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In this research, we use the regression only once when inputting the target positions of the end effectors, and then we interpolate the results instead of calculating the regression at every control step.
Population growth rates (r) were calculated by fitting linear regressions to the natural log of chlorophyll a (Ct) versus time and calculating the regression slope: lnCt = lnC0 + rt.
Colony growth rates (r, assuming exponential growth) were calculated by fitting linear regressions to the natural log of colony volume versus time (t) and calculating the regression slope: lnVt = lnV0 + rt.
This correlation is statistically significant based on calculating the regression (p = 8.8 · 10-6).
The accuracy of BPNN-based model predictions were evaluated by providing the trained network topology with a test dataset and also by calculating the regression coefficient (R) values.
This is done by calculating the regression scores against the individual SPIED entries and retaining the top ~100 correlations, see Methods for details.
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For each participant, we calculated the regression coefficients respectively for the mean response times and accuracy as a function of day.
In the experiments shown in Figs 2a and 4a, the accompanying standard "E. coli 0111 B4 Endotoxin" was used to calculate the regression model, whereas in Supplementary Figure S3a, "E. coli LPS 055 B5" (Sigma-Aldrich) was used.
Peak area and concentration were subjected to the least square linear regression equation to calculate the regression data and correlation coefficients.
The results were recorded as peak areas which together with the corresponding concentrations were then used to calculate the regression equations of each component.
Signal intensity of 102 104 feeder cells/105 stem cells were shown to be in the linear range and therefore used to calculate the regression line.
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