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A Certain Type of Rare Gem Serves as a Status Y=B0+B1X+B2X2+εY = B _ { 0 } + B _ { 1 } X + B_ { 2 } X ^ { 2 } + \varepsilon

question 46

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A certain type of rare gem serves as a status symbol for many of its owners. In theory, for low prices, the demand decreases as the price of the gem increases. However, experts hypothesise that when the gem is valued at very high prices, the demand increases with price due to the status owners believe they gain in obtaining the gem. Thus, the model proposed to best explain the demand for the gem by its price is the quadratic model:
Y=B0+B1X+B2X2+εY = B _ { 0 } + B _ { 1 } X + B_ { 2 } X ^ { 2 } + \varepsilon
where Y = demand (in thousands) and X = retail price per carat.
 SUMMARY  OUTPUT  Regression  Statistics  Multiple R 0.994 R Square 0.988 Std. Error 12.42 Observations 12 ANOVA  df  SS  MS  F  Signif F  Regression 2115,14557,5733730.0001 Residual 91,388154 Total 11116,533 Coeff  Std. Error  t Stat P-Value  Intercept 286.429.6629.640.0001 Price 0.310.065.140.0006 Price Sq 0.0000670.000070.950.3647\begin{array} { | l | l | l | l | l | l | } \hline \text { SUMMARY } & \text { OUTPUT } & & & & \\\hline \text { Regression } & \text { Statistics } & & & & \\\hline \text { Multiple R } & & 0.994 & & & \\\hline \text { R Square } & & 0.988 & & & \\\hline \text { Std. Error } & & 12.42 & & & \\\hline \text { Observations } & & 12 & & & \\\hline & & & & & \\\hline \text { ANOVA } & & & & & \\\hline & \text { df } & \text { SS } & \text { MS } & \text { F } & \text { Signif F } \\\hline \text { Regression } & 2 & 115,145 & 57,573 & 373 & 0.0001 \\\hline \text { Residual } & 9 & 1,388 & 154 & & \\\hline \text { Total } & 11 & 116,533 & & & \\\hline & & & & & \\\hline & \text { Coeff } & \text { Std. Error } & \text { t Stat } & P \text {-Value } & \\\hline \text { Intercept } & 286.42 & 9.66 & 29.64 & 0.0001 & \\\hline \text { Price } & - 0.31 & 0.06 & - 5.14 & 0.0006 & \\\hline \text { Price Sq } & 0.000067 & 0.00007 & 0.95 & 0.3647 & \\\hline\end{array} This model was fit to data collected for a sample of 12 rare gems of this type. A portion of the computer analysis obtained from Microsoft Excel is shown below:
-Referring to Instruction 16-1,a more parsimonious simple linear model is likely to be statistically superior to the fitted curvilinear for predicting sale price (Y).


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