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Table 4.2, page 114
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord1, clear
correlate income water80 educat retire peop81 cpeop water81
(obs=496)
| income water80 educat retire peop81 cpeop water81
-------------+---------------------------------------------------------------
income | 1.0000
water80 | 0.3371 1.0000
educat | 0.3463 0.0982 1.0000
retire | -0.3806 -0.2919 -0.1742 1.0000
peop81 | 0.3113 0.5251 0.0587 -0.3757 1.0000
cpeop | 0.0911 -0.0312 0.0055 -0.0585 0.1443 1.0000
water81 | 0.4178 0.7648 0.0404 -0.2731 0.6183 0.0661 1.0000
Figure 4.1, page 115. The option symbol(p) means that a small plus sign will be used, and the half option means that only the lower half of the graph will be displayed.
graph matrix income water80 educat retire peop81 cpeop water81, half msymbol(p)
Figure 4.2, page 115
use http://www.ats.ucla.edu/stat/stata/examples/rwg/nations, clear
graph matrix encon85 popgro85 fert84 birthr85, half
Figure 4.3, page 116 The yline(0) option draws a horizontal line at 0.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord1, clear
regress water81 income water80 educat retire peop81 cpeop peop80
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 6, 489) = 171.08
Model | 740477522 6 123412920 Prob > F = 0.0000
Residual | 352761188 489 721393.022 R-squared = 0.6773
-------------+------------------------------ Adj R-squared = 0.6734
Total | 1.0932e+09 495 2208563.05 Root MSE = 849.35
------------------------------------------------------------------------------
water81 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
income | 20.96699 3.463719 6.05 0.000 14.16138 27.7726
water80 | .49194 .0263478 18.67 0.000 .440171 .5437089
educat | -41.86552 13.22031 -3.17 0.002 -67.84114 -15.8899
retire | 189.1843 95.02142 1.99 0.047 2.483674 375.885
peop81 | (dropped)
cpeop | 344.6506 80.80625 4.27 0.000 185.8803 503.4209
peop80 | 248.197 28.7248 8.64 0.000 191.7578 304.6363
_cons | 242.2204 206.8638 1.17 0.242 -164.2312 648.6721
------------------------------------------------------------------------------
rvfplot,yline(0) ylabel(-4000(2000)4000) xlabel(0(2000)8000)
Figure 4.4, page 117
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord1, clear
regress water81 income water80 educat retire peop81 cpeop peop80
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 6, 489) = 171.08
Model | 740477522 6 123412920 Prob > F = 0.0000
Residual | 352761188 489 721393.022 R-squared = 0.6773
-------------+------------------------------ Adj R-squared = 0.6734
Total | 1.0932e+09 495 2208563.05 Root MSE = 849.35
------------------------------------------------------------------------------
water81 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
income | 20.96699 3.463719 6.05 0.000 14.16138 27.7726
water80 | .49194 .0263478 18.67 0.000 .440171 .5437089
educat | -41.86552 13.22031 -3.17 0.002 -67.84114 -15.8899
retire | 189.1843 95.02142 1.99 0.047 2.483674 375.885
peop81 | (dropped)
cpeop | 344.6506 80.80625 4.27 0.000 185.8803 503.4209
peop80 | 248.197 28.7248 8.64 0.000 191.7578 304.6363
_cons | 242.2204 206.8638 1.17 0.242 -164.2312 648.6721
------------------------------------------------------------------------------
predict yhat
predict e, residual
gen abse = abs(e)
graph twoway (scatter abse yhat) (mband abse yhat, bands(6) )
Figure 4.5, page 119
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord2, clear
graph twoway line H2Ouse time, ylabel(3.5(.5)5.5) xlabel(1 20(20)120) xline(127)
Figure 4.6, page 121
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord2, clear
