parental income (pincome, in $1000 per month). дра B1 + B₂hsgpa + B3pincome + e. Empirical estimation using observations generates the following STATA output. = Source Model Residual Total SS 1273.27041 671.879965 df MS Number of obs F(2, 3997) Prob > F R-squared Adj R-squared Root MSE |||||||||||| 4,0

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Author:Amos Gilat
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Consider a simple OLS model that regresses college GPA (gpa) on high school GPA (hsgpa) and
parental income (pincome, in $1000 per month).
gpa = B1 + B₂hsgpa + B3pincome + e.
Empirical estimation using observations generates the following STATA output.
Source
Model
Residual
Total
gpa
hsgpa
pincome
_cons
SS
1273.27041
671.879965
Coef.
df
Std. Err.
.6866603 .0103571
.3033177 .0066108
-.6697239
.0358777
O [0.290360532, 0.316274868]
O [0.2967069, 0.3099285]
O [0.292442934, 0.314185466]
O [-0.012957168, 0.012957168]
MS
t
Construct the 95% confidence interval for parental income.
Number of obs
F(2, 3997)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
=
=
=
=
4,000
[95% Conf. Interval]
Transcribed Image Text:Consider a simple OLS model that regresses college GPA (gpa) on high school GPA (hsgpa) and parental income (pincome, in $1000 per month). gpa = B1 + B₂hsgpa + B3pincome + e. Empirical estimation using observations generates the following STATA output. Source Model Residual Total gpa hsgpa pincome _cons SS 1273.27041 671.879965 Coef. df Std. Err. .6866603 .0103571 .3033177 .0066108 -.6697239 .0358777 O [0.290360532, 0.316274868] O [0.2967069, 0.3099285] O [0.292442934, 0.314185466] O [-0.012957168, 0.012957168] MS t Construct the 95% confidence interval for parental income. Number of obs F(2, 3997) Prob > F R-squared Adj R-squared Root MSE P>|t| = = = = 4,000 [95% Conf. Interval]
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