Distribution Function and Properties of Continuously Distributed Variances

In summary, the random variable X is continuously distributed with a probability density function of nx^(n-1) for 0 < x ≤ 1 and 0 otherwise. The distribution function F(x) is equal to x^n. When n=1 and n=2, the probability that X lies between 0.25 and 0.75 is 0.5, and the medians are 0.5 and 0.707, respectively. The expected values E(X) are 0.5 and 2/3 for n=1 and n=2, respectively.
  • #1
kasse
384
1
X is continuously distributed with probability density

[tex]f_{X}(x) = nx^{n-1}, if 0 < x \leq 1[/tex]
and
[tex]f_{X}(x) = 0, otherwise[/tex]

Find the distribution function F(x) of X. Find the probability that X lies between 0.25 and 0.75 when n=1 and when n=2. Find the median of X, i.e. the value of a so that [tex]P(X \leq a) = 1/2[/tex], when n=1 and when n=2. Find E(X) when n=1 and when n=2 and compare with the corresponding medians.


I'm first going to try to find the distribution function. Does this simply mean finding n in the probability density?
 
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  • #2
If [tex] f(x) [/tex] is the density function for a random variable, the distribution function is

[tex]
F(x) = \int_{-\infty}^x f(t) \, dt
[/tex]

You can calculate [tex] P(a \le X \le b) [/tex] either by

[tex]
F(b) - F(a)
[/tex]

or

[tex]
\int_a^b f(x) \, dx
[/tex]

(these are actually the same things dressed up in different notations)

To find the median solve [tex] F(a) = 0.5 [/tex]

To find the expected values calculate integrate [tex] x f(x) [/tex].
 
  • #3
[tex]
F(x) = \int_{-\infty}^x nt^{n-1} \, dt = [t^n]^{x}_{- \infty}
[/tex]

Shoult the integral limits be from 0 to x instead of negative infinity to x? I don't know, but that's the way it's supposed to be done if I interppret an example in my book correctly. Then I get

[tex]
F(x) = x^n
[/tex]

and

P(0.25 < X < 0.75) = 0.5 for both n=1 and n=2.

and the medians a=0.5 and a=0.707 when n=1 and n=2, respectively

and finally

E(X) = 0.5 and E(X) = 2/3 when n=1 and n=2, respectively.
 
Last edited:
  • #4
Yes - I gave the general definitions for your needs. Since your density is zero outside of the interval [tex] [0,1] [/tex], every

[tex]
\int_{-\infty}^\infty \text{ stuff} \, dx
[/tex]

reduces to

[tex]
\int_0^1 \text{stuff} \, dx
[/tex]
 

Related to Distribution Function and Properties of Continuously Distributed Variances

1. What is a distribution function?

A distribution function, also known as a probability distribution function, is a mathematical function that describes the probabilities of all possible outcomes for a random variable.

2. What is the difference between a discrete and a continuous distribution function?

A discrete distribution function is used for random variables that take on a finite or countable number of values, while a continuous distribution function is used for random variables that can take on any value within a certain range.

3. What is the purpose of a distribution function?

A distribution function is used to model and analyze random phenomena in various fields such as physics, economics, and statistics. It helps to understand the likelihood of a particular outcome and make predictions based on the data.

4. How is a distribution function related to a probability density function (PDF)?

A distribution function is the cumulative sum of the probabilities of all possible outcomes, while a PDF is the derivative of the distribution function. In other words, a PDF represents the probability of a specific value occurring, while a distribution function shows the probability of a value less than or equal to a certain value.

5. Can a distribution function be used for non-random variables?

No, a distribution function is specifically used for random variables as it describes the probabilities of their outcomes. For non-random variables, other mathematical functions such as the cumulative distribution function (CDF) or the survival function may be used.

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