Sentence examples for building a regression model from inspiring English sources

When building a regression model for relating a response to a large number of predictors, researchers often try fitting a multiple linear regression model first.

All assumptions (homogeneity of variance, independence-normality of residuals, linearity and multicollinearity) for building a regression model will be checked before model building.

A crucial and difficult step when building a regression model is the selection of variables to be included in the final model from a set of plausible and meaningful variables [ 3].

When a researcher is interested in building a regression-type model using survey data, then this is bound to be an issue that they have to address.

In order to overcome these drawbacks and improve the accuracy of forecasting, this study develops a novel approach referred to as "forward regression on the quadratic kernel support vector machine" (QKSVM-FR) for building a quadratic regression model using forward regression to select the important variables for forecasting the global horizontal radiation in the Tibet Autonomous Region.

When a NEMO score ≥6, a GC score ≥3 and a Cs score ≤6 were analysed all together as predictors of an active phase of disease (ESSG score ≥3.0), by building a logistic regression model, only NEMO and GC variables gave a significant contribution to the model, although the odds ratio of NEMO score was much higher with respect to that of GC score (Table  4).> -wrap-foot> *95% confidence interval.

The third step was building a logistic regression model including the variables identified in the bivariate analyses.

PSM involved three steps towards building a logistic regression model to derive predictors of quitting.

Based on OLS, Fotheringham et al. [ 31] developed a method to explore spatial heterogeneity by building a local regression model.

Early BVS applications have aimed to solve the problem of variable selection encountered when building a linear regression model.

Instead of building a single regression model to predict the entire range of behaviors in one simplified relationship, ABS seeks to capture emergent phenomena from individual agents.