Linear Mixed Effects Modeling

Description

We, more often than not, work on experiments involving repeated measures. Examples include responses measured from cellular assays where the repeated measures of these responses may occur over multiple experimental batches, multiple plates, multiple wells within a plate etc. Data derived from behavior of mice assayed over consecutive trials would have involve repeated measures. Standard statistical tests like t-tests, ANOVA are not applicable in this scenario. Instead it requires the use of more sophisticated approaches. Linear Mixed Effect Modeling refer to one such approach.

In this course, we will go over the motivation for the use of these models, the underlying description and assumptions behind them, example scenarios where they can be used and code going over the implementation and interpretation of these models in R.