Hi there,

I'm wondering if anyone knows what Pr(>|z|) and the z value means in the summary of a binomial glm of a model represents. Here is the print out and code: Setp is the response variable of settlement of larvae, PLD is a factor of time and habitat is where the larvae settled.

BL1 <- glm(Setp ~ PLD * Habitat, family = binomial,

data = BL)

summary(BL1)

Call:

glm(formula = Setp ~ PLD * Habitat, family = binomial, data = BL)

Deviance Residuals:

Min 1Q Median 3Q Max

-0.50751 -0.34671 -0.07356 0.16767 0.65171

##
Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) -0.4578 0.5132 -0.892 0.3723

PLD60 -1.6939 0.9660 -1.754 0.0795 .

PLD240 -1.7866 0.9923 -1.800 0.0718 .

HabitatDark Smooth -0.3895 0.7490 -0.520 0.6031

HabitatLight Rough -1.9401 1.0400 -1.866 0.0621 .

HabitatLight Smooth -2.4022 1.2178 -1.973 0.0486 *

PLD60:HabitatDark Smooth 0.5567 1.3487 0.413 0.6798

PLD240:HabitatDark Smooth -0.3106 1.6118 -0.193 0.8472

PLD60:HabitatLight Rough 1.7473 1.5919 1.098 0.2724

PLD240:HabitatLight Rough 1.4030 1.7148 0.818 0.4133

PLD60:HabitatLight Smooth 2.3095 1.6953 1.362 0.1731

PLD240:HabitatLight Smooth 2.0075 1.7913 1.121 0.2624

Signif. codes: 0 ‘* ’ 0.001 ‘’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

```
Null deviance: 33.011 on 191 degrees of freedom
```

Residual deviance: 18.402 on 180 degrees of freedom

AIC: 78.458

Number of Fisher Scoring iterations: 5

I'm trying to determine if there are significant differences because the boxplots of the raw data showed there was. Does the Pr(>|z|) need to be <0.05 to be significant?

Thanks in advance