 Solution

The command fits the simple linear regressionusing ordinary least squares. The fitted conditional mean is a straight line in time. The regression residuals are mainly positive at the beginning and end and negative in the middle, so the mean function needs curvature.

A direct improvement is polynomial regression of degree two:The nested-model F-test tests $H_0:{\beta }_2=0$ against a nonzero quadratic coefficient. If $N$ measurements were used, its statistic isA small reported $p$-value supports the quadratic term. The new regression residuals should then be checked for remaining systematic structure, changing variance and temporal dependence, because the F-test assumes independent errors of common variance. Radioactive decay also motivates an exponential mean, but the displayed curvature already justifies this simple nested comparison.