SUMMARY OUTPUT

Regression Statistics

R Square $0\mathrm{.}98$

Adjusted R Square $0\mathrm{.}97$

Standard Error $2\mathrm{.}52$

Observations $35$

Coefficients

Standard

Error t $-$Stat P$-$value Lower $95\%$ Upper $95\%$

Intercept $131\mathrm{.}9217\mathrm{.}7697\mathrm{.}11166\mathrm{.}73$

Price of Good $-7\mathrm{.}461\mathrm{.}18-9\mathrm{.}86-5\mathrm{.}06$

Price of Related

Good $10\mathrm{.}240\mathrm{.}978\mathrm{.}2712\mathrm{.}21$

Income $0\mathrm{.}300\mathrm{.}300\mathrm{.}100\mathrm{.}50$

The demand for your product $($x$)$ depends on three factors; the price of your good, the price of a related good

$($Py$),$ and the average income $($I$).$ Excel estimated the above linear demand for your product.

$22)$ Refer to the table above. what is the estimated regression equation?

A$)$ Qd

$=-7\mathrm{.}46$Px $+10\mathrm{.}24$Py $+0\mathrm{.}30$I B$)$ Qd

$=131\mathrm{.}92-7\mathrm{.}46$Px $+10\mathrm{.}24$Py $-0\mathrm{.}30$I

C$)$ Qd

$=131\mathrm{.}92+7\mathrm{.}46$Px $+10\mathrm{.}24$Py $+0\mathrm{.}30$I D$)$ Qd

$=131\mathrm{.}92-7\mathrm{.}46$Px $+10\mathrm{.}24$Py $+0\mathrm{.}30$I

$23)$ Refer to the table above. what percentage of the variation in quantity demanded is explained by this

regression?

A$)100\%$ B$)2\mathrm{.}52\%$ C$)97\%$ D$)99\%$