= Solution
Let $s_i$ be sunny days and $l_i$ latitude. The <R linear-model formula> expands to a <normal linear model> with both main effects and their <interaction term>:
$$
Y_i=\beta_0+\beta_ss_i+\beta_ll_i+\beta_{sl}s_il_i+\varepsilon_i,
\qquad \varepsilon_i\overset{\mathrm{iid}}\sim N(0,\sigma^2).
$$
The fitted conditional mean is
$$
\boxed{\widehat\mu_i=1.619+0.01403s_i+0.01438l_i-4.872\times10^{-6}s_il_i.}
$$
The residual <standard error> is $0.2836$, giving the usual <variance> estimate $s^2=0.08042896$, and $n=96+4=100$.
The estimated <interaction term> is tiny relative to its <standard error>: $t=-0.103$ and the two-sided <p-value> is $0.91811$. \b[A sensible initial simplification is to remove the interaction and refit an additive linear model], `lm(energy ~ sunny + latitude)`. Both main effects are significant in the supplied fit, and should initially be retained. Do not simply copy their old coefficient estimates into the new fit: removing a design column generally changes all estimates. Centering predictors makes intercepts and main-effect interpretations more useful, but does not change the fitted surface. The <Box–Cox transformation> in part (d) suggests a further response-scale improvement; after changing the response scale, reassess the interaction and diagnostics.
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