Any particular reason you did a plot this way instead of having a cloud of points and drawing some kind of regression line or curve through? You are unnecessarily losing information by aggregating into buckets.
I disagree. I find point clouds useful, as long as they are not pure black. Kernel density plots are better, though.
But Lumifer gave you a concrete suggestion: plot a regression curve, not a bunch of buckets. Bucketing and drawing lines between points are kinds of smoothing, so you should instead use a good smoothing. Say, loess. Just use ggplot and trust its defaults. (not loess with this many points)
Any particular reason you did a plot this way instead of having a cloud of points and drawing some kind of regression line or curve through? You are unnecessarily losing information by aggregating into buckets.
True, but it is virtually impossible to see a meaningful pattern when you have thousands data points on the graph and R2<0.2.
I disagree. I find point clouds useful, as long as they are not pure black. Kernel density plots are better, though.
But Lumifer gave you a concrete suggestion: plot a regression curve, not a bunch of buckets. Bucketing and drawing lines between points are kinds of smoothing, so you should instead use a good smoothing. Say, loess. Just use ggplot and trust its defaults. (not loess with this many points)
Well, one question is if it’s “impossible to see a meaningful pattern”, should you melt-and-recast the data so that the pattern appears X-/
Another observation is that you are constrained by Excel. R can deal with such problems easily—do you have the raw dataset available somewhere?