A company selling swimming goggles wants to analyze the company's Australian sales figures.
Time series forecasting with regression was used to generate Excel output to estimate trend of the time series of Swimming goggle sales (in thousands of dollars) where the origin is the March Quarter 2000. SUMMARY OUTPUT

\begin{array}{ll}\hline{\text { Regression Statistics }} \\\hline\text { Muttiple R } & 0.37281 \\\text { R Square } & 0.13899 \\\text { Adjusted R } \\\text { Square } & 0.12243 \\\text { Standard Error } & 10.3925 \\\text { Observations } & 54\\\hline\end{array}

\text { ANOYA }

\begin{array}{lrrrrr} & d f & S S & M S & F & \text { Significance F } \\\hline \text { Regression } & & 906.5867925 & 906.59 & 8.39406 & 0.005497292 \\\text { Residual } & 52 & 5616.172467 & 108 & & \\\text { Total } & 53 & 6522.759259 & & & \\\hline\end{array}

\begin{array}{lrrrrrr} & \text { Coefficients } & \text { StandardError } & \text { tStat } & \text { P-value } & \text { Lover } 95 \% & \text { Upper } 35 \% \\\hline \text { Intercept } & 12.237 & 2.789633876 & 4.3866 & 5.6 \mathrm{E}-05 & 6.639227133 & 17.8348469 \\\mathrm{t} & 0.26289 & 0.090738795 & 2.8973 & 0.0055 & 0.080812368 & 0.4449738\end{array}

(a) Forecast goggles sales for each quarter of 2016.
(b) Are these good predictions? Explain.

Suppose That We Calculate the Four-Period Moving Average of the Following

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