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Figure 3 compares error ellipses for separate processing at two heights and two locations.
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RESULTS: compare error rates between conditions and examine the type and severity of errors most often made with opt-in versus opt-out defaults.
RMSE assesses the accuracy of models by comparing error at an aggregate level.
Olson and Mossman compared error back-propagation neural network method with Logit model and Ordinary Least Square methods [4].
We also compared error rates by trial to explore differences in data quality across trials.
A 3×2 ANOVA was used to compare error and reaction time data with Stimulus (Neutral, Congruent, Incongruent) as the within-subjects factor and Group (low ADHD, high ADHD) as the between subjects factor.
Error Correction Toolkit (Yang et al., 2013) is designed for comparing error correction results for second-generation sequencing data.
We used the Wilcoxon test to compare error rates across volume subclasses, and to compare error rates between temporal pole and no-temporal pole within a volume infarct subclass.
Error analysis on the night nested model was performed by comparing error in the carbon balance with polyglucose consumption for every mode.
Comparing error versus correct problems during the calculation period, Ravizza et al. (2008) reported increased activation in the left lateral prefrontal area during the calculation period when problems were correctly solved compared with when errors were made.
To compare error rates between different sequencers, rabies control plasmid was sequenced a second time at Elim Biopharm (Hayward, CA) using Illumina Genome Analyzer IIx.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com