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
#CumulativeProbability #CDF #Probability #Statistics #RandomVariables #ExponentialDistribution #ProbabilityDensityFunction #PatrickJMT #MathHelp #MathTutorial #LearnProbability #MathExplained #EducationalMath #ProbabilityBasics #DiscreteVariables #ContinuousVariables
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
#CumulativeProbability #CDF #Probability #Statistics #RandomVariables #ExponentialDistribution #ProbabilityDensityFunction #PatrickJMT #MathHelp #MathTutorial #LearnProbability #MathExplained #EducationalMath #ProbabilityBasics #DiscreteVariables #ContinuousVariables



![✦ Diagonalization / Diagonalizing a Matrix, Part 2 ✦
(https://youtu.be/bjt3GiM4B0A)
Diagonalization Part 2 | Solving the Example of Matrix A
In this follow-up video, we continue our journey into matrix diagonalization by working through the example introduced in the previous video. Well apply the diagonalization process to matrix
A and fully break down each step, from calculating eigenvalues and eigenvectors to constructing matrices P and D. This video will solidify your understanding of diagonalization by seeing it applied in practice.
What You Will Learn:
Step-by-step solution to the diagonalization example from Part 1.
How to construct matrices
P and D for matrix A.
Techniques to simplify computing powers of A using diagonalization.
This example-based walkthrough will help you grasp the practical application of diagonalization, making matrix computations more intuitive and manageable.
[Patreon support link for PatrickJMT] (https://www.patreon.com/patrickjmt?ty=c)
#Diagonalization #MatrixFactorization #MatrixExample #Eigenvalues #Eigenvectors #PatrickJMT #Mathematics #LinearAlgebra #MathTutorial #MathHelp #EducationalMath #MathExplained #MathTeacher #MatrixDecomposition #MatrixSimplification #Algebra #LearnMath ✦ Diagonalization / Diagonalizing a Matrix, Part 2 ✦](https://i.ytimg.com/vi/n5wcrpc0ng0/mqdefault.jpg)






