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 houseBathroom= Unit number of bathrooms in a houseDriveway= A binary variable specifying if "Driveway=1" the house has a driveway, and if "Driveway=0" otherwiseGarage= A binary variable specifying if "Garage=1" the house has a single-door garage, and if "Garage=0" otherwiseA 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 arelationship 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 thesteps 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 youwrite down all necessary equations & steps required. Show your calculations. Model 1: OLS, using observations 1-1000Dependent variable: PriceCoefficient220.811р-value<0.0001Std. Errort-ratioconst20.798610.62***Lotsize25.46803.396007.499<0.0001***Bedroom71.66326.2381311.49<0.0001***Bathroom107.26329.95423.5810.0004***DrivewayGarage56.01774.3006213.03<0.0001***129.56014.77078.771<0.0001***Mean dependent varSum squared residR-squaredF(5, 994)Log-likelihoodSchwarz criterionS.D. dependent varS.E. of regressionAdjusted R-squaredP-value(F)Akaike criterion214.3293172.0604607.1195294271620.3587610.355536111.22492.22e-93-6563.77513139.5513169.00Hannan-Quinn13150.74Model 2: OLS, using observations 1-1000Dependent variable: PriceCoefficient354.158р-value<0.0001Std. Errort-ratioconst20.875716.97***Lotsize17.21303.769474.566<0.0001***Bedroom84.20326.9712112.08<0.0001***Bathroom140.91833.21094.243<0.0001***Mean dependent varSum squared residR-squaredF(3, 996)Log-likelihoodSchwarz criterionS.D. dependent varS.E. of regressionAdjusted R-squaredP-value(F)Akaike criterion607.1195214.329337387258193.74570.1853050.18285175.514465.16e-44-6683.48113374.9613394.59Hannan-Quinn13382.42SSRUnrestricted)/q(SSRrestrictedSSRunrestricted/(nF =kunrestricted1)Or:(R²,R² restricted)/9unrestricted(1 – R².unrestricted)/(n – kunrestricted- 1)

Sample size=1000 Dependent variable=price 5 independent variables

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.

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 1E