enseignement de base au niger :quel bilan - CONFEMEN
enseignement de base au niger :quel bilan - CONFEMEN
enseignement de base au niger :quel bilan - CONFEMEN
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------------------------------------------------------------------------------<br />
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]<br />
---------+--------------------------------------------------------------------<br />
0 | 169 457.2308 19.80588 257.4764 418.1303 496.3312<br />
1 | 71 220.338 10.50913 88.55151 199.3782 241.2978<br />
---------+--------------------------------------------------------------------<br />
combined | 240 387.15 15.89496 246.2436 355.8379 418.4621<br />
---------+--------------------------------------------------------------------<br />
diff | 236.8927 31.33887 175.1558 298.6297<br />
------------------------------------------------------------------------------<br />
Degrees of freedom: 238<br />
Ho: mean(0) - mean(1) = diff = 0<br />
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0<br />
t = 7.5591 t = 7.5591 t = 7.5591<br />
P < t = 1.0000 P > |t| = 0.0000 P > t = 0.0000<br />
L'introduction <strong>de</strong> la variable TAILLECO permet d'améliorer le pouvoir explicatif du modèle<br />
même si celui-ci <strong>de</strong>meure globalment mo<strong>de</strong>ste. Par ailleurs, on observe que la variable<br />
VOLONTAIRE se rapproche <strong>de</strong> la significativité. Il n'y a guère d'inci<strong>de</strong>nce sur les <strong>au</strong>tres<br />
variables du modèle.<br />
Modèle VI.2<br />
Probit estimates Number of obs = 240<br />
LR chi2(14) = 29.40<br />
Prob > chi2 = 0.0092<br />
Log likelihood = -147.59884 Pseudo R2 = 0.0906<br />
------------------------------------------------------------------------------<br />
MTREST~S | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ]<br />
---------+--------------------------------------------------------------------<br />
MAITRFEM*| .1134706 .078805 1.44 0.149 .666667 -.040984 .267926<br />
MTSEUL*| .0423727 .0868267 0.48 0.629 .225 -.127805 .21255<br />
VOLONT~E*| .1072567 .0760105 1.39 0.164 .391667 -.041721 .256235<br />
DIPCYCLB*| -.1200853 .0884883 -1.36 0.172 .25 -.293519 .053349<br />
NIVCYCLB*| -.0674448 .0798569 -0.85 0.397 .375 -.223961 .089072<br />
MTMOBILE*| .1057821 .0846228 1.21 0.228 .175 -.060075 .27164<br />
DOUBLFLX*| .0458859 .1318316 0.34 0.732 .083333 -.212499 .304271<br />
MTAUTR~T*| -.1799395 .0742379 -2.41 0.016 .333333 -.325443 -.034436<br />
APEACTIV*| .0508876 .0678859 0.75 0.454 .4875 -.082166 .183941<br />
CONSPE~G*| .0852968 .0680285 1.24 0.215 .391667 -.048037 .21863<br />
MTCONC~R*| .0652905 .1059508 0.62 0.534 .875 -.142369 .27295<br />
MTCHAN~O*| -.0156859 .071356 -0.22 0.826 .383333 -.155541 .124169<br />
TAILLECO | -.0003547 .0001668 -2.13 0.033 387.15 -.000682 -.000028<br />
ABSMT | -.0265008 .0117816 -2.25 0.024 1.37917 -.049592 -.003409<br />
---------+--------------------------------------------------------------------<br />
obs. P | .5916667<br />
pred. P | .5977048 (at x-bar)<br />
------------------------------------------------------------------------------<br />
(*) dF/dx is for discrete change of dummy variable from 0 to 1<br />
z and P>|z| are the test of the un<strong>de</strong>rlying coefficient being 0<br />
Nous allons maintenant distinguer les contractuels ayant suivi une formation professionnelle<br />
longue <strong>de</strong>s <strong>au</strong>tres contractuels.<br />
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