Sentence examples for multiple regression revealed from inspiring English sources

Stepwise multiple regression revealed the model: Yield=1.73 7.8*NH3 N+0.03*Pond Depth (r2=0.63).

A stepwise multiple regression revealed that canopy structure explained 36%and41%1% of the variation in WI and NDWI, respectively.

Step-wise nonlinear multiple regression revealed that structure model index (SMI) and volume fraction (BV/TV) were significant predictors of these reductions in strength (R2 = 0.83, p < 0.03).

Correlation tests (Bravais-Pearson) and Principal Component Analysis with multiple regression revealed no statistically significant relationship between the lymphocyte SI and the contaminants studied.

Multiple regression revealed that mental-rotation performance was predicted by perceptual speed and stimulus type (female or male stereotyped) but not by the perceived stereotyped nature or the perceived familiarity of the stimuli.

The hierarchical multiple regression revealed that the pre-service teachers with higher positive attitudes toward computers and greater ability for lesson planning showed higher increase in their levels of SETI.

Hierarchical multiple regressions revealed that appearance investment, as hypothesized, was a prospective predictor of increases in leanness orientation, media-ideal internalization, and muscularity behaviors.

Multiple regressions revealed that the apparently lower knowledge score for women was the result of the confounding effect of education level.

Multiple logistic regression revealed a statistically significant correlation between ADC infarct volume and outcome status (p < 0.05), but none for Canadian Neurological Scale score, age and gender (p >0.05 each).

Multiple logistic regression revealed a relationship between SLN status and tumor size (p = 0.0012) and age (p = 0.013).

Multiple logistic regression revealed that at the individual level, in both sites, the risk increased with increased child age.