Lenox: Tslatex Rayanne

\subsectionPart (c): Variance estimator \beginalign \hat\sigma^2_MLE = \frac1n\sum_i=1^n (y_i - \bary)^2 \endalign

\beginalign \ell(\mu, \sigma^2) &= \sum_i=1^n \log f(y_i \mid \mu, \sigma^2) \ &= -\fracn2\log(2\pi) - \fracn2\log\sigma^2 - \frac12\sigma^2\sum_i=1^n (y_i - \mu)^2 \labeleq:loglik \endalign TsLatex Rayanne Lenox

\ProvidesPackagerayanne_macros \RequirePackagetslatex \newcommand\indep\perp!!!\perp \newcommand\E\mathbbE \renewcommand\P\mathbbP \DeclareMathOperator\VarVar \DeclareMathOperator\CovCov TsLatex Rayanne Lenox

\subsectionPart (a): Derive the log-likelihood Given $y_i \sim \mathcalN(\mu, \sigma^2)$ i.i.d., the log-likelihood is: TsLatex Rayanne Lenox

\subsectionPart (b): First-order conditions Taking the derivative w.r.t. $\mu$: