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.
- How does age predict the 75th or 25th percentile of the income distribution?
- How is the inter-quartile range — the width between the 25th and 75th percentiles — affected by age? (If the range becomes wider as age increases — thereby signaling that an increase in age is associated with an increase in income variability.)
If you’d like to become familiar with the power and versatility of quantile regression, join us for September’s webinar for Statistically Speaking members.
Guest instructor Isabella R. Ghement, Ph.D., will discuss 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
- And more!
Join us on this webinar to understand how you can use quantile regression to expand the scope of research questions you can address with your data.
Date and Time
Wednesday, September 20, 2017
3 pm – 4:30 pm (US EDT)
(In a different time zone?)
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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