R的回归结果中有NA

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Call:
glm(formula = Y ~ project + duty_time + age + edu + complete +
    dist + pro_c, family = binomial, data = city)
Deviance Residuals:
    Min       1Q   Median       3Q      Max  
-2.5340   0.0689   0.1900   0.4103   1.5710  
Coefficients: (1 not defined because of singularities)
            Estimate Std. Error z value Pr(>|z|)   
(Intercept) 34.64496    7.50492   4.616 3.91e-06 ***
project     -0.41010    0.54316  -0.755   0.4502   
duty_time    0.08147    0.09253   0.880   0.3787   
age         -0.51124    0.10750  -4.756 1.98e-06 ***
edu          0.60957    0.35576   1.713   0.0866 .  
complete     0.57447    1.30409   0.441   0.6596   
dist         0.26857    0.56732   0.473   0.6359   
pro_c             NA         NA      NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
    Null deviance: 211.28  on 359  degrees of freedom
Residual deviance: 153.51  on 353  degrees of freedom
  (2 observations deleted due to missingness)
AIC: 167.51
Number of Fisher Scoring iterations: 7

我对数据进行progit回归，为什么会出现NA？该如何修改？

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关键词：回归结果 observations coefficients coefficient observation because family Error

刚在google上搜到是这样解释的：
NA as a coefficient in a regression indicates that the variable in question is linearly related to the other variables. In your case, this means that Q3=a×Q1+b×Q2+c  for some a,b,c . If this is the case, then there's no unique solution to the regression without dropping one of the variables. Adding Q4  is only going to make matters worse.
大家遇到类似问题可以参考下

在famafrench三因素模型的回归中系数都显示NA 这是因为这三因素共线性严重？