Sentence examples for regression to zero from inspiring English sources

Extrapolated force, calculated by linear regression to zero immersion (to eliminate buoyancy force), was used as characteristic parameter here instead of contact angle because surface tension of protein solution is modified from one cycle to another and is no more constant during the overall series of cycle's acquisition.

An F-test for reduction in model error after moving from one segment (standard linear regression) to two segments was significant at p < 0.001 for both water districts, and overall model fit (R) increased by 4% in Little Hocking and 3% in Lubeck after inclusion of the spline.

In this paper they compared three cluster-level methods (un-weighted linear regression, weighted linear regression and random-effects meta-regression) to six individual level analysis methods (standard logistic regression, robust standard errors approach, GEE, random effects meta-analytic approach, random-effects logistic regression and Bayesian random-effects regression).

These include a binary logit regression to identify zero outcomes associated with count data and NBR to model the count process.

To avoid large variance, which often arises from ordinary least square regression, the LASSO sets some regression coefficients to zero and shrinks others based on a preset regularization parameter, the so-called penalty.

If, for example, the dummy variable for cohort born in 1976 to 1980 is omitted, this is equivalent to constraining its regression coefficient to zero [2], [8].

The LASSO method combines shrinkage and model selection by automatically setting certain regression coefficients to zero [ 63].

The penalty function of LASSO performs the shrinkage of some of the regression coefficients to zero when λ is sufficiently large.

Statistical significance of the association between a MS-related trait and TFI was tested by constraining the corresponding regression coefficient to zero and comparing the log-likelihoods of the constrained and unconstrained models in a likelihood ratio χ test.

Each cohort is modeled separately using hierarchical logistic regression to construct five standardized predicted-to-expected risk ratios, which are then pooled to create a single hospital metric.

We used multiple logistic regression to explore four factors that were initially perceived to have a potential bearing on the quality of surgical staging.