How do you interpret the R-squared obtained from running this regression? . regress wages education workexp unionmember south ib(last).occupation femaleSourceModelResidualTotalwageseducationworkexpunionmembersouthoccupationManagementSalesClericalServiceProfessionalfemaleconsSS4477.324989599.3735714076.6985Coefficient.6722878.09369551.517377.6885843df·10447.732498523 18.3544428Std. err.533 26.41031620990429. 0165623.50835654150423MS2.799024.756551-1.17642.8139971-.54809446319067-1.349161 .60659751.531115.6888258-1.845267 .4152299-.8195072 1.348736Number of obsF(10, 523)Prob > FR-squaredAdj R-squaredRoot MSEt P>|t|6.79 0.0005.66 0.0002.98 0.003-1.66 0.0983.70 0.000-1.45 0.149-0.87 0.3860.027-2.222.22 0.027-4.44 0.000-0.61 0.544===.4777169.0611587.5187055-1.503939=1.312772-2.775525-1.789482-2.540828. 1779095=-2.66099-3.469113=[95% conf. interval]53424.390.00000.31810.30504.28428668586. 12623222.516049.12677054.285276.4226859.6932927-.15749362.88432-1.0295441.830098

R-square is the goodness of fit for linear regression model. It basically shows how regression model…

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How do you interpret the R-squared obtained from running this regression

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Question

How do you interpret the R-squared obtained from running this regression?

. regress wages education workexp unionmember south ib(last).occupation female
Source
Model
Residual
Total
wages
education
workexp
unionmember
south
occupation
Management
Sales
Clerical
Service
Professional
female
cons
SS
4477.32498
9599.37357
14076.6985
Coefficient
.6722878
.0936955
1.517377
.6885843
df
·
10
447.732498
523 18.3544428
Std. err.
533 26.4103162
0990429
. 0165623
.5083565
4150423
MS
2.799024
.756551
-1.17642
.8139971
-.5480944
6319067
-1.349161 .6065975
1.531115
.6888258
-1.845267 .4152299
-.8195072 1.348736
Number of obs
F(10, 523)
Prob > F
R-squared
Adj R-squared
Root MSE
t P>|t|
6.79 0.000
5.66 0.000
2.98 0.003
-1.66 0.098
3.70 0.000
-1.45 0.149
-0.87 0.386
0.027
-2.22
2.22 0.027
-4.44 0.000
-0.61 0.544
=
=
=
.4777169
.0611587
.5187055
-1.503939
=
1.312772
-2.775525
-1.789482
-2.540828
. 1779095
=
-2.66099
-3.469113
=
[95% conf. interval]
534
24.39
0.0000
0.3181
0.3050
4.2842
8668586
. 1262322
2.516049
.1267705
4.285276
.4226859
.6932927
-.1574936
2.88432
-1.029544
1.830098

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