# Chapter 3 Recap: Univariate analyses

We have learned a multitude of analyses that allowed us to interpret ecological data while depicting the effects of *one or multiple* variables in *one* response variable.

We can recall the:

General Linear Models, from which we used the functions:

`lm()`

;`anova()`

;`t.test()`

;`lmer()`

.Generalized Linear Models, where we learned how to apply using:

`glm()`

and`glmer()`

with several`family()`

link functions.Generalized Additive Models, with the:

`gam()`

function.

These models allowed us to ask questions such as:

*What are the effects of precipitation and temperature on species richness?**How does the abundance of microbes change between hosts?**Do co-occurring fish become more aggressive after being induced to fear?*

However, one may be interested in making inferences from ecological data containing *more than one* outcome or dependent variable.

This interest may be driven by hypothesis testing and modelling, but also be entirely exploratory.