Suggestion: Make two linear models, one with and one without that possible outlier. See what difference that one may make.
Fifteen points is bit small perhaps with the dispersion I see.
Have you done a correlation on the x y variables? With and without the "outlier"?
I doubt that the point at the top right is an outlier, but I would say that it is a very high influence/leverage point that will have a big influence on the fit/correlation and the estimates of the slope and intercept. Be sure you know the difference. If I had to pick a potential outlier, it would be the point at about temp=82, chirps ~ 150. But the point here is that the appropriateness of a linear model is better determined by the shape of the QQ plot, and the location of the high leverage point.
Consider a plot where many points are down in the lower left-hand corner and a single point is in the upper right-hand corner. If you fit a regression line to data like that, it is likely to be linear with a large R squared value, all due to a single point. So, in your case, if the Cook's D value for that extreme temperature/chirp number observation is greater than an F value with 1 and 14 df and a probability of 0.5.= 0.479, then it is probably distorting the fit to be "more linear". I wish you had been provided the DFFITS value and the studentized residual value in addition to Cook's D..
That still doesn't answer the question of whether a linear model is appropriate. I would say that it is appropriate if that high point is biologically plausible, but may not be appropriate (can't tell without the data) if that point is removed from the analysis.
SteveDenham
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