# How do I interpret the output of glm in R?

I'm very new to R and get some trouble to interpret. Here is the code

Crabs <- read.table("http://www.stat.ufl.edu/~aa/cat/data/Crabs.dat",
fit <- glm(y ~ width + factor(color), family = binomial, data = Crabs)
summary(fit)


And the output is

Call:
glm(formula = y ~ width + factor(color), family = binomial, data = Crabs)

Deviance Residuals:
Min       1Q   Median       3Q      Max
-2.1124  -0.9848   0.5243   0.8513   2.1413

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)    -11.38519    2.87346  -3.962 7.43e-05 ***
width            0.46796    0.10554   4.434 9.26e-06 ***
factor(color)2   0.07242    0.73989   0.098    0.922
factor(color)3  -0.22380    0.77708  -0.288    0.773
factor(color)4  -1.32992    0.85252  -1.560    0.119
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 225.76  on 172  degrees of freedom
Residual deviance: 187.46  on 168  degrees of freedom
AIC: 197.46

Number of Fisher Scoring iterations: 4


Q1: In sector 'Coefficients:' there is no 'factor(color)1' but others. Why?

Q2: What's the difference between 'Null deviance' and 'Residual deviance'

Q3: What does 'AIC' mean?

• AIC is short for Akaike information criterion (Google is your friend). Null/residual deviance are explained in many different places (again, Google is your friend). Color 1 is the reference category, the estimates for color 2/3/4 are in relation to this category. – Edgar Jun 1 at 12:46
• – gung - Reinstate Monica Jun 1 at 15:25
• Absolutely no offense to the poster but this is what happens when you don't have a degree in Mathematics and trying to mess around with the global "machine learning" and "data science" trend. – Rrjrjtlokrthjji Jun 2 at 12:49