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SCENARIO 17-1
a Real Estate Builder Wishes to Determine How

question 210

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SCENARIO 17-1
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: SUMMARY OUTPUT
Regression Statistics
 Multiple R 0.865 R Square 0.748 Adjusted R Square 0.726 Standard Error 5.195 Observations 50\begin{array} { l l } \text { Multiple R } & 0.865 \\ \text { R Square } & 0.748 \\ \text { Adjusted R Square } & 0.726 \\ \text { Standard Error } & 5.195 \\ \text { Observations } & 50 \end{array}
ANOVA
df SS  MS F Signif F Regression 3605.77361201.92450.0000 Residual 1214.226426.3962 Total 494820.0000\begin{array} { l c c c c c } & d f & \text { SS } & \text { MS } & F & \text { Signif } F \\ \text { Regression } & & 3605.7736 & 1201.9245 & & 0.0000 \\ \text { Residual } & & 1214.2264 & 26.3962 & & \\ \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} { l c l c c } & \text { Coeff } & \text { StdError } & t \text { Stat } & 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}
-Referring to Scenario 17-1, which of the independent variables in the model are significant at the 5% level?


Definitions:

Random Assignment

The process of allocating subjects or experimental units to different groups in an experiment using random methods, to ensure that each group is similar at the start.

Double Blind

A study or experiment design in which neither the participants nor the experimenters know who is receiving a particular treatment, to prevent bias.

Stratifying

The process of dividing a population or sample into subgroups, known as strata, to ensure that research or analysis is more accurate and reflective of the whole.

Blocking

A design technique in experiments that groups units with similar characteristics together to isolate the variable of interest and reduce variation.

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