distributed. The conversation also explores the distribution and density functions of X^2 and concludes that it is not uniformly distributed. The derivation and reasoning behind these conclusions are explained and confirmed.
  • #1
frzncactus
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0

Homework Statement



If X is a random variable uniformly distributed over (0,1), and a, b are constants, what can you say about the random variable aX + b? What about X^2?

Homework Equations



For uniformity of notation, let

f(x) = probability density function of x
F(a) = distribution function of x
g(x) = density function of Y = aX + b
G(a) = distr function of Y
h(x) = density function of Z = X^2
H(a) = distr function of Z

The Attempt at a Solution



I came across this problem as I was studying for finals, and wasn't quite sure about the answer. I've been stumped for the past two days, and created an account here out of hopelessness just so I could ask this question (so please help? it's not even for a grade, I promise!). I think the answer is that aX + b is uniform, and that X^2 is not. Here's my attempt at a derivation:

G(Y) = P{Y < y) = P{aX+b < y) = P{X < (y-b)/a} = F((y-b)/a)
Now I differentiate with respect to y:
g(y) = d/dy (G(y)) = d/dy (F((y-b)/a)) = 1/a * f((y-b)/a) by chain rule

Since the integral of the probability density function of anything is 1, I integrated g(y) from x=0 to x=1, i.e. y = -b/a to (1-b)/a after modifying the original (0,1) bounds. At this point, I got confused because I wasn't entirely sure if the bounds are supposed to be changed that way, and I'm not sure whether I should be integrating over x or y. Heck, I have no idea if I'm overthinking the problem in the first place.

Next, I attempted the same thing for X^2 by trying to follow a similar thread: https://www.physicsforums.com/showthread.php?t=398718

Putting the result from that thread into my notation, I think that the distribution and density functions of X^2 would be:
H(x) = 1 - F(-sqrt x) + F(sqrt x) = 1 + F(sqrt x)
h(x) = [1/(2 sqrt x)] * (h(sqrt x))I feel like there's something very simple that I don't understand yet. Please enlighten me!
 
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  • #2
frzncactus said:

Homework Statement



If X is a random variable uniformly distributed over (0,1), and a, b are constants, what can you say about the random variable aX + b? What about X^2?

Homework Equations



For uniformity of notation, let

f(x) = probability density function of x
F(a) = distribution function of x
g(x) = density function of Y = aX + b
G(a) = distr function of Y
h(x) = density function of Z = X^2
H(a) = distr function of Z

The Attempt at a Solution



I came across this problem as I was studying for finals, and wasn't quite sure about the answer. I've been stumped for the past two days, and created an account here out of hopelessness just so I could ask this question (so please help? it's not even for a grade, I promise!). I think the answer is that aX + b is uniform, and that X^2 is not. Here's my attempt at a derivation:

G(Y) = P{Y < y) = P{aX+b < y) = P{X < (y-b)/a} = F((y-b)/a)
Now I differentiate with respect to y:
g(y) = d/dy (G(y)) = d/dy (F((y-b)/a)) = 1/a * f((y-b)/a) by chain rule

Since the integral of the probability density function of anything is 1, I integrated g(y) from x=0 to x=1, i.e. y = -b/a to (1-b)/a after modifying the original (0,1) bounds. At this point, I got confused because I wasn't entirely sure if the bounds are supposed to be changed that way, and I'm not sure whether I should be integrating over x or y. Heck, I have no idea if I'm overthinking the problem in the first place.

Next, I attempted the same thing for X^2 by trying to follow a similar thread: https://www.physicsforums.com/showthread.php?t=398718

Putting the result from that thread into my notation, I think that the distribution and density functions of X^2 would be:
H(x) = 1 - F(-sqrt x) + F(sqrt x) = 1 + F(sqrt x)
h(x) = [1/(2 sqrt x)] * (h(sqrt x))


I feel like there's something very simple that I don't understand yet. Please enlighten me!

You haven't really missed anything. Your treatment may be longer than necessary, but it is OK.
 
  • #3
Thank you for the response! Was my conclusion that ax+b is uniform correct? I am uncertain about how it can fit the form for a uniform distribution.
 
  • #4
frzncactus said:
Thank you for the response! Was my conclusion that ax+b is uniform correct? I am uncertain about how it can fit the form for a uniform distribution.

Of course Y = aX+b is uniformly distributed, and your derivation was correct: on a range of y, the cdf ##P(Y \leq y)## is linear in ##y##, so the derivative is constant over the range.
 
  • #5
OHHH. To confirm my understanding, a cdf with a constant derivative (with respect to a uniform random variable) would therefore have a constant density, and thus would be uniform as well. Was that a correct statement?
 
  • #6
frzncactus said:
OHHH. To confirm my understanding, a cdf with a constant derivative (with respect to a uniform random variable) would therefore have a constant density, and thus would be uniform as well. Was that a correct statement?

I think you know the answer to that! Just use the definitions, etc.
 
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1. What is a uniform random variable?

A uniform random variable is a type of probability distribution that has a constant probability of occurring for all possible outcomes. This means that every outcome has an equal chance of occurring.

2. How is a uniform random variable different from other probability distributions?

Unlike other probability distributions, a uniform random variable does not have a bias towards any particular outcome. This means that it is completely random and does not favor any specific value.

3. What is the formula for calculating the probability of a uniform random variable?

The formula for calculating the probability of a uniform random variable is P(X = x) = 1/n, where n is the total number of possible outcomes and x is the specific outcome being considered.

4. How is a uniform random variable used in scientific research?

A uniform random variable is commonly used in scientific research to simulate random events or to represent situations where all outcomes are equally likely. It can also be used to generate random samples for statistical analysis.

5. What are some real-life applications of uniform random variables?

Uniform random variables have many real-life applications, such as in gambling, lottery, and random sampling in surveys. They are also used in computer simulations, weather forecasting, and quality control processes in manufacturing.

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