transformation of varablies for continuous distribution

Distribution-Function (F ) Technique
which works as follows:
When the new random variable Y is defined as g(X), we find its distribution
function FY (y) by computing Pr(Y < y) = Pr[g(X) < y]. This amounts to solving
the g(X) < y inequality for X [usually resulting in an interval of values], and then
integrating f (x) over this interval [or, equivalently, substituting into F (x)].Distribution-Function (F ) Technique
which works as follows:
When the new random variable Y is defined as g(X), we find its distribution
function FY (y) by computing Pr(Y < y) = Pr[g(X) < y]. This amounts to solving
the g(X) < y inequality for X [usually resulting in an interval of values], and then
integrating f (x) over this interval [or, equivalently, substituting into F (x)].Distribution-Function (F ) Technique
which works as follows:
When the new random variable Y is defined as g(X), we find its distribution
function FY (y) by computing Pr(Y < y) = Pr[g(X) < y]. This amounts to solving
the g(X) < y inequality for X [usually resulting in an interval of values], and then
integrating f (x) over this interval [or, equivalently, substituting into F (x)].Distribution-Function (F ) Technique
which works as follows:
When the new random variable Y is defined as g(X), we find its distribution
function FY (y) by computing Pr(Y < y) = Pr[g(X) < y]. This amounts to solving
the g(X) < y inequality for X [usually resulting in an interval of values], and then
integrating f (x) over this interval [or, equivalently, substituting into F (x)].