Cumulative distribution function

In summary, the individual is having a disagreement with their TA regarding a problem set. The question asks to find the distribution function associated with two given density functions, but the individual is having issues with the answers they have obtained. The first density function violates the property of a cdf and the second density function has a cdf that is greater than 1 for any value of x. The individual is questioning if they are wrong or if there is something obvious they are missing. They are seeking help with this issue.
  • #1
island-boy
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0
Once again, I'm having a disagreement with my TA regarding a problem set he gave us.

Here is the exact question, as written:
Find the distribution function associated with the following density functions:
a) [tex] f(x) = 3(1-x^2) [/tex] , x an element of (0,1)
b)[tex] g(x) = x^{-2} [/tex], x an element of positive real numbers

here's where my problem lies.
For a)
if you solve for the cdf, you get:
[tex] F(x) = \int_{0}^{x} 3(1-t^2) dt = [3t - t^3]_{0}^{x} = 3x - x^3 [/tex] for x element (0,1)

however, this value of F(x) takes the value of 2 when x = 1, which violates the property of a cdf! Also, if you take the integral of the density from 0 to 1, you will get 2! again a violation of the property of a density function, as the integral should be between 0 and 1.

for b)
if you solve for the cdf, you get
[tex] F(x) = \int_{0}^{x} t^{-2} dt = [-t^{-1}]_{0}^{x} = - 1/x + \infty [/tex] for x element positive real

which again is greater than 1 for any value of x, positive real.

and if you get the integral of the density function from 0 to infinity (as the density function is defined for all positive real), you get infinity! which is not between 0 and 1.

the TA, however said that there is NOTHING wrong with the questions, even after he inspected it.

Am I insane to think that the questions are wrong? or am I not seeing something obvious?

help please. I'm going crazy.
 
Last edited:
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  • #2
anyone have an idea? thanks.
 

What is a cumulative distribution function (CDF)?

A cumulative distribution function (CDF) is a mathematical function that shows the probability of a random variable taking on a value less than or equal to a given value. It is used to describe the distribution of a random variable and is often denoted as F(x).

How is a CDF calculated?

A CDF can be calculated by taking the integral of the probability density function (PDF) of a random variable. The result is a function that increases from 0 to 1 as the value of the random variable increases.

What is the relationship between a CDF and a PDF?

A CDF and PDF are closely related as they both describe the distribution of a random variable. The CDF shows the cumulative probability of a random variable taking on a value, while the PDF shows the probability density of a random variable at a specific value. The CDF can be calculated by taking the integral of the PDF.

How is a CDF used in statistics?

CDFs are commonly used in statistics to analyze and describe the distribution of a random variable. They can be used to calculate probabilities, determine percentiles, and compare different distributions. They are also useful for visualizing data and identifying patterns and trends.

What is the difference between a discrete and continuous CDF?

A discrete CDF is used for discrete random variables, which can only take on a finite or countable number of values. A continuous CDF is used for continuous random variables, which can take on any value within a given range. The shape of the CDF will differ for discrete and continuous variables, but both will increase from 0 to 1 as the value of the random variable increases.

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