Sentence examples for built a linear regression from inspiring English sources

We built a linear regression model and calculated the coefficient of determination R 2 to measure the strength of association between all possible permutations of variables.

Thus, we manually built a linear regression model in SPSS (Statistical Package for Social Sciences) V.16 and SAS (Statistical Analysis System) V.9.2.

For example, Narlikar et al. built a linear regression model to identify active enhancers in heart based on 727 sequence features including 721 TF binding related genetic features [65].

We examined gene expression data in two ways: (1) we calculated mean expression using the ΔCt method (relative to Rps29), and (2) we built a linear regression model for MCV including terms for Hbb gene expression (total or Hbb-b1) expressed as the ΔCt, and Hbb s vs. d genotype.

To calculate the loss of income in families with ASD or OD children, we built a linear regression model using a sample of families with TD children to obtain the predicted household income in the ASD and OD groups based on parental socioeconomic status and demographics.

Use one single descriptor to build a linear regression model, and perform a leave-one-out (LOO) cross-validation for the model.

To address this we used the linear modelling tool in Chipster to build a linear regression model that allows us to include all the variables in the same analysis and to take the pairing structure into account.

We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%.

We built a multivariable linear regression model to determine the association between independent caregivers' and patients' characteristics and dependent variables, intensity of provided informal care and CarerQoL of the informal caregiver, adjusted for different covariates.

We have not built a multiple linear regression model to determine the added predictive power of nursing assessments to other indicators of mortality.

We built a generalized linear regression model for each gene using gene expression in lung as the outcome and gene expression in blood, age and gender as predictors.