Question

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

ANOVA

*df* SS MS *F* *Signif
F*

Regression 3605.7736 1201.9245 0.0000

Residual 1214.2264 26.3962

Total 49 4820.0000

Coeff StdError *t
Stat* *p-value*

Intercept -1.6335 5.8078 -0.281 0.7798

Income 0.4485 0.1137 3.9545 0.0003

Size 4.2615 0.8062 5.286 0.0001

School -0.6517 0.4319 -1.509 0.1383

Referring to Table 14-4, which of the independent variables in the model are significant at the 5% level?

Income, School |
||

Income, Size |
||

Size, School |
||

Income, Size, School |

Answer #1

Given Information:

A real estate builder wants to know 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 model in excel whose output is given.

To determine which independent variables in the model are significant at 5% level of significance by using p-value, we use the decision rule that if the p-value is less than 0.05 then the independent variable in the model is significant.

Independent variables |
p-value |

Income |
0.0003 |

Size |
0.0001 |

School |
0.1383 |

The independent variable having p-value less than 0.05 are Income and size.

Hence, the second option is the correct answer.

An executive in the home construction industry is interested in
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
executive randomly selected 50 families and ran the multiple
regression. Excel output is provided below:
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.865
R Square
0.748
Adjusted R...

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. The business
literature involving human capital shows that education influences
an individual’s annual income. Combined, these may influence family
size....

A real estate developer wishes to study the relationship between
the size of home a client will purchase (in square feet) and other
variables. Possible independent variables include the family
income, family size, whether there is a senior adult parent living
with the family (1 for yes, 0 for no), and the total years of
education beyond high school for the husband and wife. The sample
information is reported below.
Family
Square Feet
Income (000s)
Family Size
Senior Parent
Education...

Statistical Methods of Business II – Case Study –
Indiana Real Estate
Ann Perkins, a realtor in Brownsburg, Indiana, would like to use
estimates from a multiple regression model to help prospective
sellers determine a reasonable asking price for their homes. She
believes that the following four factors influence the asking price
(Price) of a house:
The square footage of the house (SQFT)
The number of bedrooms (Bed)
The number of bathrooms (Bath)
The lot size (LTSZ) in acres
She...

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