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A Company Selling Swimming Goggles Wants to Analyze the Company's

Question 107

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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
 Regression Statistics  Muttiple R 0.37281 R Square 0.13899 Adjusted R  Square 0.12243 Standard Error 10.3925 Observations 54\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}

 ANOYA \text { ANOYA }
dfSSMSF Significance F  Regression 906.5867925906.598.394060.005497292 Residual 525616.172467108 Total 536522.759259\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}

 Coefficients  StandardError  tStat  P-value  Lover 95% Upper 35% Intercept 12.2372.7896338764.38665.6E056.63922713317.8348469t0.262890.0907387952.89730.00550.0808123680.4449738\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.

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(a) blured image (b) No they are not good prediction...

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