For any discrete random variable $X$, with probability mass function
$P(X=j)=p_j, p_j \geq 0, j \in \{0, \dots , N \}$, and $\Sigma_{j=0}^N \: p_j =1$, define the polynomial function $g_x(z) = \Sigma_{j=0}^N \: p_j \: z^j$. For a certain discrete random variable $Y$, there exists a scalar $\beta \in [0,1]$ such that $g_y(z) =(1- \beta+\beta z)^N$. The expectation of $Y$ is
- $N \beta(1-\beta)$
- $N \beta$
- $N (1-\beta)$
- Not expressible in terms of $N$ and $\beta$ alone