Probability distribution function proof

In summary, the conversation was about proving a function to be a valid probability distribution function. The solution involved integrating the function and setting it equal to 1, but there was a mistake in the integration step which resulted in a value of 4/3 instead of 1. After correcting the mistake, the value was indeed equal to 1, proving the function to be a valid probability distribution function.
  • #1
Pomico
25
0
[SOLVED] Probability distribution function proof

Homework Statement



Prove the function p(x) = [tex]\frac{3}{4b^{3}}[/tex](b[tex]^{2}[/tex]-x[tex]^{2}[/tex]) for -b [tex]\leq[/tex] x [tex]\leq[/tex] +b is a valid probability distribution function.

Homework Equations



I'm not sure if it's as simple as this, but I've been using [tex]\int[/tex] p(x) dx (between b and -b) = 1 if the function is valid


The Attempt at a Solution



[tex]\int[/tex] p(x) dx (between b and -b) = [tex]\frac{3x}{4b}[/tex] - [tex]\frac{x^{3}}{12b^{3}}[/tex] between b and -b = [tex]\frac{3}{4}[/tex] - [tex]\frac{1}{12}[/tex] + [tex]\frac{3}{4}[/tex] - [tex]\frac{1}{12}[/tex] = [tex]\frac{4}{3}[/tex]

I treated b as a constant as I was integrating with respect to x, but clearly [tex]\frac{4}{3}[/tex] is not equal to 1... please help!
 
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  • #2
You seem to have forgotten the "3" in "3/4" when you multiplied [tex]\frac{x^3}{3}[/tex] by [tex]\frac{3}{4b^3}[/tex]. You should have
[tex]\frac{3}{4}- \frac{1}{4}+ \frac{3}{4}- \frac{1}{4}= 1[/tex]
 
  • #3
Heh thanks, stupid mistake...
I assumed it was the integration I got wrong as opposed to an earlier stage, I'll check more thoroughly next time!
Thanks again :)
 

Related to Probability distribution function proof

1. What is a probability distribution function (PDF)?

A probability distribution function (PDF) is a mathematical function that describes the likelihood of a random variable taking on a specific value or falling within a certain range of values. It is often used to model and analyze the behavior of a system or process in which there is inherent uncertainty.

2. How is a PDF different from a probability mass function (PMF)?

A probability mass function (PMF) is used to describe the probability of discrete random variables, while a PDF is used for continuous random variables. A PDF is a smooth curve, while a PMF is a function of discrete values. Additionally, the area under a PDF curve represents the probability of the random variable falling within a specific range of values, while the sum of the PMF values for all possible outcomes must equal 1.

3. What is the relationship between a PDF and a cumulative distribution function (CDF)?

A cumulative distribution function (CDF) is the integral of a PDF and represents the probability that a random variable will take on a value less than or equal to a given value. This means that the CDF can be used to calculate the probability of a random variable falling within a specific range of values. The CDF is also useful for determining percentiles and other statistical measures.

4. How is the PDF used in hypothesis testing and statistical inference?

In hypothesis testing and statistical inference, the PDF is used to determine the probability of observing a particular sample or set of data if a certain hypothesis is true. This can help researchers make decisions about whether to accept or reject a null hypothesis based on the likelihood of the observed data. The PDF is also used in model fitting and parameter estimation.

5. What are some commonly used probability distributions and their corresponding PDFs?

Some commonly used probability distributions include the normal distribution, binomial distribution, Poisson distribution, and exponential distribution. The PDFs for these distributions have distinct shapes and parameters that determine the behavior of the random variable. Other distributions, such as the chi-square and t-distributions, are often used in statistical inference.

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