= Solution
The estimates maximize the grouped-binomial likelihood, numerically obtained by <iteratively reweighted least squares>. At the fitted probabilities, the estimated covariance matrix is the inverse observed information, approximately
$$
\widehat{\operatorname{Var}}(\widehat\beta)
=(X^T\widehat W X)^{-1},
\qquad
\widehat W_{ii}=n_i\widehat p_i(1-\widehat p_i).
$$
The `Std. Error` column is the square root of its diagonal. Each `z value` is `Estimate/Std. Error`, and the two-sided <Wald test> p-value is
$$
2\Phi(-|z|)
$$
for the null hypothesis that the corresponding coefficient is zero.
Thus $\widehat\beta_0=1.6855$ is the fitted log odds for a nonmalignant patient at site A. The site coefficients compare B and C with A, while $\widehat\beta_M=-0.9048$ compares malignant with nonmalignant tumours after adjustment for site. In odds-ratio form,
$$
e^{0.8096}\approx2.25,
\qquad e^{-0.5423}\approx0.58,
\qquad e^{-0.9048}\approx0.405.
$$
At level $0.05$, the B-versus-A contrast is not significant ($p=0.0624$), the C-versus-A contrast is not significant ($p=0.2610$), and malignancy has a significant negative association with survival ($p=0.0175$). The intercept test merely compares the baseline survival probability with $1/2$.
The null deviance $11.693$ compares the intercept-only fit with the saturated model and has $6-1=5$ residual degrees of freedom. The residual deviance $0.85048$ compares the four-parameter fitted model with the saturated model and has $6-4=2$ degrees of freedom. Finally, the <Akaike information criterion> is
$$
\boxed{-2\ell(\widehat\beta)+2\cdot4=29.003.}
$$
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