1. Suppose that you estimate a model of house prices to determine the impact of having beach frontage on the value of a house. 14 You do some research, and you decide to use the size of the lot instead of the size of the house for a number of theoretical and data availability rea- sons. Your results (standard errors in parentheses) are: PRICE₁ = 40 + 35.0 LOT-2.0 AGE + 10.0 BED; - 4.0 FIRE¡ (5.0) (1.0) N = 30 (10.0) (4.0) R² = .63 (10) PRICE₁ =the price of the th house (in thousands of dollars) where: LOTi AGE BED FIRE =the size of the lot of the th house (in thousands of square feet) = the age of the th house in years =the number of bedrooms in the th house =a qualitative/dummy variable for a fireplace (1 yes for the th house) a. You expect the variables LOT and BED to have positive coefficients. Create and test the appropriate hypotheses to evaluate these expectations at the 5-percent level. b. You expect AGE to have a negative coefficient. Create and test the appropriate hypotheses to evaluate these expectations at the 10-percent level. c. At first you expect FIRE to have a positive coefficient, but one of your friends says that fireplaces are messy and are a pain to keep clean, so you're not sure. Run a two-sided t-test around zero to test these

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1. Suppose that you estimate a model of house prices to determine the impact
of having beach frontage on the value of a house.14 You do some research,
and you decide to use the size of the lot instead of the size of the house for a
number of theoretical and data availability rea- sons. Your results (standard
errors in parentheses) are:
PRICE; = 40 + 35.0 LOT¡ – 2.0 AGE; + 10.0 BED; – 4.0 FIRE;
(1.0)
(5.0)
(10.0)
(4.0)
(10)
N = 30
R2 =.63
where:
PRICE¡ =the price of the th house (in thousands of
dollars)
=the size of the lot of the th house (in thousands of
square feet)
= the age of the th house in years
=the number of bedrooms in the th house
=a qualitative/dummy variable for a fireplace (1 =
yes for the th house)
LOT;
AGE;
BED;
FIRE;
a. You expect the variables LOT and BED to have positive coefficients. Create
and test the appropriate hypotheses to evaluate these expectations at
the 5-percent level.
b. You expect AGE to have a negative coefficient. Create and test the
appropriate hypotheses to evaluate these expectations at the 10-percent
level.
c. At first you expect FIRE to have a positive coefficient, but one of your
friends says that fireplaces are messy and are a pain to keep clean, so
you're not sure. Run a two-sided ttest around zero to test these
Transcribed Image Text:1. Suppose that you estimate a model of house prices to determine the impact of having beach frontage on the value of a house.14 You do some research, and you decide to use the size of the lot instead of the size of the house for a number of theoretical and data availability rea- sons. Your results (standard errors in parentheses) are: PRICE; = 40 + 35.0 LOT¡ – 2.0 AGE; + 10.0 BED; – 4.0 FIRE; (1.0) (5.0) (10.0) (4.0) (10) N = 30 R2 =.63 where: PRICE¡ =the price of the th house (in thousands of dollars) =the size of the lot of the th house (in thousands of square feet) = the age of the th house in years =the number of bedrooms in the th house =a qualitative/dummy variable for a fireplace (1 = yes for the th house) LOT; AGE; BED; FIRE; a. You expect the variables LOT and BED to have positive coefficients. Create and test the appropriate hypotheses to evaluate these expectations at the 5-percent level. b. You expect AGE to have a negative coefficient. Create and test the appropriate hypotheses to evaluate these expectations at the 10-percent level. c. At first you expect FIRE to have a positive coefficient, but one of your friends says that fireplaces are messy and are a pain to keep clean, so you're not sure. Run a two-sided ttest around zero to test these
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