Cumulative Probability Distribution Functions (CDFs) @patrickjmt
Cumulative Probability Distribution Functions (CDFs)  @patrickjmt
Uploaded November 2024 | Updated September 2026, 1 week ago
Cumulative Probability Distribution Functions (CDFs)
Learn how to work with cumulative probability distribution functions (CDFs) for both discrete and continuous random variables. In this video, we define CDFs, show how they accumulate probabilities, and compare their formulas:

Discrete:

F(x)=∑P(X=x_i)

Continuous:

F(x) = ∫ f(t)dt on (−∞, x)


We also dive into the exponential distribution as an example, covering its probability density function (PDF) and CDF. By the end, you'll know how to apply CDFs to solve real-world problems, such as finding probabilities and proportions for events.

What You Will Learn:
What cumulative distribution functions are and why they matter.
The key differences between discrete and continuous CDFs.
Practical applications, including working with the exponential distribution.
If you're studying probability and statistics, this is an essential concept to master!

Support my work on Patreon: patreon.com/patrickjmt?ty=c

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Cumulative Probability Distribution Functions (CDFs)

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