predict resid, residuals gen residsq - resid^2 regress residsq bdrms sqrft Source s df MS Number of obs 72 F (2, 69) 4.84 Model 455633843 2 227816922 Prob > F 0.0108 Residual 3.2504e+09 69 47107747. 4 R-squared 0.1229 Adj R-squared 0.0975 Total 3.7061e+09 71 52198146.7 Root MSE 6863.5 residsq Coef. Std. Err. t P>|t| [95% Conf. Interval] bdrms 1097.839 1126.483 0.97 0.333 -1149.433 3345.11 sqrft 3.315872 1.644671 2.02 0.048 .0348425 6.596901 cons -7733.813 3789.974 -2.04 0.045 -15294.6 -173.0207
predict resid, residuals gen residsq - resid^2 regress residsq bdrms sqrft Source s df MS Number of obs 72 F (2, 69) 4.84 Model 455633843 2 227816922 Prob > F 0.0108 Residual 3.2504e+09 69 47107747. 4 R-squared 0.1229 Adj R-squared 0.0975 Total 3.7061e+09 71 52198146.7 Root MSE 6863.5 residsq Coef. Std. Err. t P>|t| [95% Conf. Interval] bdrms 1097.839 1126.483 0.97 0.333 -1149.433 3345.11 sqrft 3.315872 1.644671 2.02 0.048 .0348425 6.596901 cons -7733.813 3789.974 -2.04 0.045 -15294.6 -173.0207
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Please show your work. Thank you!
![predict resid, residuals
gen residsq = resid^2
regress residsq bdrms sgrft
Source
df
MS
Number of obs
72
F (2, 69)
4.84
=
Model
455633843
2
227816922
Prob > F
0.0108
Residual
3.2504e+09
69
47107747. 4
R-squared
0.1229
Adj R-squared
0.0975
Total
3.7061e+09
71
52198146.7
Root MSE
6863.5
residsą
Сoef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
bdrms
1097.839
1126.483
0.97
0.333
-1149.433
3345.11
sgrft
3.315872
1.644671
2.02
0.048
.0348425
6.596901
cons
-7733.813
3789.974
-2.04
0.045
-15294.6
-173.0207](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F7b8c9b98-5fbd-4d31-a6f4-3d74ac9a7d10%2F541ad91e-2d11-4770-ae71-892fadc3f3e7%2F8rpbd06_processed.png&w=3840&q=75)
Transcribed Image Text:predict resid, residuals
gen residsq = resid^2
regress residsq bdrms sgrft
Source
df
MS
Number of obs
72
F (2, 69)
4.84
=
Model
455633843
2
227816922
Prob > F
0.0108
Residual
3.2504e+09
69
47107747. 4
R-squared
0.1229
Adj R-squared
0.0975
Total
3.7061e+09
71
52198146.7
Root MSE
6863.5
residsą
Сoef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
bdrms
1097.839
1126.483
0.97
0.333
-1149.433
3345.11
sgrft
3.315872
1.644671
2.02
0.048
.0348425
6.596901
cons
-7733.813
3789.974
-2.04
0.045
-15294.6
-173.0207
Transcribed Image Text:QUESTION 5
Use the data in HPRICE_1.dta and estimate the regression model
price = Bo+ B, bdrms + B2 sqrft+u
while allowing for the possibility that the errors are heteroskedastic. What is the value of the F-statistic for testing the overall significance of the regression?
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