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For repeated measurements, we used a mixed linear regression model [19].
We used a mixed linear model to test for association between SNP variation in 35 selection-candidate genes and trait variation in teosinte [11], [12].
I used a mixed linear model ANOVA in SAS to analyze the GCRMA normalized log2 transcript level (gene expression) data from the factorial experiment to estimate the variance contributions of accession versus treatment.
We used a mixed linear model (SAS, Procedure MIXED), with transgenic type (crop, F1 hybrid or first-generation backcross) as the fixed treatment effect, phenological traits of weeds (time to first flower, flowering duration and total number of flowers) as covariates, and block and treatment×block interaction as random effects.
We used a mixed linear model, as implemented in SPSS (version 13; SPSS Inc., Chicago, IL, USA) for the analyses.
We used a mixed linear regression model to estimate the association between peak expiratory flow rate and particulate air pollutants.
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Preference and selectivity data were analyzed using a mixed linear model with the MIXED procedure [ 40] and the Kenward-Roger method for degrees of freedom.
Prior to the heritability analyses, we tested for fixed effects on the traits by using a mixed linear model (GLMM) with repeated measurements as a random effect (SAS Proc Mixed; see [28]).
The primary outcome was compared using a mixed linear model, adjusting for the period effect.
Data was analyzed using a mixed linear model panelist, order and treatment (sample) effects.
Selected variables in the margin of safety study and the reproductive studies were subjected to statistical analyses by using a mixed linear model.
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