Sentence examples for errors in the predictor from inspiring English sources

The first expression is derived under the assumption that the errors in the predictor variables are homoscedastic, i.e., of constant variance.

Such measurement errors in the predictor variables will tend to reduce a true association between e.g. an eye pathology and the outcome, i.e. visual acuity, but it does not invalidate the associations that we actually find.

Because slope is potentially biased downward in linear regression due to estimate error in the predictor variable, we have applied a bias correction using an estimate of the reliability ratio ([ 37], chapter 1) as described by Holeski et al.[ 38]; essentially each slope is multiplied by the appropriate heritability (reliability ratio).

In effect, the technique allows for errors on the predictor, as well as on the predicted variables.

Turning to the mean square error of the predictor in Eq. (6) we need to acknowledge that uncertainty is introduced both by the estimation of the model parameters and by the random residual terms linked to each population element.

However, the screening of variables using significance testing runs the risk of increased type I errors of the predictors in the multivariable model [ 20], and should instead be based on evaluation of background knowledge [ 6].

Although flexible in its application, the RMSE is calculated with the assumption that error variance is homoscedastic across changes in the predictor variable.

The state predictor is designed to track the plant states smoothly by incorporating proportional and integral error terms in the state predictor model.

A positive coefficient indicates that increases in the predictor variable are on average related to increases in CASI errors.

The relative importance (RI) of a predictor in a Random Forest model is obtained by the out-of-bag (OOB) error estimation, which is the increase of mean squared error (MSE) when the predictor values are permuted.

This procedure does not require assumptions of normality or homoskedasticity of errors in predictor variables (Alexopoulos, 2010).