\begin{figure}
        \centering
        \includegraphics[scale=0.2]{Figure211.png}
        \caption{In the case of a Gaussian distribution, with θ
corresponding to the mean $\mu$, the regression
function illustrated in Figure 2.10 takes the form
of a straight line, as shown in red. In this
case, the random variable z corresponds to the
derivative of the log likelihood function and is
given by $(x-\mu_{ML})/\sigma^2$, and its expectation that
defines the regression function is a straight line
given by $(\mu-\mu_{ML})/\sigma^2$. The root of the regression
function corresponds to the maximum likelihood
estimator $\mu_{ML}$.}
    \end{figure}

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