d) The estimates of the model Y_(i)=eta _(0)+eta _(1)x_(1i)+eta _(2)x_(2i)+epsi _(i) are presented below.
Assume that you divide the variables Y and x_(l) by 10 and make no changes to x_(2). You estimate the
model again using the scaled variables Y and x_(1). Referring to the Stata output above, explain which
changes you expect to see on the estimated coefficients (including the constant), the estimated
standard errors, the t-statistics, probability values and the goodness of fit of the model.
d) The estimates of the model Y; = o + X + Xi + &; are presented below.
Source
ss
dp
MS
Number of obs F(2,47) Prob > F R-squared Adj R-squared Root MSE
50 20.47 0.0000 0.4655 0.4428 33.269
=
Model Residual
45310.4785 52019.7006
2 22655.2393 47 1106.80214
Total
97330.1792
49 1986.33019
Y
Coefficient Std.err.
t
P>|t|
[95% conf.interval]
X1 X2 _cons
.0266161 1.137443 61.85654
.0139976 .2608716 6.394451
1.90 4.36 9.67
0.063 0.000 0.000
-.0015435 .612637 48.99256
.0547758 1.662249 74.72051
Assume that you divide the variables Y and X by 10 and make no changes to X2. You estimate the model again using the scaled variables Y and X.. Referring to the Stata output above, explain which changes you expect to see on the estimated coefficients (including the constant), the estimated standard errors, the t-statistics, probability values and the goodness of fit of the model.