“Probability Density Function (PDF) & Cumulative Distribution Function (CDF) | Simple Explanation” скачать в хорошем качестве

“Probability Density Function (PDF) & Cumulative Distribution Function (CDF) | Simple Explanation”

🎥 In this video, we clearly explain the concepts of PDF (Probability Density Function) and CDF (Cumulative Distribution Function) — the foundation of continuous random variables in probability theory. We’ll break down the definitions, relationships, formulas, and graphical interpretations that make these concepts easy to understand and apply in problems. ✅ Topics Covered in This Video What is a Probability Density Function (PDF) What is a Cumulative Distribution Function (CDF) Relationship between PDF and CDF How to find CDF from PDF and vice versa 🧠 Key Concepts 👉 PDF (f(x)) – Describes how probability is distributed over the values of a continuous random variable. 👉 CDF (F(x)) – Gives the probability that the random variable takes a value less than or equal to x. Perfect For B.Tech / B.Sc / M.Tech students (ECE, CSE, EEE,) GATE / ESE / UGC NET / University Exams preparation Learners studying Probability & Random Processes or Data Science basics