TABLE 14-4
a Real Estate Builder Wishes to Determine How

question 22

Multiple Choice

TABLE 14-4
A real estate builder wishes to determine how house size (House) is influenced by family income (Income) , family size (Size) , and education of the head of household (School) . House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomly selected 50 families and ran the multiple regression.
Microsoft Excel output is provided below:
$\begin{array}{ll} & \text { Regression Stuistics } \\\hline \text { Multiple R } & 0.865 \\\text { R Square } & 0.748 \\\text { Adjusted R Square } & 0.726 \\\text { Standard Error } & 5.195 \\\text { Observations } &50 \\\hline\end{array}$
ANOVA
$\begin{array}{lrrrrr}\hline & \text { d f }& \text {S S } & \text {M S } & \text {F } & \text {Significance F } \\\hline \text {Regression} & & 3605.7736 & 1201.9245 & &0.0000 \\\text {Residual} & & 1214.2264 & 26.3962 & \\Total & 49 & 4820.0000 & & & \\\hline\end{array}$

$\begin{array}{lcccc}\hline & \text { Coefficients} & \text {Standard Error} & \text {t Stat }& \text {p -value} \\\hline \text {Intercept }& -1.6335 & 5.8078 & -0.281 & 0.7798 \\ \text {Income} & 0.4485 & 0.1137 & 3.9545 & 0.0003 \\ \text {Size} & 4.2615 & 0.8062 & 5.286 & 0.0001 \\ \text {School }& -0.6517 & 0.4319 & -1.509 & 0.1383 \\\hline\end{array}$

-Referring to Table 14-4, at the 0.01 level of significance, what conclusion should the builder draw regarding the inclusion of School in the regression model?

• A) School is significant in explaining house size and should be included in the model because its p-value is less than 0.01.
• B) School is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01.
• C) School is significant in explaining house size and should be included in the model because its p-value is more than 0.01.
• D) School is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01.

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Definitions:

SAS Proficiency

A measure of one's skill and knowledge in using the Statistical Analysis System (SAS) software for data management and analysis.

Chi-square Test

An analytical procedure employed to find out if a considerable link is present between two categorical factors.

Goodness-of-fit Test

Statistical tests that measure how well observed sample data fits a distribution from a population with a specified distribution.

Categorical Variable

A type of variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or observation to a particular group or category.