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Differences between Tex19.1 −/− and control littermate datasets were identified by linear modelling with limma (71), using false discovery rate-adjusted P-values to determine statistical significance.
Relationships between EC and relative height was poorly described by linear modelling with R values for each salinity type ranging from 0.18 for the NaCl + CaCl2 treatments, R = 0.2 CaCl2, R = 0.34 NaSO4, to 0.38 for the NaCl treatment.
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Calibration curve was obtained by linear model with weighted 1/x2 regression analysis.
DM sites between nRBCs and every other cell type were detected by linear modeling with nRBCs as the reference cell type and SVs included as covariates.
Representing nonlinear systems by linear models along with structured or unstructured uncertainties and applying robust control strategies could reduce the computational complexity in comparison with implementing the nonlinear model predictive controllers.
Treatment effects were assessed by ANOVA, using general linear modelling, with BMI, period, treatment and treatment order included in the model.
It is shown that one has to maintain certain resolution of the design matrix to maximize the information, obtainable by a design, about a system described by a linear model with interactions.
Catalyst deactivation was treated by a linear model with respect to surface intermediates.
In this paper we consider uniquely E-optimal and highly E-efficient designs described by a linear model with design matrices with elements in {−1,0,1}.
We next asked whether any of the hormones measured correlated with metabolic phenotypes by fitting linear models with cohort, generation, gender, and age within generation as covariates.
aEstimated mean ± SE by generalized linear model with adjustment for age, gender, weight status and community centers.
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