Respuesta :
Distribution of the random variable W = Y. b distribution of the random variable W = VY is derived.
What is Random Variable?
A continuous random variable is a random variable where the data or value can assume infinitely many values ( meaning it’s a continuous set of data. )
For example a random variable measuring the time taken for someone to cook rice is continuous since there are an infinite number of possible times that can be done.
a) If the random variable Y has a uniform distribution on the interval (0, 1), then the probability density function (PDF) of Y is given by:
[tex]f_Y(y) = 1 for 0 < y < 1[/tex]
= 0
If we let W = Y, then the PDF of W is simply the same as the PDF of Y, since W and Y are equal. Therefore, the PDF of W is given by:
[tex]f_W(w) = 1 for 0 < w < 1[/tex]
= 0
b) If we let W = VY, where V is a constant, then the PDF of W is given by:
[tex]f_W(w) = f_Y(w/V) / |V|[/tex]
Substituting in the expression for the PDF of Y, we get:
[tex]f_W(w) = 1/|V| for 0 < w/V < 1[/tex]
= 0
If V > 0, then this reduces to:
[tex]f_W(w) = 1/V for 0 < w < V[/tex]
= 0
If V < 0, then the range of w is reversed and the PDF becomes:
[tex]f_W(w) = -1/V for V < w < 0[/tex]
= 0
The normalization factor [tex]|V|[/tex] is necessary because the transformation
W = VY stretches or compresses the original uniform distribution on the interval (0, 1) by a factor of [tex]|V|[/tex].
Hence, Distribution of the random variable W = Y. b distribution of the random variable W = VY is derived.
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