**1) what is the difference between a GLM and a GLMM?
**

GLM: only has fixed effects

GLMM: has fixed and random effects

both have: a set distribution and associated link function, response variable not assumed to be linear or normal, response variable should be continuous, explanatory variables are categorical and can have continuous covariates

**2)What is the difference between fixed and random effects? **

Fixed: this is the treatment in your experiment, should have an effect on your response (slope changes)

Ex: soil characteristics are the fixed effect when testing the influence of soil on plant growth of different species

Random: intercept changes but should not have an effect on your response (slope does not change)

Ex: species is a random effect when testing the influence of soil characteristics on plant growth of different species

**3) Checklist of steps for running a GLM/GLMM (Box 4 Bolker)**

- specify model: define – fixed effects, random effects, covariates, response variable,
*a priori*decide on the number of factors to include in the model - choose distribution and appropriate link function
- check assumptions: homogeniety of variances, outliers, does your distribution match the assumed distribution? (goodness of fit statistics). If variances are not homogeneous or the distribution does not match then change your model, adding effects or covariates, interactions etc
- recheck assumptions of final model (see previous step)
- check overdistribution (especially for poisson distributions)
- can choose/compare models using AIC values (lower AIC = better)

**4) Common errors that are made when performing a GLM/GLMM**

Selecting the wrong distribution and/or the wrong link function

not checking the fit of the model to the distribution