Distribution Function and Properties of Continuously Distributed Variances

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Homework Help Overview

The discussion revolves around a probability density function for a continuously distributed random variable X, defined on the interval (0, 1). Participants are exploring the distribution function, probability calculations, median, and expected value for different values of n.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • The original poster attempts to find the distribution function and questions whether finding n in the probability density is necessary. Other participants provide definitions and methods for calculating the distribution function, probabilities, medians, and expected values, while also discussing the limits of integration.

Discussion Status

Participants are actively engaging with the problem, sharing definitions and methods. Some have provided calculations for the distribution function and probabilities, while others are questioning the setup and limits of integration. There is no explicit consensus yet, but several productive lines of reasoning are being explored.

Contextual Notes

Participants note that the density function is zero outside the interval [0, 1], which influences the integration limits for calculations. There is an ongoing discussion about the correct interpretation of the integral limits in the context of the problem.

kasse
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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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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].
 
[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:
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]
 

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