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Given a random variable \(Y\) that is a function of a random variable \(X\), \(Y=u(X)\), we can use the distribution function technique to find the pdf of \(Y\). $$ \begin{align*} & G\left(y \right)=P \left(Y ≤ y \right)=P\left[u\left(X \right) ≤ y…

Given \(X\) and \(Y\) are independent random variables, then the probability density function of \(X+Y\) can be shown by the equation below: $$ { f }_{ X+Y }\left( a \right) =\int _{ -\infty }^{ \infty }{ { f }_{ X…

The probability generating function of a discrete random variable is a power series representation of the random variable’s probability density function as shown in the formula below: \(G\left( n \right) =P\left( X=0 \right) \ast n^{ 0 }+P\left( X=1 \right) \ast…

The variance of a discrete random variable is the sum of the square of all the values the variable can take times the probability of that value occurring minus the sum of all the values the variable can take times…

The expected value of a discrete random variable is the sum of all the values the variable can take times the probability of that value occurring as in the formula shown below: $$ E\left( X \right) =\sum { xp\left( x…

Given a random experiment, with sample space, \(S\), we can define the possible values of \(S\) as a random variable. Random variables can be discrete or continuous. A discrete random variable is a variable whose range of possible values is…