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

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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

Expert Solution

Step 1

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

It represents the percentage of variation for dependent variable that explained by independent variables.

R-square lies between 0-1 or 0-100% where 0% means no variation in the response variable from its mean, whereas 100% full variation from its mean value.

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