我接下来准备进行显著性分析,但是数据显著的好少。。想请教大神们,我接下来该如何处理?
Ordered logistic regression Number of obs = 505
LR chi2(47) = 90.10
Prob > chi2 = 0.0002
Log likelihood = -57.396594 Pseudo R2 = 0.4397
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willingness | Coef. Std. Err. z P>|z| [95% Conf. Interval]
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gender | .5958861 .696034 0.86 0.392 -.7683155 1.960088
age | -.1040043 .0328105 -3.17 0.002 -.1683117 -.0396969
junior | 12.88047 3301.628 0.00 0.997 -6458.192 6483.953
senior | 11.41895 3301.629 0.00 0.997 -6459.654 6482.492
university | 11.02672 3301.629 0.00 0.997 -6460.046 6482.1
noreligion | -1.491643 8.446589 -0.18 0.860 -18.04665 15.06337
buddhism | .3006055 8.420164 0.04 0.972 -16.20261 16.80382
otherrelig~n | -.4642088 8.473095 -0.05 0.956 -17.07117 16.14275
businessman | .579737 .2119733 2.73 0.006 .1642769 .9951971
teacher_do~k | 2.280551 1.354211 1.68 0.092 -.373655 4.934756
free_profe~n | .3283207 .8679861 0.38 0.705 -1.372901 2.029542
otherwork | 1.439926 .9145676 1.57 0.115 -.3525939 3.232445
decision | 1.075329 .8415668 1.28 0.201 -.5741113 2.72477
familynumb~s | -.1236159 .2633305 -0.47 0.639 -.6397341 .3925023
child | .9622938 .7545695 1.28 0.202 -.5166353 2.441223
income1 | .0507315 71.36812 0.00 0.999 -139.8282 139.9297
income2 | .1738886 71.37057 0.00 0.998 -139.7099 140.0576
income3 | -1.117104 71.3775 -0.02 0.988 -141.0144 138.7802
frequency | .028704 .4532851 0.06 0.950 -.8597184 .9171264
consume | -.0013918 .0018245 -0.76 0.446 -.0049678 .0021842
heard | -.989246 .6739309 -1.47 0.142 -2.310126 .3316343
bought | .3733213 .8258584 0.45 0.651 -1.245331 1.991974
office | 1.307363 .4467973 2.93 0.003 .4316567 2.18307
pack | -.2952653 .4134639 -0.71 0.475 -1.10564 .5151091
harm | -.5036708 .2336732 -2.16 0.031 -.9616619 -.0456798
commonmutt~m | .6294959 .3366884 1.87 0.062 -.0304012 1.289393
drug_remain | -.115847 .3315062 -0.35 0.727 -.7655872 .5338932
risk | -.1312077 .148495 -0.88 0.377 -.4222526 .1598371
brand | .0247406 .2901837 0.09 0.932 -.5440089 .5934901
area | -2.545137 .8882732 -2.87 0.004 -4.286121 -.804154
quality | -.7380446 .7481022 -0.99 0.324 -2.204298 .7282088
certificate | -.2985141 .7295237 -0.41 0.682 -1.728354 1.131326
nutrition | -.3212375 .6802458 -0.47 0.637 -1.654495 1.01202
price | .5125021 .9044316 0.57 0.571 -1.260151 2.285155
transgenosis | -.6897848 .9547334 -0.72 0.470 -2.561028 1.181458
protection | -1.274193 1.159663 -1.10 0.272 -3.547091 .9987057
price1 | -.063381 .0229581 -2.76 0.006 -.1083781 -.0183838
willingness1 | -2.793654 1.289322 -2.17 0.030 -5.320679 -.2666284
price2 | .0108072 .0231849 0.47 0.641 -.0346344 .0562487
willingness2 | -.5875318 .8252558 -0.71 0.477 -2.205004 1.02994
finalprice | .0136633 .0303833 0.45 0.653 -.0458868 .0732135
pinyinlable | -.4336775 .6109077 -0.71 0.478 -1.631034 .7636795
govlable | -1.239354 1.013189 -1.22 0.221 -3.225167 .7464593
chaoshi | .6251946 .9432762 0.66 0.507 -1.223593 2.473982
caishichang | -1.292808 .7940842 -1.63 0.104 -2.849184 .2635687
zhuanyingd~n | .2506933 .8856108 0.28 0.777 -1.485072 1.986459
qita | -13.26424 1833.543 -0.01 0.994 -3606.943 3580.415
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/cut1 | 5.563715 3302.413 -6467.048 6478.175
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