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Cumulative Density Function

The CDF of a variable X, also known as the X distribution function, represents likelihood that X will have a value less than or equal to X

What is Cumulative Density Function (CDF)?

The CDF of a variable X, also known as the X distribution function, represents the likelihood that X will have a value less than or equal to X. Of course, continuous statistical aspects have a role in this. The monotonicity of a CDF is its distinguishing feature. Monotonic growing, to be precise. This means that the probability will always rise over time.

Example of CDF:

The formula of CDF is:
F(x) = P(X ≤ x)

F(x) = function of X

P = probability that an X will have a value less than or equal to x

x = real value variables

Why is CDF important?

Whenever analysts want to assist the possibility of more than one event happening together, CDF plays a critical role. It gives the probability of less than and equal to combining several events making analysts’ decisions more informed.

Owais Siddiqui
1 min read
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