The variables used in the analysis below include: house price ($1000s) - price, lot size (square feet) - lotsz, house square feet - sqft, and number of bedrooms - bdrms. Use the estimated OLS models to answer the questions below: Model A: In (price) =-.975+.873ln(sqft), (.085) (.641) n = 88; R² = .553 Model B: In(price) = -1.297 (.651) n = 88; R² = .643 +.700ln(sqft) + .168ln(lotsz) + .037bdrms, (.093) (.038) (.028) From Model B, test the null hypothesis that all of the slope parameters are jointly equal to zero, i.e. Ho: Bin(sqft) = 0, Bin(lotsz) = 0, 3bdrms = 0. Report the test statistic.
The variables used in the analysis below include: house price ($1000s) - price, lot size (square feet) - lotsz, house square feet - sqft, and number of bedrooms - bdrms. Use the estimated OLS models to answer the questions below: Model A: In (price) =-.975+.873ln(sqft), (.085) (.641) n = 88; R² = .553 Model B: In(price) = -1.297 (.651) n = 88; R² = .643 +.700ln(sqft) + .168ln(lotsz) + .037bdrms, (.093) (.038) (.028) From Model B, test the null hypothesis that all of the slope parameters are jointly equal to zero, i.e. Ho: Bin(sqft) = 0, Bin(lotsz) = 0, 3bdrms = 0. Report the test statistic.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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![The variables used in the analysis below include: house price ($1000s) – price, lot size (square feet) – lotsz, house square feet – sqft, and number of bedrooms – bdrms. Use the estimated OLS models to answer the questions below:
Model A:
\[
\ln(\widehat{\text{price}}) = -0.975 \, (\pm 0.641) + 0.873 \ln(\text{sqft}) \, (\pm 0.085),
\]
\[
n = 88; \, R^2 = 0.553
\]
Model B:
\[
\ln(\widehat{\text{price}}) = -1.297 \, (\pm 0.651) + 0.700 \ln(\text{sqft}) \, (\pm 0.093) + 0.168 \ln(\text{lotsz}) \, (\pm 0.038) + 0.037 \text{bdrms} \, (\pm 0.028),
\]
\[
n = 88; \, R^2 = 0.643
\]
From Model B, test the null hypothesis that all of the slope parameters are jointly equal to zero, i.e.
\[
H_0: \beta_{\ln(\text{sqft})} = 0, \, \beta_{\ln(\text{lotsz})} = 0, \, \beta_{\text{bdrms}} = 0.
\]
Report the test statistic.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ff2c90e6f-21b1-498d-a838-23f699b7822e%2F5cc6753a-0d25-4b98-a372-30c035f1d875%2Feklwged_processed.png&w=3840&q=75)
Transcribed Image Text:The variables used in the analysis below include: house price ($1000s) – price, lot size (square feet) – lotsz, house square feet – sqft, and number of bedrooms – bdrms. Use the estimated OLS models to answer the questions below:
Model A:
\[
\ln(\widehat{\text{price}}) = -0.975 \, (\pm 0.641) + 0.873 \ln(\text{sqft}) \, (\pm 0.085),
\]
\[
n = 88; \, R^2 = 0.553
\]
Model B:
\[
\ln(\widehat{\text{price}}) = -1.297 \, (\pm 0.651) + 0.700 \ln(\text{sqft}) \, (\pm 0.093) + 0.168 \ln(\text{lotsz}) \, (\pm 0.038) + 0.037 \text{bdrms} \, (\pm 0.028),
\]
\[
n = 88; \, R^2 = 0.643
\]
From Model B, test the null hypothesis that all of the slope parameters are jointly equal to zero, i.e.
\[
H_0: \beta_{\ln(\text{sqft})} = 0, \, \beta_{\ln(\text{lotsz})} = 0, \, \beta_{\text{bdrms}} = 0.
\]
Report the test statistic.
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