  Visual validation of regression models in scatterplots is a common practice
for assessing model quality, yet its efficacy remains unquantified. We
conducted two empirical experiments to investigate individuals' ability to
visually validate linear regression models (linear trends) and to examine the
impact of common visualization designs on validation quality. The first
experiment showed that the level of accuracy for visual estimation of slope
(i.e., fitting a line to data) is higher than for visual validation of slope
(i.e., accepting a shown line). Notably, we found bias toward slopes that are
"too steep" in both cases. This lead to novel insights that participants
naturally assessed regression with orthogonal distances between the points and
the line (i.e., ODR regression) rather than the common vertical distances (OLS
regression). In the second experiment, we investigated whether incorporating
common designs for regression visualization (error lines, bounding boxes, and
confidence intervals) would improve visual validation. Even though error lines
reduced validation bias, results failed to show the desired improvements in
accuracy for any design. Overall, our findings suggest caution in using visual
model validation for linear trends in scatterplots.
