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There are four different types of linearized Langmuir isotherm models; we used a simple linear model in this study.
We used a simple linear regression of estimated and known probability distributions and area under receiver operating characteristic curves (AUC) to evaluate the performance of each method.
In order to assess the overall relationship between knowledge of biological evolution and acceptance of biological evolution, we used a simple linear regression comparing the percentage scores of all the participants on the knowledge of biological evolution measure to their percentage scores on the acceptance of evolution measure.
For analysis of combined data from the three experiments in the final figure, we used a simple linear regression model.
We used a simple linear model to infer the number of misalignments; this yielded a false positive rate of 1.0% for PhiX174.
We used a simple linear mediation function to model the non-trophic, positive effect of macrophytes on several functional groups that use the structural complexity of these habitat-forming organisms as a refuge from predation.
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We used a simple linear-regression method to analyze the relationship between HDRS scores and DBOLD LR signals in the pre- and post-treatment groups.
Fluxomers and the FIA algorithm, as opposed to prior approaches, allow us to effectively reduce the number of system variables using a simple linear transformation on x.
This is done using a simple linear model which extracts a regional wind description from the real measurements.
A very satisfactory representation of the smoothed-transient response is obtained using a simple linear first order model.
The accuracy of the finite element solution is established using a simple linear model which has a closed form solution.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com