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The above analysis uses a general linear model to test for significant effects of foundress number and season on the fig-wasp conflict.
FEAT uses a general linear model approach.
The second step uses a general linear regression weighted on the inversed probabilities of not being censored to model health care costs in patients who have complete follow-up information during the time period.
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Time-series data were analyzed using a general linear model extended by generalized estimation equations (GEE).
Time-series data were analyzed by using a general linear model extended by generalized estimation equations (GEEs).
Statistical analyses were performed upon the time-series data from each functional run using a General Linear Model in FSL.
Data were analysed using SAS software (version 9.2) using a general linear model (GLM).
The PL treatments were performed in triplicate and data were analyzed using a general linear model.
Balance data were analyzed using a general linear model procedure with horse, period and treatment as main effects.
These calculations were then compared with the actual measurements of the excised fragments using a general linear model.
We also evaluated 9 regressors of non-interest using a General Linear Model (GLM): 6 regressors to movement correction, 1 to white matter signal, 1 to cerebrospinal fluid and 1 to global signal.
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CEO of Professional Science Editing for Scientists @ prosciediting.com