But did you know quantiles are also valuable in regression, where they can answer a broader set of research questions than standard linear regression?
In standard linear regression, the focus is on estimating the mean of a response variable given a set of predictor variables.
In quantile regression, we can go beyond the mean of the response variable. Instead we can understand how predictor variables predict (1) the entire distribution of the response variable or (2) one or more relevant features (e.g., center, spread, shape) of this distribution.
For example, quantile regression can help us understand not only how age predicts the mean or median income, but also how age predicts the 75th or 25th percentile of the income distribution.
Or we can see how the inter-quartile range — the width between the 75th and 25th percentile — is affected by age. Perhaps the range becomes wider as age increases, signaling that an increase in age is associated with an increase in income variability.
In this webinar, we will help you become familiar with the power and versatility of quantile regression by discussing topics such as:
- Quantiles – a brief review of their computation, interpretation and uses;
- Distinction between conditional and unconditional quantiles;
- Formulation and estimation of conditional quantile regression models;
- Interpretation of results produced by conditional quantile regression models;
- Graphical displays for visualizing the results of conditional quantile regression models;
- Inference and prediction for conditional quantile regression models;
- Software options for fitting quantile regression models.
Join us on this webinar to understand how quantile regression can be used to expand the scope of research questions you can address with your data.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Dr. Isabella Ghement is the principal of Ghement Statistical Consulting Company Ltd., an independent statistical consulting and training firm in Richmond, British Columbia, Canada.
Isabella has presented a number of R and advanced regression short courses to graduate students and researchers in academia and industry. She is a member of the Steering Committee for the American Statistical Association (ASA)’s Conference on Statistical Practice 2018 and the Program Chair of the ASA Section on Statistical Graphics for the Joint Statistical Meetings (JSM) 2018.
Isabella obtained her Ph.D. in Statistics from the University of British Columbia.
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