Question 5. Figure 1 shows the correlation between four variables in a data set containing 88 properties that have recently been sold: the house prices (price), number of bedrooms in the house (bdrms), the size of the lot (lotsize) and the area of the house, measured in square feet (sqrft). Figure 2 reports estimates of the regression . Source reg price bdrms lotsize Model Residual Total price = Bo + B₁bdrms, + ₂lotsize, + & Figure 1 corr price bdrms lotsize sqrft (obs-88) price bdrms lotsize cons price. bdrms lotsize sqrft SS 8.2891e+11 8.8940e+10 9.1785e+11 price 1.0000 0.5081 1.0000 0.9502 0.5453 1.0000 0.7879 0.5315 0.9101 df bdrms lotsize Figure 2 2 4.1446e+11 85 1.0464e+09 Coef. Std. Err. MS 87 1.0550e+10 -1754.92 4917.219 .2195683 .0092312 159878.7 15548.94 t p>|t| -0.36 0.722 23.79 0.000 10.28 0.000 sqrft 1.0000 85) - Number of obs = F( 2, Prob > F R-squared 88 396.10 = 0.0000 0.9031 0.9008 32347 Adj R-squared- Root MSE [95% Conf. Interval] -11531.67 .2012142 128963.3 8021.829 .2379225 190794.2 Using the evidence in Figures 1 and 2, what conclusions can you draw about the coefficient B₂? A. Owing to collinearity it is biased. B. The coefficient is insignificant at the 1% level. C. The coefficient is insignificant at the 5% level. D. Owing to endogeneity the coefficient is biased. E. None of the above.
Question 5. Figure 1 shows the correlation between four variables in a data set containing 88 properties that have recently been sold: the house prices (price), number of bedrooms in the house (bdrms), the size of the lot (lotsize) and the area of the house, measured in square feet (sqrft). Figure 2 reports estimates of the regression . Source reg price bdrms lotsize Model Residual Total price = Bo + B₁bdrms, + ₂lotsize, + & Figure 1 corr price bdrms lotsize sqrft (obs-88) price bdrms lotsize cons price. bdrms lotsize sqrft SS 8.2891e+11 8.8940e+10 9.1785e+11 price 1.0000 0.5081 1.0000 0.9502 0.5453 1.0000 0.7879 0.5315 0.9101 df bdrms lotsize Figure 2 2 4.1446e+11 85 1.0464e+09 Coef. Std. Err. MS 87 1.0550e+10 -1754.92 4917.219 .2195683 .0092312 159878.7 15548.94 t p>|t| -0.36 0.722 23.79 0.000 10.28 0.000 sqrft 1.0000 85) - Number of obs = F( 2, Prob > F R-squared 88 396.10 = 0.0000 0.9031 0.9008 32347 Adj R-squared- Root MSE [95% Conf. Interval] -11531.67 .2012142 128963.3 8021.829 .2379225 190794.2 Using the evidence in Figures 1 and 2, what conclusions can you draw about the coefficient B₂? A. Owing to collinearity it is biased. B. The coefficient is insignificant at the 1% level. C. The coefficient is insignificant at the 5% level. D. Owing to endogeneity the coefficient is biased. E. None of the above.
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter3: Functions And Graphs
Section3.6: Quadratic Functions
Problem 38E
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