$($d$)$ The auto.arima function was used to automatically fit a SARIMA model SARIMA$($p$,$ d$,$ q$)($P$,$ D$,$ Q$)12,$ i$.$e$.,$ using the commands: $>$fit$\mathrm{.}3=$ auto.arima$($hotel$)>$summary$($fit$\mathrm{.}3)$ Further, the t$-$ratios were computed to test the significance of parameters using the commands: $>$fit$\mathrm{.}3$$coef $9>$fit$\mathrm{.}3$$var.coef $>$std$.$errors$=$sqrt$($diag$($fit$\mathrm{.}3$$var.coef$))>$std$.$errors $>$t$.$ratios $=$ fit$\mathrm{.}3$$coef$/$std$.$errors $>$t$.$ratios $($i$)$ Write down the SARIMA$($p$,$ d$,$ q$)($P$,$ D$,$ Q$)12$ in the form $$P $($B S $)$p $($B$)(1$ B S $)$ D $(1$ B$)$ dXt $=$ $+$Q $($B S $)$ q $($L$)$ t where t is white noise. $(6)($ii$)$ Using the commands: $>$checkresiduals$($fit$\mathrm{.}3)$ to check whether the residuals are white noise, we have Comment on the adequacy of this mode