Variations of Q–Q Plots: The Power of Our Eyes!
By Adam Loy, Lendie Follett, Heike Hofmann. 2015
88 citations recorded by the source corpus.
Abstract In statistical modeling, we strive to specify models that resemble data collected in studies or observed from processes. Consequently, distributional specification and parameter estimation are central to parametric models. Graphical procedures, such as the quantile–quantile (Q–Q) plot, are arguably the most widely used method of distributional assessment, though critics find their interpretation to be overly subjective. Formal goodness of fit tests are available and are quite powerful, but only indicate whether there is a lack of fit, not why there is lack of fit. In this article, we explore the use of the lineup protocol to inject rigor into graphical distributional assessment and compare its power to that of formal distributional tests. We find that lineup tests are considerably more powerful than traditional tests of normality. A further investigation into the design of Q–Q plots shows that de-trended Q–Q plots are more powerful than the standard approach as long as the plot preserves distances in x and y to be the same. While we focus on diagnosing nonnormality, our approach is general and can be directly extended to the assessment of other distributions.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/04b7543c972721599f536585d78879412a48a07d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
Citations in the corpus
- Visual Inference for the Funnel Plot in Meta-Analysis (2019, methodology): “The magnitude of this effect is unknown, but at least questions the use of online recruitment systems like Amazon’s Mechanical Turk, which has been regularly used in visual inference experiments in the past (e.g., Loy et al., 2016; Majumder et al., 2013).”