A certain continuous random variable has a probability density function (PDF) given by:
f ( x ) = C x ( 1 − x ) 2 ,
where x can be any number in the real interval [ 0 , 1 ] . Compute C using the normalization condition on PDFs.
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Why did you do an integration by parts? It doesn't make sense to me. It is rather simple to solve this integral using this:
∫ 0 1 C x ( 1 − x ) 2 d x = C ⋅ ∫ 0 1 x − 2 x 2 + 2 x 3 . . .
Just apply the power rule and it would return the result 1 2 1 while yours result seems strange to me, would you care to explain more?
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"Just apply the power rule" is also pretty much what I did. You had to integrate three terms and do some sums and differences, I only had to integrate one term after doing the integration by parts ;)
The integration by parts was convenient for me because I could see beforehand that the boundary (first) term would evaluate to zero at both endpoints. Honestly, either method seems equally elementary to me.
how did you get 1/3?
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That came from integrating the (1-x)^2 part of the integrand and then he just moved the 1/3 outside the integral since it is a constant factor.
It’s absolutely amazing to me that anyone can understand this, lol
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its not 'that' difficult. Integration by parts is a simple technique. There are vastly more complicated things on this site then this.
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The normalization condition is the integral:
∫ 0 1 C x ( 1 − x ) 2 d x = 1 .
Computing using integration by parts,
∫ 0 1 C x ( 1 − x ) 2 d x = − 3 1 C x ( 1 − x ) 3 ∣ ∣ 0 1 + 3 1 ∫ 0 1 C ( 1 − x ) 3 d x = 3 1 ∫ 0 1 C ( 1 − x ) 3 d x = − 3 1 4 1 C ( 1 − x ) 4 ∣ ∣ 0 1 = 1 2 C .
Thus C = 1 2 by the normalization condition.