# GLM interpretation

I have to use a GLM to interpret some data. Could anyone tell me whether this is correct just from looking at the output?

And if it is - is it significant?

This is the code I used:

genderglm <- glm(glasses ~ gender + books,
data=worksheet, family=binomial)
summary(genderglm)


And this is the output:

> summary(genderglm)

Call:
glm(formula = glasses ~ gender + books,
family = "binomial",
data = worksheet)

Deviance Residuals:
Min       1Q   Median       3Q      Max
-1.4756  -1.2508  -0.1428   1.0032   1.8038

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)    0.6784     0.2954   2.296  0.02167 *
genderMale    -1.3254     0.4736  -2.799  0.00513 **
genderOther  -16.2444  1455.3976  -0.011  0.99109
books         -0.2537     0.1381  -1.837  0.06620 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 163.55  on 117  degrees of freedom
Residual deviance: 149.14  on 114  degrees of freedom
AIC: 157.14

Number of Fisher Scoring iterations: 14

• What is your research question ? Is glasses a binary variable ? What is books ? And how many of each gender are in the data ? Commented Nov 14, 2020 at 12:04
• Whether gender and number of books read have an effect on glasses-wearing. Yes, glasses only have 'Y' and 'N'. 30 males, 87 females, 1 other. Commented Nov 14, 2020 at 12:30

There is no point in interpreting the output for genderOther.