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

Question 116

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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.  SUMMARV OUTPUT  Regression Stotitics  Multiple R 0.37281 R Square 0.13899 Adjusted R  Square 0.12243 StandardError 10.3925 Observations 54\begin{array}{l}\text { SUMMARV OUTPUT }\\\hline\text { Regression Stotitics }\\\begin{array}{lc}\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}\end{array}

 ANOVA \text { ANOVA }
 Sgnificance dfSMSFF Regression 1906.5867925906.598.394060.005497292 Residual 525616.172467108 Total 536522.759259\begin{array}{lccccc} \hline& & & & & \text { Sgnificance } \\& d f & S & M S & F & F \\\hline \text { Regression } & 1 & 906.5867925 & 906.59 & 8.39406 & 0.005497292 \\\text { Residual } & 52 & 5616.172467 & 108 & & \\\text { Total } & 53 & 6522.759259 & & & \\\hline\end{array}

 Standard  Upper  Coefficients  Error  t Stat p-value  Lower 95%95% Intercept 12.2372.7896338764.38665.6E056.63922713317.8348469t0.262890.0907387952.89730.00550.0808123680.4449738\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 } & 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 \\\hline\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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