If you are like I was for a long time, you have avoided learning R. You’ve probably heard that there’s a steep learning curve, and that the available documentation is not necessarily user-friendly. Frankly, both things are true, to some extent. The best and worst thing about R is that it is open-source and there is no single company that is responsible for R or your ability to use it. While there is a developer community that maintains a set of standards and regulated documentation, anyone can add new functionality to R through user-created “packages.” This gives R users a large, flexible range of options (once you know how to install the packages, of course!), which can be a major advantage. On the other hand, these packages are as diverse as the users who create them, and they may emphasize different model features, output displays, and even basic methodological principles. Underlying all of this, though, is what I feel is the truly intimidating part of R:

In this video I will answer a question from a recent webinar Random Intercept and Random Slope Models. We are answering questions here because we had over 500 people live on the webinar so we didn't have time to get through all the questions.

In this video I will answer a question from a recent webinar Random Intercept and Random Slope Models. We are answering questions here because we had over 500 people live on the webinar so we didn't have time to get through all the questions.

Quantiles (the median, 25th percentile, etc.) are valuable statistical descriptors, but their usefulness doesn’t stop there. In regression analysis, quantiles can also help answer a broader set of research questions than standard linear regression. In standard linear regression, the focus is on predicting the mean of a response (or dependent) variable, given a set of predictor variables. For example, standard linear regression can help us understand how age predicts the mean income of a study population. Contrast this with quantile regression, which allows us to go beyond the mean of the response variable. Now we can understand how predictor variables predict the entire distribution of the response variable, or one or more relevant features (e.g., center, spread, shape) of this distribution.

In this video I will answer a question from a recent webinar Random Intercept and Random Slope Models. We are answering questions here because we had over 500 people live on the webinar so we didn't have time to get through all the questions.

In this video I will answer a question from a recent webinar Random Intercept and Random Slope Models. We are answering questions here because we had over 500 people live on the webinar so we didn't have time to get through all the questions.

In this video I will answer a question from a recent webinar Random Intercept and Random Slope Models. We are answering questions here because we had over 500 people live on the webinar so we didn't have time to get through all the questions.

In this video I will answer a question from a recent webinar Random Intercept and Random Slope Models. We are answering questions here because we had over 500 people live on the webinar so we didn't have time to get through all the questions.

In this video I will answer a question from a recent webinar Random Intercept and Random Slope Models. We are answering questions here because we had over 500 people live on the webinar so we didn't have time to get through all the questions.

If your statistical training was typical, it centered on the normal distribution and models that assume the data are normal. And while the normal distribution is incredibly helpful in many applications, it’s easy to forget that it isn’t so “normal” when dealing with actual data. So… what do you do when the normal distribution just doesn’t work?