Probability Density Function

In summary, a Probability Density Function (PDF) is a mathematical function used to describe the likelihood of a continuous random variable taking on a specific value. It is different from a Cumulative Distribution Function (CDF) in that it gives the probability of a specific outcome occurring, while a CDF gives the probability of a value less than or equal to a given value. A Probability Density Function can be interpreted as a graph that shows the relative likelihood of different outcomes in a continuous distribution, with the area under the curve representing the total probability and the height of the curve at a specific point representing the likelihood of that outcome occurring. Some key properties of a Probability Density Function include that the area under the curve must be equal to 1, the function cannot
  • #1
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Hi Guys,

I am having some trouble trying to solve a probability density function question.

...If the density function is: f(x) = 9x^3, 0 < x 1. What is the conditional probability of P(X > 0.2 | X <0.6) ??

Any help would be greatly appreciated :)
 
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  • #2
Find the probability that X is greater than 0.2 and less than 0.6 and divide that by the probability that X is less than 0.6.
 
  • #3
Thanks for the help BicycleTree
 

What is a Probability Density Function (PDF)?

A Probability Density Function (PDF) is a mathematical function that describes the probability of a continuous random variable taking on a specific value. It is used to determine the likelihood of a particular outcome occurring in a continuous distribution.

What is the difference between a PDF and a Cumulative Distribution Function (CDF)?

A PDF gives the probability of a specific outcome occurring, while a CDF gives the probability of a value less than or equal to a given value. In other words, the PDF gives the height of the curve at a specific point, while the CDF gives the area under the curve up to that point.

How do you interpret a Probability Density Function?

A Probability Density Function can be interpreted as a graph that shows the relative likelihood of different outcomes in a continuous distribution. The area under the curve represents the total probability, and the height of the curve at a specific point represents the likelihood of that outcome occurring.

What are the properties of a Probability Density Function?

Some key properties of a Probability Density Function include that the area under the curve must be equal to 1, the function cannot have negative values, and the highest point on the curve represents the most likely outcome.

How is a Probability Density Function used in statistics and research?

A Probability Density Function is used to model and analyze various types of data in statistics and research. It allows researchers to make predictions about the likelihood of different outcomes and to compare different distributions. It is also used to calculate important statistics such as mean, median, and variance in a continuous distribution.

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