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Instruction 13-4
a Real Estate Builder Wishes to Determine How

question 195

Multiple Choice

Instruction 13-4
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 metres,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:
OUTPUT
SUMMARY
Regression Statistics
 Multiple R 0.865 R Square 0.748 Adj. R Square 0.726 Std. Error 5.195 Observations 50 \begin{array}{ll}\text { Multiple R } & 0.865 \\ \text { R Square } & 0.748 \\ \text { Adj. R Square } & 0.726 \\ \text { Std. Error } & 5.195 \\ \text { Observations } & 50\end{array}
ANOVA
df SS  MS F Siguif F Regression 3605.7736901.44340.0001 Residual 1214.226426.9828 Total 494820.0000\begin{array}{llllll} & d f & \text { SS } & \text { MS } & F & \text { Siguif } F \\\text { Regression } & & 3605.7736 & 901.4434 & & 0.0001 \\\text { Residual } & & 1214.2264 & 26.9828 & & \\\text { Total } & 49 & 4820.0000 & & &\end{array}

 Coeff  StdError t Stat P-value  Intercept 1.63355.80780.2810.7798 Income 0.44850.11373.95450.0003 Size 4.26150.80625.2860.0001 School 0.65170.43191.5090.1383\begin{array}{lllll} & \text { Coeff } & \text { StdError } & \boldsymbol{t} \text { Stat } & \boldsymbol{P} \text {-value } \\\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\end{array} Note: Adj.R Square = Adjusted R Square;Std.Error = Standard Error
-Referring to Instruction 13-4,one individual in the sample had an annual income of $10,000,a family size of 1,and an education of 8 years.This individual owned a home with an area of 1,000 square metre (House = 10.00) .What is the residual (in hundreds of square metre) for this data point?


Definitions:

September

The ninth month of the year in the Gregorian calendar.

Variable Overhead Efficiency Variance

A metric that assesses the difference between the actual hours worked and the standard hours expected, multiplied by the variable overhead rate.

Variable Overhead Efficiency Variance

is the difference between the actual variable overhead incurred and the standard cost allocated, based on the efficiency of utilizing resources.

Variable Overhead Efficiency Variance

The difference between the actual variable overhead and what the variable overhead costs should have been for the actual good units produced.

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