A company selling swimming goggles wants to analyze the company's Australian sales time 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.

\text { SUMMARY OUTPUT }

\begin{array}{lr}{\text { Regression Statistics }} \\\hline \text { Multiple R } & 0.37281 \\\text { R Square } & 0.13899 \\\text { Adjusted R } & \\\text { Square } & 0.12243 \\\text { StandardError } & 10.3925 \\\text { Observations } & 54 \\\hline\end{array}

\begin{array}{l}A N O V A\\\begin{array}{lrcccr}\hline & d f & S S & M S & F & \text { Significance } F \\\hline \text { Regression } & 1 & 906.5867925 & 906.59 & 839406 & 0.005497292 \\\text { Residual } & 52 & 5616.172467 & 108 & & \\\text { Total } & 53 & 6522.759259 & & &\end{array}\end{array}

\begin{array}{lrrrrrr} & {\text { Standard }} & \\& \text { Coefficients } & \text { Error } & \text { tStat } & \text { P-value } & \text { Lower } 95 \% & \text { Upper } 95 \% \\\hline \text { Interoept } & 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}

Interpret the 95% confidence interval for time t.

The Following Seasonal Indexes and Trend Line Were Computed from Five

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