Sentence examples for build a linear regression from inspiring English sources

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 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].

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.

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.

Locally weighted partial least squares (LW-PLS) is one of Just-in-Time (JIT) modeling methods; PLS is used to build a local linear regression model every time when output variables need to be estimated.

In order to build an accurate model, we propose a two-tier algorithm in our algorithm which builds a multiple linear regression model from a small set of simulated data.

Therefore we build a log-linear regression as following: log(Follow-up) = α+β*Baseline+σ*ε, where Follow-up is the follow-up WOMAC score and Baseline is the baseline WOMAC score.

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.