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Instruction 16-2
a Certain Type of Rare Gem Serves as a Status

question 27

True/False

Instruction 16-2
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 = β0 + β1X + β2X2 + ε
where Y = demand (in thousands)and X = retail price per carat.
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:
 SUMMARY output Regression  Statistics  Multiple R 0.994 R Square 0.988 Std. Error 12.42 Observations 12 ANOVA  dff  SS  MS F Siguif F Regression 2115145575733730.0001 Residual 91388154 Total 11116533 Coeff  StdError  t Stat  P-Value  Intercept 286.429.6629.640.0001 Price 0.310.065.140.0006 Price Sq p.000067p.00007p.95p.3647\begin{array}{|l|l|l|l|l|l|}\text { SUMMARY } &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 { dff } & \text { SS } & \text { MS } & F & \text { Siguif } F \\\hline \text { Regression } & 2 & 115145 & 57573 & 373 & 0.0001 \\\hline \text { Residual } & 9 & 1388 & 154 & & \\\hline \text { Total } & 11 & 116533 & & &\\\hline\\\hline & \text { Coeff } & \text { StdError } & \text { t Stat } & \text { P-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 } & p .000067 & p .00007 & p .95 & p .3647\\\hline\end{array} Note: Std.Error = Standard Error
-Referring to Instruction 16-2,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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