regress H2Ouse temp rain educ
Source | SS df MS Number of obs = 137
-------------+------------------------------ F( 3, 133) = 21.57
Model | 8.40906094 3 2.80302031 Prob > F = 0.0000
Residual | 17.2802366 133 .129926591 R-squared = 0.3273
-------------+------------------------------ Adj R-squared = 0.3122
Total | 25.6892976 136 .188891894 Root MSE = .36045
------------------------------------------------------------------------------
H2Ouse | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
temp | .0128571 .0016975 7.57 0.000 .0094995 .0162147
rain | -.0474281 .021229 -2.23 0.027 -.0894182 -.005438
educ | -.2469767 .1134846 -2.18 0.031 -.4714448 -.0225086
_cons | 3.828001 .1006446 38.03 0.000 3.62893 4.027072
------------------------------------------------------------------------------
predict resid, residual
tsset time
time variable: time, 1 to 137
ac resid, lags(25) xlabel(0(2)24) ylabel(-.2(.2)1)
Figure 4.7, page 122 The c(s) option is short for connect(s), and the s stands for smooth.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord2, clear
regress H2Ouse temp rain educ
Source | SS df MS Number of obs = 137
-------------+------------------------------ F( 3, 133) = 21.57
Model | 8.40906094 3 2.80302031 Prob > F = 0.0000
Residual | 17.2802366 133 .129926591 R-squared = 0.3273
-------------+------------------------------ Adj R-squared = 0.3122
Total | 25.6892976 136 .188891894 Root MSE = .36045
------------------------------------------------------------------------------
H2Ouse | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
temp | .0128571 .0016975 7.57 0.000 .0094995 .0162147
rain | -.0474281 .021229 -2.23 0.027 -.0894182 -.005438
educ | -.2469767 .1134846 -2.18 0.031 -.4714448 -.0225086
_cons | 3.828001 .1006446 38.03 0.000 3.62893 4.027072
------------------------------------------------------------------------------
predict e, resid
graph twoway line e time, ylabel(-.8(.2).8) xlabel(0 20(20)120) yline(0)
Figure 4.8, page 122
Graph 1:
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord2, clear
regress H2Ouse temp rain educ
Source | SS df MS Number of obs = 137
-------------+------------------------------ F( 3, 133) = 21.57
Model | 8.40906094 3 2.80302031 Prob > F = 0.0000
Residual | 17.2802366 133 .129926591 R-squared = 0.3273
-------------+------------------------------ Adj R-squared = 0.3122
Total | 25.6892976 136 .188891894 Root MSE = .36045
------------------------------------------------------------------------------
H2Ouse | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
temp | .0128571 .0016975 7.57 0.000 .0094995 .0162147
rain | -.0474281 .021229 -2.23 0.027 -.0894182 -.005438
educ | -.2469767 .1134846 -2.18 0.031 -.4714448 -.0225086
_cons | 3.828001 .1006446 38.03 0.000 3.62893 4.027072
------------------------------------------------------------------------------
predict e, resid
graph twoway line e time, ylabel(-.8(.2).8) xlabel(1 20(20)120) yline(0)
Graph 2:
gen e1 = e[_n-1]/3 + e[_n]/3 + e[_n+1]/3 (2 missing values generated) graph twoway line e1 time, ylabel(-.8(.2).8) xlabel(1 20(20)120) yline(0)
Graph 3:
gen e2 = e1[_n-1]/3 + e1[_n]/3 + e1[_n+1]/3 (4 missing values generated) graph twoway line e2 time, ylabel(-.8(.2).8) xlabel(1 20(20)120) yline(0)
Graph 4:
gen e3 = e2[_n-1]/3 + e1[_n]/3 + e1[_n+1]/3 (5 missing values generated) gen e4 = e3[_n-1]/3 + e1[_n]/3 + e1[_n+1]/3 (6 missing values generated) gen e5 = e4[_n-1]/3 + e1[_n]/3 + e1[_n+1]/3 (7 missing values generated) graph twoway line e5 time, ylabel(-.8(.2).8) xlabel(1 20(20)120) yline(0)
Figure 4.9, page 124.
