$x_1=1$ if quarter $1,0$ otherwise; $x_2=1$ if quarter $2,0$ otherwise; $x_3=1$ if quarter $3,0$ otherwise

hat$(y{)}_t=$

$($c$)$ Compute the quarterly forecasts for the next year based on the model you developed in part $($b$).($Round your answers to two decimal places.$)$

quarter $1$ forecast

quarter $2$ forecast

quarter $3$ forecast

quarter $4$ forecast

variable $t$ such that $t=1$ for quarter $1$ in year $1,t=2$ for quarter $2$ in year $1,$dotst$=12$ for quarter $4$ in year $3.($Round your numerical values to three decimal places.$)$

hat$(y{)}_t=$

$($e$)$ Compute the quarterly forecasts for the next year based on the model you developed in part $($d$).($Round your answers to two decimal places.$)$

quarter $1$ forecast

quarter $2$ forecast

quarter $3$ forecast

quarter $4$ forecast

$($f$)$ Is the model you developed in part $($b$)$ or the model you developed in part $($d$)$ more effective? Justify your answer.

The model in part $($b$)$ appears to be more effective since it has a higher MSE than the model in part $($d$).$

The model in part $($d$)$ appears to be more effective since it has more variables than the model in part $($b$).$

The model in part $($d$)$ appears to be more effective since it has a lower MSE than the model in part $($b$).$

The model in part $($b$)$ appears to be more effective since it has a lower MSE than the model in part $($d$).$

The model in part $($b$)$ appears to be more effective since there is no linear trend visible in the data.