A Company Selling Swimming Goggles Wants to Analyze Its Australian

A company selling swimming goggles wants to analyze its Australian sales figures.
Time series forecasting with regression was used to generate Excel output to estimate trend and seasonal effects of the time series of Swimming goggle sales (in thousands of dollars) where the origin is the March Quarter 2000 and Q1 denotes sales in the March quarter, Q3 denotes sales in the September quarter and Q4 denotes sales in the December quarter. $\begin{array}{l}\text { SUMMARV OUTPUT }\\\hline\text { Regression Stotitics }\\\begin{array}{lc}\hline \text { Multiple R } & 0.9460 \\\text { R Square } & 0.8950 \\\text { Adjusted RSquare } & 0.8864 \\\text { Standard Error } & 3.7394 \\\text { Observations } & 54 \\\hline\end{array}\end{array}$
$\text { ANOVA }$
$\begin{array}{lccccc} \hline& & & & & \text { Sgnificance } \\& d f & S & M S & F & F \\\hline \text { Regression } & 4 & 5837.596003 & 1459.4 & 104.3701 & 2.41949 \mathrm{E}-23 \\\text { Residual } & 49 & 685.1632564 & 13.9829 & & \\\text { Total } & 53 & 6522.759259 & & &\\\hline\end{array}$
$\begin{array}{lcrrrrr}\hline&& \text { Standard } & & && \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text {p-value } & \text { Lower } 95 \%& 95 \%\\\hline\text { Intercept } & 3.0588 & 1.3331 & 2.2944 & 0.0261 & 0.3797 & 5.7378 \\\mathrm{t} & 0.2518 & 0.0327 & 7.7052 & 0.0000 & 0.1861 & 0.3175 \\\mathrm{Q} 1 & 12.4604 & 1.3897 & 8.9664 & 0.0000 & 9.6677 & 15.2530 \\\mathrm{Q} 3 & 1.1458 & 1.4721 & 0.7784 & 0.4401 & -1.8124 & 4.1041 \\\mathrm{Q} 4 & 23.9121 & 1.4403 & 16.6025 & 0.0000 & 21.0177 & 26.8064\\\hline\end{array}$ (a) Forecast swimming goggle sales for all four quarters of 2016.
(b) Are these good forecasts? Explain.
(c) Separate the difference in your forecasts for June 2016 and December 2016 between seasonal and trend.