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Given no departure from linearity, we used linear relations.
In her early work, Ms. Logemann, who was born in 1942, used linear, abstract imagery.
We used linear SVMs34 to train multivariate pattern classifiers for pain and rejection.
No matter what the medium, two basic types of programming are used: linear, or straight-line programming, and branching programming.
We next used linear regression to assess whether the amount of time in each sleep stage was related to change in performance for LF shared features.
We used linear mixed-effects models with a robust/sandwich estimator for the variance (Model 1) to account for within-subject correlation in the outcome measures.
We created CRHS maps for each year and used linear regression across time to identify locations of significant change in CRHS.
We used linear regression models to identify major determinants of length of stay.
We used linear mixed effects regression models to evaluate differences in brain structure between patient groups.
We used linear mixed-models to assess baseline to week 24 longitudinal changes.
Then we used linear SVM to classify image of species.
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