具有30个离散，4个连续输入变量和1个连续变量的线性回归模型的VIF值限制（如4,5,6,7，......）应该是什么？
令人困惑的是，不同的研究人员建议使用不同的VIF值。
我在SPSS中尝试过，并为离散变量创建虚拟变量。结果如下
系数
模型非标准化系数标准化系数t Sig。共线性统计
B标准。错误Beta容差VIF
（常数）.076 1.262 .060 .952
缺席.014 .012 .020 1.170 .243 .776 1.289
G1 .129 .039 .109 3.326 .001 .214 4.665
G2 .857 .036 .773 23.541 .000 .215 4.645
年龄.027 .050 .010 .548 .584 .649 1.540
school_new -.170 .135 -.025 -1.265 .206 .588 1.702
sex_new .150 .121 .023 1.239 .216 .680 1.471
address_new -.119 .127 -.017 -.937 .349 .712 1.405
famsize_new .038 .118 .005 .320 .749 .830 1.205
pstatus_new .004 .169 .000 .025 .980 .786 1.272
schoolsup_new .197 .178 .019 1.105 .269 .811 1.234
famsup_new -.070 .110 -.011 -.632 .528 .836 1.197
paid_new .147 .222 .011 .659 .510 .865 1.156
activities_new -.009 .108 -.001 -.087 .931 .830 1.204
nursery_new .070 .132 .009 .531 .596 .879 1.137
higher_new -.124 .189 -.012 -.655 .513 .712 1.404
internet_new -.115 .134 -.015 -.858 .391 .755 1.324
romantic_new .022 .112 .003 .200 .842 .832 1.202
M_prim_edu -.046 .556 -.006 -.083 .934 .046 21.942
M_5th_TO_9th -.114 .560 -.016 -.203 .839 .038 26.474
M_secon_edu -.143 .566 -.018 -.253 .801 .045 22.328
M_higher_edu -.309 .583 -.042 -.529 .597 .036 27.719
F_prim_edu -.454 .518 -.062 -.875 .382 .046 21.795
F_5th_TO_9th -.318 .522 -.046 -.608 .543 .041 24.624
F_secon_edu -.300 .532 -.037 -.563 .574 .053 18.873
F_higher_edu -.269 .547 -.033 -.492 .623 .051 19.613
M_health_job -.195 .253 -.025 -.770 .441 .229 4.373
M_other_job .050 .256 .004 .197 .844 .541 1.849
M_services_job -.273 .225 -.041 -1.211 .226 .199 5.016
M_teacher_job -.013 .226 -.002 -.055 .956 .286 3.496
F_health_job .470 .335 .036 1.400 .162 .355 2.814
F_other_job .003 .362 .000 .008 .993 .539 1.854
F_services_job .151 .269 .023 .563 .574 .136 7.336
F_teacher_job .015 .275 .002 .054 .957 .159 6.293
reason_school_repu .239 .194 .031 1.235 .217 .364 2.746
reason_course_pref .176 .202 .023 .873 .383 .347 2.886
reason_other .364 .175 .056 2.074 .039 .320 3.129
guard_mother -.030 .129 -.004 -.234 .815 .699 1.431
guard_other .311 .259 .023 1.204 .229 .612 1.635
tra_time_15_TO_30min .043 .120 .006 .356 .722 .764 1.309
tra_time_30_TO_60min .274 .206 .023 1.327 .185 .745 1.342
tra_time_GT_60min .791 .351 .038 2.254 .025 .816 1.225
study_2_TO_5hrs_time .171 .129 .026 1.325 .186 .584 1.713
study_5_TO_10hrs_time .151 .177 .017 .853 .394 .605 1.654
study_GT_10hrs_time .073 .253 .005 .290 .772 .743 1.347
failure_1_time -.532 .189 -.051 -2.814 .005 .704 1.421
failure_2_time -.691 .362 -.033 -1.906 .057 .766 1.305
failure_3_time -.428 .375 -.019 -1.140 .255 .813 1.230
family_rela_bad -.002 .381 .000 -.004 .997 .391 2.558
family_rela_avg .012 .322 .001 .038 .970 .177 5.642
family_rela_good .011 .303 .002 .037 .971 .106 9.470
family_rela_excel -.101 .308 -.014 -.329 .743 .127 7.885
freetime_low .105 .236 .012 .447 .655 .315 3.172
freetime_avg -.038 .217 -.006 -.174 .862 .217 4.600
freetime_high -.026 .231 -.004 -1111 .911 .228 4.384
freetime_very_high -.153 .266 -.014 -.572 .567 .363 2.753
go_out_low .095 .223 .012 .424 .672 .280 3.576
go_out_avg .135 .218 .019 .619 .536 .236 4.244
go_out_high .186 .232 .024 .801 .423 .264 3.781
go_out_very_high -.132 .246 -.015 -.537 .591 .284 3.521
Dalc_low -.157 .156 -.019 -1.006 .315 .655 1.527
Dalc_avg .274 .250 .021 1.097 .273 .628 1.592
Dalc_high -.877 .352 -.043 -2.488 .013 .763 1.310
Dalc_very_high .102 .407 .005 .250 .802 .571 1.751
Walc_low .031 .144 .004 .213 .831 .656 1.526
Walc_avg -.148 .164 -.018 -.901 .368 .594 1.683
Walc_high .000 .205 .000 .002 .998 .495 2.020
Walc_very_high -.059 .309 -.005 -.190 .849 .393 2.542
health_low -.065 .205 -.006 -.314 .754 .542 1.845
health_avg -.125 .185 -.015 -.677 .499 .459 2.179
health_high -.088 .190 -.010 -.465 .642 .482 2.075
health_very_high -.234 .169 -.035 -1.381 .168 .357 2.801
一个。因变量：G3