Histogram: The bin option allows you to select how many bins the histogram will have.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord1, clear
regress water81 income water80 educat retire peop81 cpeop
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 6, 489) = 171.08
Model | 740477522 6 123412920 Prob > F = 0.0000
Residual | 352761188 489 721393.022 R-squared = 0.6773
-------------+------------------------------ Adj R-squared = 0.6734
Total | 1.0932e+09 495 2208563.05 Root MSE = 849.35
------------------------------------------------------------------------------
water81 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
income | 20.96699 3.463719 6.05 0.000 14.16138 27.7726
water80 | .49194 .0263478 18.67 0.000 .440171 .5437089
educat | -41.86552 13.22031 -3.17 0.002 -67.84114 -15.8899
retire | 189.1843 95.02142 1.99 0.047 2.483674 375.885
peop81 | 248.197 28.7248 8.64 0.000 191.7578 304.6363
cpeop | 96.4536 80.51903 1.20 0.232 -61.75235 254.6596
_cons | 242.2204 206.8638 1.17 0.242 -164.2312 648.6721
------------------------------------------------------------------------------
predict e, residual
histogram e, fraction norm bin(13) xlabel(-4000(2000)4000) ylabel(0(.1).4)
Boxplot:
graph box e, yline(0) ylabel(-4000(2000)4000)
Symmerty plot:
symplot e, xlabel(0 2000 4000) ylabel(0 2000 4000)
Quantile normal plot:
qnorm e, ylabel(-4000(2000)4000) xlabel(-2000 0 2000)
Table 4.3, page 127.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord1, clear
regress water81 income water80 educat retire peop81 cpeop
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 6, 489) = 171.08
Model | 740477522 6 123412920 Prob > F = 0.0000
Residual | 352761188 489 721393.022 R-squared = 0.6773
-------------+------------------------------ Adj R-squared = 0.6734
Total | 1.0932e+09 495 2208563.05 Root MSE = 849.35
------------------------------------------------------------------------------
water81 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
income | 20.96699 3.463719 6.05 0.000 14.16138 27.7726
water80 | .49194 .0263478 18.67 0.000 .440171 .5437089
educat | -41.86552 13.22031 -3.17 0.002 -67.84114 -15.8899
retire | 189.1843 95.02142 1.99 0.047 2.483674 375.885
peop81 | 248.197 28.7248 8.64 0.000 191.7578 304.6363
cpeop | 96.4536 80.51903 1.20 0.232 -61.75235 254.6596
_cons | 242.2204 206.8638 1.17 0.242 -164.2312 648.6721
------------------------------------------------------------------------------
predict DBincome, dfbeta(income)
predict DBwtr80, dfbeta(water80)
predict DBeducat, dfbeta(educat)
predict DBretire, dfbeta(retire)
predict DBpeop81, dfbeta(peop81)
predict DBcpeop, dfbeta(cpeop)
summarize DBincome DBwtr80 DBeducat DBretire DBpeop81 DBcpeop
Variable | Obs Mean Std. Dev. Min Max
-------------+-----------------------------------------------------
DBincome | 496 .0004404 .0907714 -.248154 1.340371
DBwtr80 | 496 -.0002074 .0895012 -1.38789 .6845321
DBeducat | 496 -.0001973 .0434508 -.3601133 .1023189
DBretire | 496 .0000682 .0445025 -.216704 .2785343
DBpeop81 | 496 .0000747 .0612796 -.4263415 .4554408
DBcpeop | 496 -.0000535 .0406626 -.23371 .3507265
Figure 4.10, page 127.
regress water81 income water80 educat retire peop81 cpeop peop80
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 6, 489) = 171.08
Model | 740477522 6 123412920 Prob > F = 0.0000
Residual | 352761188 489 721393.022 R-squared = 0.6773
-------------+------------------------------ Adj R-squared = 0.6734
Total | 1.0932e+09 495 2208563.05 Root MSE = 849.35
------------------------------------------------------------------------------
water81 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
income | 20.96699 3.463719 6.05 0.000 14.16138 27.7726
water80 | .49194 .0263478 18.67 0.000 .440171 .5437089
educat | -41.86552 13.22031 -3.17 0.002 -67.84114 -15.8899
retire | 189.1843 95.02142 1.99 0.047 2.483674 375.885
peop81 | (dropped)
cpeop | 344.6506 80.80625 4.27 0.000 185.8803 503.4209
peop80 | 248.197 28.7248 8.64 0.000 191.7578 304.6363
_cons | 242.2204 206.8638 1.17 0.242 -164.2312 648.6721
------------------------------------------------------------------------------
avplot income, xlabel(-20(20)60) ylabel(-4000(2000)4000)
Figure 4.11, page 128. The DFBETAs need to be scaled according to the endnote. The [w=rcd] option does this.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord1, clear
regress water81 income water80 educat retire peop81 cpeop
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 6, 489) = 171.08
