An empirical study is done on estimating the value of the houses in a city based on the following underlying factors: Price= Value of the house (in *1000 Dollars) otsize=Size of the property lot (in acres) Bedroom3 Unit number of bedrooms of a house Bathroom= Unit number of bathrooms in a house Driveway= A binary variable specifying if "Driveway=1" the house has a driveway, and if "Driveway=0" otherwise Garage= A binary variable specifying if "Garage=1" the house has a single-door garage, and if "Garage=0" otherwise A statistician has reviewed a database of 1000 records of houses sold in the city, and has come up with the following regression equation, to try to find a elationship among variables: Price(i)=B0 + B1[Lotsize(i)] + B2[Bedroom(i)] + B3[Bathroom(i)] + B4[Driveway(i)] + B5[Garage(i)] +u(i) A. By reviewing the result of GRETL outcomes in the attachment, perform a hypothesis test to verify the validity of all slope coefficients, collectively. Show the teps of the deployed hypothesis method. 3. Test if both Driveway & Garage need to be jointly included in the model as important variables in estimating the price of the houses in the city. Make sure ye vrite down all necessary equations & steps required. Show your calculations.
An empirical study is done on estimating the value of the houses in a city based on the following underlying factors: Price= Value of the house (in *1000 Dollars) otsize=Size of the property lot (in acres) Bedroom3 Unit number of bedrooms of a house Bathroom= Unit number of bathrooms in a house Driveway= A binary variable specifying if "Driveway=1" the house has a driveway, and if "Driveway=0" otherwise Garage= A binary variable specifying if "Garage=1" the house has a single-door garage, and if "Garage=0" otherwise A statistician has reviewed a database of 1000 records of houses sold in the city, and has come up with the following regression equation, to try to find a elationship among variables: Price(i)=B0 + B1[Lotsize(i)] + B2[Bedroom(i)] + B3[Bathroom(i)] + B4[Driveway(i)] + B5[Garage(i)] +u(i) A. By reviewing the result of GRETL outcomes in the attachment, perform a hypothesis test to verify the validity of all slope coefficients, collectively. Show the teps of the deployed hypothesis method. 3. Test if both Driveway & Garage need to be jointly included in the model as important variables in estimating the price of the houses in the city. Make sure ye vrite down all necessary equations & steps required. Show your calculations.
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
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Question
3)
![An empirical study is done on estimating the value of the houses in a city based on the following underlying factors:
Price= Value of the house (in *1000 Dollars)
Lotsize=Size of the property lot (in acres)
Bedroom= Unit number of bedrooms of a house
Bathroom= Unit number of bathrooms in a house
Driveway= A binary variable specifying if "Driveway=1" the house has a driveway, and if "Driveway=0" otherwise
Garage= A binary variable specifying if "Garage=1" the house has a single-door garage, and if "Garage=0" otherwise
A statistician has reviewed a database of 1000 records of houses sold in the city, and has come up with the following regression equation, to try to find a
relationship among variables:
Price(i)=B0 + B1[Lotsize(i)] + B2[Bedroom(i)] + B3[Bathroom(i)] + B4[Driveway(i)] + B5[Garage(i)] +u(i)
A. By reviewing the result of GRETL outcomes in the attachment, perform a hypothesis test to verify the validity of all slope coefficients, collectively. Show the
steps of the deployed hypothesis method.
B. Test if both Driveway & Garage need to be jointly included in the model as important variables in estimating the price of the houses in the city. Make sure you
write down all necessary equations & steps required. Show your calculations.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fe779c950-a70e-4610-a191-11ec3afdb7c4%2Ff643841c-98e3-408c-87db-e97f8b39ddbe%2Fdm1vuh_processed.png&w=3840&q=75)
Transcribed Image Text:An empirical study is done on estimating the value of the houses in a city based on the following underlying factors:
Price= Value of the house (in *1000 Dollars)
Lotsize=Size of the property lot (in acres)
Bedroom= Unit number of bedrooms of a house
Bathroom= Unit number of bathrooms in a house
Driveway= A binary variable specifying if "Driveway=1" the house has a driveway, and if "Driveway=0" otherwise
Garage= A binary variable specifying if "Garage=1" the house has a single-door garage, and if "Garage=0" otherwise
A statistician has reviewed a database of 1000 records of houses sold in the city, and has come up with the following regression equation, to try to find a
relationship among variables:
Price(i)=B0 + B1[Lotsize(i)] + B2[Bedroom(i)] + B3[Bathroom(i)] + B4[Driveway(i)] + B5[Garage(i)] +u(i)
A. By reviewing the result of GRETL outcomes in the attachment, perform a hypothesis test to verify the validity of all slope coefficients, collectively. Show the
steps of the deployed hypothesis method.
B. Test if both Driveway & Garage need to be jointly included in the model as important variables in estimating the price of the houses in the city. Make sure you
write down all necessary equations & steps required. Show your calculations.
Transcribed Image Text:Model 1: OLS, using observations 1-1000
Dependent variable: Price
Coefficient
220.811
р-value
<0.0001
Std. Error
t-ratio
const
20.7986
10.62
***
Lotsize
25.4680
3.39600
7.499
<0.0001
***
Bedroom
71.6632
6.23813
11.49
<0.0001
***
Bathroom
107.263
29.9542
3.581
0.0004
***
Driveway
Garage
56.0177
4.30062
13.03
<0.0001
***
129.560
14.7707
8.771
<0.0001
***
Mean dependent var
Sum squared resid
R-squared
F(5, 994)
Log-likelihood
Schwarz criterion
S.D. dependent var
S.E. of regression
Adjusted R-squared
P-value(F)
Akaike criterion
214.3293
172.0604
607.1195
29427162
0.358761
0.355536
111.2249
2.22e-93
-6563.775
13139.55
13169.00
Hannan-Quinn
13150.74
Model 2: OLS, using observations 1-1000
Dependent variable: Price
Coefficient
354.158
р-value
<0.0001
Std. Error
t-ratio
const
20.8757
16.97
***
Lotsize
17.2130
3.76947
4.566
<0.0001
***
Bedroom
84.2032
6.97121
12.08
<0.0001
***
Bathroom
140.918
33.2109
4.243
<0.0001
***
Mean dependent var
Sum squared resid
R-squared
F(3, 996)
Log-likelihood
Schwarz criterion
S.D. dependent var
S.E. of regression
Adjusted R-squared
P-value(F)
Akaike criterion
607.1195
214.3293
37387258
193.7457
0.185305
0.182851
75.51446
5.16e-44
-6683.481
13374.96
13394.59
Hannan-Quinn
13382.42
SSRUnrestricted)/q
(SSRrestricted
SSRunrestricted/(n
F =
kunrestricted
1)
Or:
(R²,
R² restricted)/9
unrestricted
(1 – R².
unrestricted)/(n – kunrestricted
- 1)
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