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.

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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.

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