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?

Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation. This includes favorites like: All Generalized Linear Models, including logistic, probit, Poisson, beta, negative binomial regression Linear Mixed Models Generalized Linear Mixed Models Parametric Survival Analysis models, like Weibull models Structural Equation Models That's a lot of models. If you've ever learned any of these, you've heard that some of the statistics that compare model fit in competing models require that models be nested. This is particularly important while you're trying to do model building.

Residuals can be a very broad topic - one that most everyone has heard of, but few people truly understand. It’s time to change that. By definition, a “residual” is “the quantity remaining after other things have been subtracted or allowed for.” In statistics, we use the term in a similar fashion. Residuals come in various forms: Standardized Studentized Pearson Deviance But which ones do we use… and why?