I am currently running an analysis to quantify temporal linear trends across repeated measurements acquired during a physical ergonomics experiment (i.e., evaluating posture metrics collected over several cycles during a physical movement task). For this particular analysis, I am interested in describing the linear temporal trend by simultaneously combining the linear slope with the fit (for example, R squared) into a single metric (computed using linear regression and a weighted least squares algorithm).
I have discussed this matter with my research advisor, who suggested to compute the ratio of standard error of the slope over the slope estimate. I have also considered computing the ratio of the slope estimate over the range of the lower and upper bounds of the 95% confidence interval of the slope estimate. However, we are not certain on an appropriate strategy that is statistically valid.
Does anyone have any suggestions on a method to simultaneously combine the linear slope and error (i.e., model fit) outputs computed using linear regression? Thank you for your time and help!