Log-linear exponential-decay fit ($y=\ln (R/R_{\ast }),m=-1/\tau ,c=\ln (R_0/R_{\ast })$)

An exponential decay rate $R=R_0{e}^{-t/\tau }$ becomes a straight line in $x=t$ and $y=\ln (R/R_{\ast })$, where $R_{\ast }$ is a fixed reference rate. Constant small fractional rate errors become approximately constant vertical errors $s\simeq {\sigma }_R/R$ by propagation of uncertainty. With independent Gaussian log-rate errors, linear least squares gives $\hat{m}=S_{xy}/S_{xx}$ and $\hat{c}=\bar{y}-\hat{m}\bar{x}$.

The covariance matrix is obtained by applying the variance rules to these linear estimators:Transforming back gives $\hat{\tau }=-1/\hat{m}$, ${\sigma }_{\tau }\simeq {\sigma }_m/{\hat{m}}^2$, ${\hat{R}}_0=R_{\ast }{e}^{\hat{c}}$ and ${\sigma }_{R_0}\simeq {\hat{R}}_0{\sigma }_c$. If a candidate lifetime fixes the slope $m_a$, the intercept must be refitted as $c_a=\bar{y}-m_a\bar{x}$. The profiled objective obeysThis follows by separating the centred slope residual from the unrestricted regression residual, whose inner product with the design columns is zero by the normal equations for linear least squares. Known supplied measurement errors are not rescaled by the observed reduced chi-squared. This log-error model is an approximation for proportional errors, not an exact replacement for every raw-count likelihood.