Think Different

            I saw that students are unclear about #PDF [probability density function] and #CDF [cumulative density function]. I would be glad to provide a comprehensive explanation of probability density functions ( PDFs ) and cumulative distribution functions ( CDFs ), incorporating the best aspects of the previous responses and addressing any potential shortcomings: Probability Density Function ( PDF ): A PDF is a mathematical function that describes the probability distribution of a continuous random variable. It represents the likelihood of a random variable taking on a particular value within a certain range. The PDF is always non-negative and its integral over its entire range must equal 1. For a continuous random variable X, the PDF is denoted as f(x). The probability of X falling within a certain range [a, b] is given by the integral of the PDF over that range: P(a ≤ X ≤ b) = ∫[a, b] f(x) dx. Cumulative Distribution Function ( CDF ): A CDF is...