How to calculate R logistic regression standard error values manually?
<p>I want to know, how the standard error values calculate from the logistic regression in R manually.  </p>

<p>I took a sample data and i applied binomial logistic regression on it</p>

<pre><code>data = data.frame(x = c(1,2,3,4,5,6,7,8,9,10),y = c(0,0,0,1,1,1,0,1,1,1))
model = glm(y ~ x, data = data, family = binomial(link = "logit"))
</code></pre>

<p>And my summary of the model is as follows and i have no idea, how the standard error has been calculated</p>

<pre><code>&gt; summary(model)

Call:
glm(formula = y ~ x, family = binomial(link = "logit"), data = data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9367  -0.5656   0.2641   0.6875   1.2974  

Coefficients:
            Estimate Std. Error z value Pr(&gt;|z|)  
(Intercept)  -2.9265     2.0601  -1.421   0.1554  
x             0.6622     0.4001   1.655   0.0979 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 13.4602  on 9  degrees of freedom
Residual deviance:  8.6202  on 8  degrees of freedom
AIC: 12.62

Number of Fisher Scoring iterations: 5
</code></pre>

<p>That would be great, if some one will answer to this... thanks in advance</p>