Sentence examples for impact regression from inspiring English sources
The statistical analysis indicates that among the variables that have a statistically significant effect on lignin removal from corn stover (three first-order effects, three second-order effects, and one interaction effect), NaOH loading had the most significant impact (regression coefficient β1 = 8.73), indicating the highest sensitivity of lignin content to alkali charge.
During a ramp contraction, motor unit initial discharge rate is mildly correlated within an individual (ICC: 0.11), though accounting for this effect significantly impacts regression analysis (p = 0.01).
In order to analyze the impacts, regression testing technique as proposed in [8] was slightly modified into two versions.
For example, 'troubled with reading' item loaded on both the Daily Life Impact' factor (regression coefficient of 0.54), and the Symptoms factor (regression coefficient of 0.47).
Simulation studies show that sample-size calculations accounting for the impact of regression to the mean with the bootstrap procedure are more appropriate than is the conventional method.
Since there is an impact of regression techniques on the improved effect of incorporating qualitative soil structure information, selecting a proper technique will help to maximize the combined influence of flexible regression algorithms and soil structure information on PTF accuracy.
The other covariates did not appreciably impact the regression models.
For the first time, we report a meta-analysis of published studies and the potential impact of regression dilution bias.
Current intensity and cumulative dose of smoking were both inversely associated with methylation intensities, and controlling for potential confounders had very little impact on regression coefficients.
Further, we have implicitly assumed that the only bias present is additive (the present analysis does not consider multiplicative bias), and this should not impact the regression slope.
The impact of regression assumptions on the predicted values is greater in the case of extrapolation (outside the range of values in the data set) than in the case of interpolation (within the range of values in the data set).