Model | 740477522 6 123412920 Prob > F = 0.0000
Residual | 352761188 489 721393.022 R-squared = 0.6773
-------------+------------------------------ Adj R-squared = 0.6734
Total | 1.0932e+09 495 2208563.05 Root MSE = 849.35
------------------------------------------------------------------------------
water81 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
income | 20.96699 3.463719 6.05 0.000 14.16138 27.7726
water80 | .49194 .0263478 18.67 0.000 .440171 .5437089
educat | -41.86552 13.22031 -3.17 0.002 -67.84114 -15.8899
retire | 189.1843 95.02142 1.99 0.047 2.483674 375.885
peop81 | 248.197 28.7248 8.64 0.000 191.7578 304.6363
cpeop | 96.4536 80.51903 1.20 0.232 -61.75235 254.6596
_cons | 242.2204 206.8638 1.17 0.242 -164.2312 648.6721
------------------------------------------------------------------------------
predict DBincome, dfbeta(income)
regress water81 water80 educat retire peop81 cpeop peop80
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 5, 490) = 184.54
Model | 714043771 5 142808754 Prob > F = 0.0000
Residual | 379194939 490 773867.223 R-squared = 0.6531
-------------+------------------------------ Adj R-squared = 0.6496
Total | 1.0932e+09 495 2208563.05 Root MSE = 879.70
------------------------------------------------------------------------------
water81 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
water80 | .5218759 .0268043 19.47 0.000 .4692104 .5745414
educat | -17.03541 13.01692 -1.31 0.191 -42.61128 8.540463
retire | 47.74677 95.39495 0.50 0.617 -139.6869 235.1804
peop81 | 263.9229 29.62926 8.91 0.000 205.7068 322.139
cpeop | 134.9 83.13626 1.62 0.105 -28.44755 298.2475
peop80 | (dropped)
_cons | 291.3458 214.0905 1.36 0.174 -129.3028 711.9944
------------------------------------------------------------------------------
predict yresid, residual
regress income water80 educat retire peop81 cpeop peop80
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 5, 490) = 39.56
Model | 24271.7315 5 4854.3463 Prob > F = 0.0000
Residual | 60129.3572 490 122.712974 R-squared = 0.2876
-------------+------------------------------ Adj R-squared = 0.2803
Total | 84401.0887 495 170.50725 Root MSE = 11.078
------------------------------------------------------------------------------
income | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
water80 | .0014278 .0003375 4.23 0.000 .0007646 .002091
educat | 1.184248 .1639156 7.22 0.000 .8621833 1.506312
retire | -6.745725 1.201261 -5.62 0.000 -9.105983 -4.385467
peop81 | .7500293 .3731065 2.01 0.045 .0169433 1.483115
cpeop | 1.833663 1.046893 1.75 0.080 -.2232916 3.890617
peop80 | (dropped)
_cons | 2.342986 2.695935 0.87 0.385 -2.954032 7.640004
------------------------------------------------------------------------------
predict xresid, residual
gen rcd = DBincome
replace rcd = . if abs(DBincome) > 2
(0 real changes made)
replace rcd = [(99/18)*abs(DBincome)*((abs(DBincome))+1)^2]+1
(496 real changes made)
replace rcd = 2 if abs(DBincome) ==.
(0 real changes made)
graph twoway (scatter yresid xresid [w=rcd], msymbol(oh)) (lfit yresid xresid), ///
ylabel(-4000(2000)4000) xlabel(-20(20)60)
Figure 4.12, page 129.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord1, clear
regress water81 income water80 educat retire peop81 cpeop
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 6, 489) = 171.08
Model | 740477522 6 123412920 Prob > F = 0.0000
Residual | 352761188 489 721393.022 R-squared = 0.6773
-------------+------------------------------ Adj R-squared = 0.6734
Total | 1.0932e+09 495 2208563.05 Root MSE = 849.35
------------------------------------------------------------------------------
water81 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
income | 20.96699 3.463719 6.05 0.000 14.16138 27.7726
water80 | .49194 .0263478 18.67 0.000 .440171 .5437089
educat | -41.86552 13.22031 -3.17 0.002 -67.84114 -15.8899
retire | 189.1843 95.02142 1.99 0.047 2.483674 375.885
peop81 | 248.197 28.7248 8.64 0.000 191.7578 304.6363
cpeop | 96.4536 80.51903 1.20 0.232 -61.75235 254.6596
_cons | 242.2204 206.8638 1.17 0.242 -164.2312 648.6721
------------------------------------------------------------------------------
predict DBwtr80, dfbeta(water80)
regress water81 income educat retire peop81 cpeop peop80
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 5, 490) = 79.31
Model | 488996056 5 97799211.3 Prob > F = 0.0000
Residual | 604242653 490 1233148.27 R-squared = 0.4473
-------------+------------------------------ Adj R-squared = 0.4417
