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 . Model Residual reg price bdrms lotsize. Source ss Total price price = Bo + B₁bdrms, + B₂lotsize, + & Figure 1 . corr price bdrms lotsize sqrft (oba-88) bdrms lotsize _cons price. bdrms lotsize sqrft 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 df bdrms lotsize Figure 2 MS 2 4.1446e+11 85 1.0464e+09 Coef. Std. Err. 87 1.0550e+10 0.9101 1.0000 t P>|t| sqrft -1754.92 4917.219 -0.36 0.722 .2195683 .0092312 23.79 0.000 159878.7 15548.94 10.28 0.000 Number of obs= F( 2, 85) - Prob > F R-squared 88 396.10 0.0000 -0.9031 Adj R-squared - 0.9008 Root MSE 32347 [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 . Model Residual reg price bdrms lotsize. Source ss Total price price = Bo + B₁bdrms, + B₂lotsize, + & Figure 1 . corr price bdrms lotsize sqrft (oba-88) bdrms lotsize _cons price. bdrms lotsize sqrft 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 df bdrms lotsize Figure 2 MS 2 4.1446e+11 85 1.0464e+09 Coef. Std. Err. 87 1.0550e+10 0.9101 1.0000 t P>|t| sqrft -1754.92 4917.219 -0.36 0.722 .2195683 .0092312 23.79 0.000 159878.7 15548.94 10.28 0.000 Number of obs= F( 2, 85) - Prob > F R-squared 88 396.10 0.0000 -0.9031 Adj R-squared - 0.9008 Root MSE 32347 [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.
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Author:Amos Gilat
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![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
Model
Residual
Total
.reg price bdrms lotsize
price
price = Bo + B₁bdrms, + ß₂lotsize, + &₁
Figure 1
corr price bdrms lotsize sqrft
(obs-88)
bdrms
lotsize
_cons
.
price
bdrms
lotsize
sqrft
SS
8.2891e+11
8.8940e+10
9.1785e+11
price bdrms lotsize sqrft
1.0000
0.5081
1.0000
0.9502 0.5453 1.0000
0.7879 0.5315
0.9101
df
Figure 2
MS
2 4.1446e+11
85 1.0464e+09
87 1.0550e+10
Coef. Std. Err.
t
P>|t|
-1754.92 4917.219 -0.36 0.722
.2195683 .0092312 23.79 0.000
159878.7 15548.94 10.28 0.000
C. The coefficient is insignificant at the 5% level.
D. Owing to endogeneity the coefficient is biased.
E. None of the above.
1.0000
85) =
Number of obs =
F( 2,
Prob > F
R-squared
Adj R-squared =
Root MSE
=
-11531.67
.2012142
128963.3
=
88
396.10
0.0000
0.9031
0.9008
32347
[95% Conf. Interval]
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.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa504c1dc-36bb-4c1c-9ee6-66df6aada4b2%2F8cd97059-25ef-40c5-9992-4c94fcdcaa0f%2Fz1wt14_processed.png&w=3840&q=75)
Transcribed Image Text: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
Model
Residual
Total
.reg price bdrms lotsize
price
price = Bo + B₁bdrms, + ß₂lotsize, + &₁
Figure 1
corr price bdrms lotsize sqrft
(obs-88)
bdrms
lotsize
_cons
.
price
bdrms
lotsize
sqrft
SS
8.2891e+11
8.8940e+10
9.1785e+11
price bdrms lotsize sqrft
1.0000
0.5081
1.0000
0.9502 0.5453 1.0000
0.7879 0.5315
0.9101
df
Figure 2
MS
2 4.1446e+11
85 1.0464e+09
87 1.0550e+10
Coef. Std. Err.
t
P>|t|
-1754.92 4917.219 -0.36 0.722
.2195683 .0092312 23.79 0.000
159878.7 15548.94 10.28 0.000
C. The coefficient is insignificant at the 5% level.
D. Owing to endogeneity the coefficient is biased.
E. None of the above.
1.0000
85) =
Number of obs =
F( 2,
Prob > F
R-squared
Adj R-squared =
Root MSE
=
-11531.67
.2012142
128963.3
=
88
396.10
0.0000
0.9031
0.9008
32347
[95% Conf. Interval]
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.
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