Difference between a PDF and a CDF?

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The probability density function (PDF) and cumulative distribution function (CDF) are closely related concepts in statistics. The CDF is indeed the integral of the PDF, which means it represents the probability that a random variable takes on a value less than or equal to a specific point. Conversely, the PDF is the derivative of the CDF, indicating the likelihood of the random variable taking on a specific value. Understanding this relationship clarifies the distinction between the two functions. This foundational knowledge is essential for analyzing probability distributions effectively.
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I'm having a lot of trouble figuring out the difference between a probability density function and a cumulative distribution function.

Is the CDF just the integral of the PDF?

Thanks in advance.
 
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The PDF (probability density function) is the derivative of the CDF (cumulative distribution function). Conversely, the CDF is the integral of the PDF.
 
Alright that's what I thought. Much appreciated! I was freaking out haha.
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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