Total | 1.0932e+09 495 2208563.05 Root MSE = 1110.5
------------------------------------------------------------------------------
water81 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
income | 33.10548 4.448118 7.44 0.000 24.36574 41.84522
educat | -42.21332 17.28474 -2.44 0.015 -76.17467 -8.251971
retire | 84.66143 124.019 0.68 0.495 -159.0132 328.3361
peop81 | (dropped)
cpeop | 376.5361 105.6257 3.56 0.000 169.0009 584.0713
peop80 | 491.5161 33.46848 14.69 0.000 425.7566 557.2755
_cons | 586.0337 269.3883 2.18 0.030 56.73503 1115.332
------------------------------------------------------------------------------
predict yresid, residual
regress water80 income educat retire peop81 cpeop peop80
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 5, 490) = 47.23
Model | 500781798 5 100156360 Prob > F = 0.0000
Residual | 1.0392e+09 490 2120731.64 R-squared = 0.3252
-------------+------------------------------ Adj R-squared = 0.3183
Total | 1.5399e+09 495 3110990.51 Root MSE = 1456.3
------------------------------------------------------------------------------
water80 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
income | 24.67473 5.833263 4.23 0.000 13.21344 36.13602
educat | -.7069974 22.66721 -0.03 0.975 -45.24391 43.82992
retire | -212.4708 162.6385 -1.31 0.192 -532.0258 107.0841
peop81 | (dropped)
cpeop | 64.81579 138.5176 0.47 0.640 -207.3459 336.9775
peop80 | 494.6113 43.89057 11.27 0.000 408.3744 580.8482
_cons | 698.8928 353.2758 1.98 0.048 4.770461 1393.015
------------------------------------------------------------------------------
predict xresid, residual
gen rcd1 = DBwtr80
replace rcd1 = . if abs(DBwtr80) > 2
(0 real changes made)
replace rcd1 = [(99/18)*abs(DBwtr80)*((abs(DBwtr80))+1)^2]+1
(496 real changes made)
replace rcd1 = 2 if abs(DBwtr80) ==.
graph twoway (scatter yresid xresid [w=rcd1], msymbol(oh)) (lfit yresid xresid), ///
ylabel(-2000(2000)6000) xlabel(-2000(0)8000)
Figure 4.13, page 129. These are done the same as above, dropping the different variables.
Figure 4.14, page 131. We use the regpt program to make graphs similar to the left and right panel of figure 4.14. You can download regpt from within Stata by typing findit regpt (see How can I use the findit command to search for programs and get additional help? for more information about using findit).
regpt
Table 4.4, page 133.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord1, clear
regress water81 income water80 educat retire peop81 cpeop
Source | SS df MS Number of obs = 496
-------------+------------------------------ F( 6, 489) = 171.08
Model | 740477522 6 123412920 Prob > F = 0.0000
Residual | 352761188 489 721393.022 R-squared = 0.6773
-------------+------------------------------ Adj R-squared = 0.6734
Total | 1.0932e+09 495 2208563.05 Root MSE = 849.35
------------------------------------------------------------------------------
water81 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
income | 20.96699 3.463719 6.05 0.000 14.16138 27.7726
water80 | .49194 .0263478 18.67 0.000 .440171 .5437089
educat | -41.86552 13.22031 -3.17 0.002 -67.84114 -15.8899
retire | 189.1843 95.02142 1.99 0.047 2.483674 375.885
peop81 | 248.197 28.7248 8.64 0.000 191.7578 304.6363
cpeop | 96.4536 80.51903 1.20 0.232 -61.75235 254.6596
_cons | 242.2204 206.8638 1.17 0.242 -164.2312 648.6721
------------------------------------------------------------------------------
predict DBincome, dfbeta(income)
predict DBwtr80, dfbeta(water80)
predict e, residual
predict h, leverage
predict z, rstandard
predict t, rstudent
predict D, cooksd
list case e h z t D DBincome DBwtr80 if D>=.15
Observation 94
case 101 e -4037.047 h .0817331
z -4.960134 t -5.08462 D .3128368
DBincome .0629841 DBwtr80 -1.38789
Observation 118
case 127 e 3315.585 h .061611
z 4.029793 t 4.094227 D .1523151
DBincome .980374 DBwtr80 -.1809267
Observation 125
case 134 e 3687.116 h .0903783
z 4.551666 t 4.646505 D .2940669
DBincome 1.340371 DBwtr80 .2510464
Table 4.5, page 134.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/concord1, clear
quietly regress water81 income water80 educat retire peop81 cpeop
* Stata 8 code.
vif
* Stata 9 code and output.
estat vif
Variable | VIF 1/VIF
-------------+----------------------
peop81 | 1.55 0.643154
water80 | 1.48 0.674804
income | 1.40 0.712424
retire | 1.29 0.775514
educat | 1.15 0.872996
cpeop | 1.04 0.956972
-------------+----------------------
Mean VIF | 1.32
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