In this lecture on Probability and Statistics, we discuss Continuous Random Variables and the Probability Density Function (PDF). We will explore the key properties of PDF, how it is used to compute probabilities for continuous distributions, and solve several examples for better understanding.
📌 Topics Covered:
✅ Definition of Continuous Random Variable
✅ Probability Density Function (PDF) and its Properties
✅ Relationship between PDF and Cumulative Distribution Function (CDF)
✅ Solved Examples and Applications
This lecture is based on the following references:
📖 Michael Baron, Probability and Statistics for Computer Scientists (Second Edition)
📖 Jay L. Devore, Probability and Statistics for Engineering and the Sciences (8th Edition)
📖 S. Ross, A First Course in Probability (10th Edition, Pearson Education)
📖 J. E. Freund & R. E. Walpole, Mathematical Statistics (4th Edition, Prentice Hall)
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