Understanding Distribution Functions: Proving and Verifying Their Properties

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The discussion focuses on verifying whether a linear combination of two distribution functions, F and G, represented as λF + (1 - λ)G, is also a distribution function for 0 ≤ λ ≤ 1. It confirms that this combination meets the criteria of a distribution function, including limits approaching 1 as x approaches infinity and 0 as x approaches negative infinity. Additionally, the participants question whether the product F.G qualifies as a distribution function, prompting a return to the fundamental definition of distribution functions. The consensus is that both examples provided can be verified as distribution functions. Understanding these properties is essential for working with distribution functions in probability theory.
Alexsandro
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Could someone help me. I don't able to explain if is FG is a distribution fuction:
Show that if F and G are distribution functions and 0 \leq \lambda \leq 1 then \lambda.F + (1 - \lambda).G is a distribution function. Is the product F.G a distribution function?
 
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In order see if a function is a distribution function, go back to the definition.

F(x) is a d.f. if F-> 1 as x -> inf, F-> 0 as x-> -inf. F(y)>=F(x) for y>x.

It should be easy for you to verify that in both examples you have a distribution function.
 
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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