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A Company Selling Swimming Goggles Wants to Analyze Its Australian

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Essay

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.  SUMMARV OUTPUT  Regression Stotitics  Multiple R 0.9460 R Square 0.8950 Adjusted RSquare 0.8864 Standard Error 3.7394 Observations 54\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}
 ANOVA \text { ANOVA }
 Sgnificance dfSMSFF Regression 45837.5960031459.4104.37012.41949E23 Residual 49685.163256413.9829 Total 536522.759259\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}
 Standard  Upper  Coefficients  Error  t Stat p-value  Lower 95%95% Intercept 3.05881.33312.29440.02610.37975.7378t0.25180.03277.70520.00000.18610.3175Q112.46041.38978.96640.00009.667715.2530Q31.14581.47210.77840.44011.81244.1041Q423.91211.440316.60250.000021.017726.8064\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) Write out the regression equations for each of the four quarters.
(b) Sketch the four equations from part (a) on the same set of axes.
(c) Interpret the coefficients on all the indicator variables.
(c) All the indicator variables have positive coefficients. Is this surprising? Explain.

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Definitions:

Fibrocartilage

A type of cartilage with a dense interlacing network of type I collagen fibers, providing strength and flexibility to structures like intervertebral discs.

Meniscus

The curved surface of a liquid in a tube or those fibrocartilaginous structures in the knee joint that reduce friction and absorb shock.

Hyaline

A type of smooth, translucent cartilage found in various parts of the body such as the nose, trachea, and larynx, providing support with some flexibility.

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