Hello,
I was wondering what's the importance and substantive interpretation of the table of deviance residual in the context of (logistic) regression modelling.

R code example:
Code: 
mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv")
mydata$rank <- factor(mydata$rank)
model <- glm(admit ~ gre + gpa + rank, data = mydata, family = "binomial")
anova(model, test="Chisq")

anova(model, test="Chisq")
Analysis of Deviance Table

Model: binomial, link: logit

Response: admit

Terms added sequentially (first to last)


     Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
NULL                   399     499.98              
gre   1  13.9204       398     486.06 0.0001907 ***
gpa   1   5.7122       397     480.34 0.0168478 *  
rank  3  21.8265       394     458.52 7.088e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Could you please explain what information can we get from that, compared to to the table reporting coefficient estimates, odds ratios, and p-values.

Thank you
best
gm