Sentence examples for stepwise regression algorithm from inspiring English sources

The soft sensor uses an automated stepwise regression algorithm to predict quality.

A description of the stepwise regression algorithm used by the soft sensor is provided in Section 4.

As a check on this manual model-building procedure, we also employed an automated forward stepwise regression algorithm in SAS utilizing all significant univariate variables (with p < 0.05 as the inclusion/exclusion criterion).

An automated forward stepwise regression algorithm produced a model that included all variables presented in Table 5, with the addition of three covariates: traffic-weighted road length within 100 m and the total length of Class 3 variables within 200 m and 400 m.

Binary logistic regression models for the dependant variables were built in which each consecutive, conceptually grouped, block of variables were entered into a stepwise regression algorithm (SES, health and behaviour, community context, and pollution health effects) following forced entry of a priori variables.

Additionally, three of these studies did not report smoking adjusted results [ 10- 12], although smoking was included in stepwise regression algorithms.

In summary, this assessment brings to light the deficiencies in how many studies in preventive medicine and epidemiology conduct variable selection, over-relying on multiple significance testing and stepwise regression algorithms.

Last, while the generic problems associated with stepwise regression algorithms are of lesser threat to the proposed method, given its multi-sample and replication requirements, additional adjustments may be reasonable (e.g. accounting for chance effects by using different p-value cut-offs or effect size estimates).

Stepwise regression is the appropriate algorithm in this instance, as it both removes and adds predictors.

QSPPBR models were derived after variable selection by genetic algorithm followed by stepwise regression and tested by cross- and external validation.

Most of these studies are analyzed either by fitting a risk-regression model with all exposures forced in the model, or by using a preliminary-testing algorithm, such as stepwise regression, to produce a smaller model.